Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review
The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals. This paper presents a systematic review of deep learning methods for ECG data from both mo...
Saved in:
| Published in | Computers in biology and medicine Vol. 122; p. 103801 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Ltd
01.07.2020
Elsevier Limited |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0010-4825 1879-0534 1879-0534 |
| DOI | 10.1016/j.compbiomed.2020.103801 |
Cover
| Abstract | The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals.
This paper presents a systematic review of deep learning methods for ECG data from both modeling and application perspectives.
We extracted papers that applied deep learning (deep neural network) models to ECG data that were published between January 1st of 2010 and February 29th of 2020 from Google Scholar, PubMed, and the Digital Bibliography & Library Project. We then analyzed each article according to three factors: tasks, models, and data. Finally, we discuss open challenges and unsolved problems in this area.
The total number of papers extracted was 191. Among these papers, 108 were published after 2019. Different deep learning architectures have been used in various ECG analytics tasks, such as disease detection/classification, annotation/localization, sleep staging, biometric human identification, and denoising.
The number of works on deep learning for ECG data has grown explosively in recent years. Such works have achieved accuracy comparable to that of traditional feature-based approaches and ensembles of multiple approaches can achieve even better results. Specifically, we found that a hybrid architecture of a convolutional neural network and recurrent neural network ensemble using expert features yields the best results. However, there are some new challenges and problems related to interpretability, scalability, and efficiency that must be addressed. Furthermore, it is also worth investigating new applications from the perspectives of datasets and methods.
This paper summarizes existing deep learning research using ECG data from multiple perspectives and highlights existing challenges and problems to identify potential future research directions.
•A systematic review of deep learning methods on Electrocardiogram data.•Including 191 papers from multiple research fields from 2010 to 2020.•Analyzing papers from perspectives of task, model and data.•Discussing 7 aspects of challenges and potential opportunities for future works. |
|---|---|
| AbstractList | The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals.
This paper presents a systematic review of deep learning methods for ECG data from both modeling and application perspectives.
We extracted papers that applied deep learning (deep neural network) models to ECG data that were published between January 1st of 2010 and February 29th of 2020 from Google Scholar, PubMed, and the Digital Bibliography & Library Project. We then analyzed each article according to three factors: tasks, models, and data. Finally, we discuss open challenges and unsolved problems in this area.
The total number of papers extracted was 191. Among these papers, 108 were published after 2019. Different deep learning architectures have been used in various ECG analytics tasks, such as disease detection/classification, annotation/localization, sleep staging, biometric human identification, and denoising.
The number of works on deep learning for ECG data has grown explosively in recent years. Such works have achieved accuracy comparable to that of traditional feature-based approaches and ensembles of multiple approaches can achieve even better results. Specifically, we found that a hybrid architecture of a convolutional neural network and recurrent neural network ensemble using expert features yields the best results. However, there are some new challenges and problems related to interpretability, scalability, and efficiency that must be addressed. Furthermore, it is also worth investigating new applications from the perspectives of datasets and methods.
This paper summarizes existing deep learning research using ECG data from multiple perspectives and highlights existing challenges and problems to identify potential future research directions.
•A systematic review of deep learning methods on Electrocardiogram data.•Including 191 papers from multiple research fields from 2010 to 2020.•Analyzing papers from perspectives of task, model and data.•Discussing 7 aspects of challenges and potential opportunities for future works. The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals. This paper presents a systematic review of deep learning methods for ECG data from both modeling and application perspectives. We extracted papers that applied deep learning (deep neural network) models to ECG data that were published between January 1st of 2010 and February 29th of 2020 from Google Scholar, PubMed, and the Digital Bibliography & Library Project. We then analyzed each article according to three factors: tasks, models, and data. Finally, we discuss open challenges and unsolved problems in this area. The total number of papers extracted was 191. Among these papers, 108 were published after 2019. Different deep learning architectures have been used in various ECG analytics tasks, such as disease detection/classification, annotation/localization, sleep staging, biometric human identification, and denoising. The number of works on deep learning for ECG data has grown explosively in recent years. Such works have achieved accuracy comparable to that of traditional feature-based approaches and ensembles of multiple approaches can achieve even better results. Specifically, we found that a hybrid architecture of a convolutional neural network and recurrent neural network ensemble using expert features yields the best results. However, there are some new challenges and problems related to interpretability, scalability, and efficiency that must be addressed. Furthermore, it is also worth investigating new applications from the perspectives of datasets and methods. This paper summarizes existing deep learning research using ECG data from multiple perspectives and highlights existing challenges and problems to identify potential future research directions. BackgroundThe electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals.ObjectiveThis paper presents a systematic review of deep learning methods for ECG data from both modeling and application perspectives.MethodsWe extracted papers that applied deep learning (deep neural network) models to ECG data that were published between January 1st of 2010 and February 29th of 2020 from Google Scholar, PubMed, and the Digital Bibliography & Library Project. We then analyzed each article according to three factors: tasks, models, and data. Finally, we discuss open challenges and unsolved problems in this area.ResultsThe total number of papers extracted was 191. Among these papers, 108 were published after 2019. Different deep learning architectures have been used in various ECG analytics tasks, such as disease detection/classification, annotation/localization, sleep staging, biometric human identification, and denoising.ConclusionThe number of works on deep learning for ECG data has grown explosively in recent years. Such works have achieved accuracy comparable to that of traditional feature-based approaches and ensembles of multiple approaches can achieve even better results. Specifically, we found that a hybrid architecture of a convolutional neural network and recurrent neural network ensemble using expert features yields the best results. However, there are some new challenges and problems related to interpretability, scalability, and efficiency that must be addressed. Furthermore, it is also worth investigating new applications from the perspectives of datasets and methods.SignificanceThis paper summarizes existing deep learning research using ECG data from multiple perspectives and highlights existing challenges and problems to identify potential future research directions. The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals.BACKGROUNDThe electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals.This paper presents a systematic review of deep learning methods for ECG data from both modeling and application perspectives.OBJECTIVEThis paper presents a systematic review of deep learning methods for ECG data from both modeling and application perspectives.We extracted papers that applied deep learning (deep neural network) models to ECG data that were published between January 1st of 2010 and February 29th of 2020 from Google Scholar, PubMed, and the Digital Bibliography & Library Project. We then analyzed each article according to three factors: tasks, models, and data. Finally, we discuss open challenges and unsolved problems in this area.METHODSWe extracted papers that applied deep learning (deep neural network) models to ECG data that were published between January 1st of 2010 and February 29th of 2020 from Google Scholar, PubMed, and the Digital Bibliography & Library Project. We then analyzed each article according to three factors: tasks, models, and data. Finally, we discuss open challenges and unsolved problems in this area.The total number of papers extracted was 191. Among these papers, 108 were published after 2019. Different deep learning architectures have been used in various ECG analytics tasks, such as disease detection/classification, annotation/localization, sleep staging, biometric human identification, and denoising.RESULTSThe total number of papers extracted was 191. Among these papers, 108 were published after 2019. Different deep learning architectures have been used in various ECG analytics tasks, such as disease detection/classification, annotation/localization, sleep staging, biometric human identification, and denoising.The number of works on deep learning for ECG data has grown explosively in recent years. Such works have achieved accuracy comparable to that of traditional feature-based approaches and ensembles of multiple approaches can achieve even better results. Specifically, we found that a hybrid architecture of a convolutional neural network and recurrent neural network ensemble using expert features yields the best results. However, there are some new challenges and problems related to interpretability, scalability, and efficiency that must be addressed. Furthermore, it is also worth investigating new applications from the perspectives of datasets and methods.CONCLUSIONThe number of works on deep learning for ECG data has grown explosively in recent years. Such works have achieved accuracy comparable to that of traditional feature-based approaches and ensembles of multiple approaches can achieve even better results. Specifically, we found that a hybrid architecture of a convolutional neural network and recurrent neural network ensemble using expert features yields the best results. However, there are some new challenges and problems related to interpretability, scalability, and efficiency that must be addressed. Furthermore, it is also worth investigating new applications from the perspectives of datasets and methods.This paper summarizes existing deep learning research using ECG data from multiple perspectives and highlights existing challenges and problems to identify potential future research directions.SIGNIFICANCEThis paper summarizes existing deep learning research using ECG data from multiple perspectives and highlights existing challenges and problems to identify potential future research directions. |
| ArticleNumber | 103801 |
| Author | Zhou, Yuxi Hong, Shenda Sun, Jimeng Shang, Junyuan Xiao, Cao |
| Author_xml | – sequence: 1 givenname: Shenda surname: Hong fullname: Hong, Shenda email: hongshenda@pku.edu.cn organization: Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, USA – sequence: 2 givenname: Yuxi surname: Zhou fullname: Zhou, Yuxi email: joy_yuxi@pku.edu.cn organization: School of Electronics Engineering and Computer Science, Peking University, Beijing, China – sequence: 3 givenname: Junyuan surname: Shang fullname: Shang, Junyuan email: sjy1203@pku.edu.cn organization: School of Electronics Engineering and Computer Science, Peking University, Beijing, China – sequence: 4 givenname: Cao surname: Xiao fullname: Xiao, Cao email: cao.xiao@iqvia.com organization: Analytics Center of Excellence, IQVIA, Cambridge, USA – sequence: 5 givenname: Jimeng surname: Sun fullname: Sun, Jimeng email: jimeng@illinois.edu organization: Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32658725$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNkU9vFCEYh4mpsdvqVzAkXrzMCsMwCx6MtfFf0qQXPRMG3tmyMjACo9lvL5ttY7KnPRHI8z7A73eFLkIMgBCmZE0J7d_t1iZO8-DiBHbdkvZwzAShz9CKio1sCGfdBVoRQknTiZZfoqucd4SQjjDyAl2ytudi0_IVgvt5jqkswRUHGetgsXnQ3kPY1m0csQWYsQedggtbPEF5iDbjMSYMHkxJ0ehkXdwmPWGri36Pb3De5wKTLs7gBH8c_H2Jno_aZ3j1uF6jn18-_7j91tzdf_1-e3PXmI7L0lgyDkZqJjQH2Ag9dK0lXDMOvRyk3BiuJYgeDAMYBzranlOtGRUjt5z2ll2jt0fvnOLvBXJRk8sGvNcB4pJV27VsI0VP-4q-OUF3cUmhvq5SVJJOSHagXj9Sy1CzVnNyk0579RRgBT4cAZNizglGZVypP4-hJO28okQdGlM79b8xdWhMHRurAnEieLrjjNFPx1GokdaYk8rGQTBgXarVKBvdOZKPJxLjXXBG-1-wP0_xD8nszbk |
| CitedBy_id | crossref_primary_10_3389_fcvm_2025_1471989 crossref_primary_10_1109_ACCESS_2022_3165966 crossref_primary_10_1109_ACCESS_2024_3378730 crossref_primary_10_1007_s00521_023_09267_5 crossref_primary_10_1007_s11042_024_18302_z crossref_primary_10_3390_s22114075 crossref_primary_10_1007_s11761_024_00436_5 crossref_primary_10_1038_s41598_023_50334_7 crossref_primary_10_1109_JBHI_2023_3281977 crossref_primary_10_1016_j_engappai_2024_109480 crossref_primary_10_1186_s12911_022_02035_w crossref_primary_10_1371_journal_pone_0317630 crossref_primary_10_1016_j_compbiomed_2024_109088 crossref_primary_10_3390_app13084964 crossref_primary_10_3390_jintelligence9020017 crossref_primary_10_1016_j_cpcardiol_2023_101744 crossref_primary_10_1088_1361_6579_ac6aa2 crossref_primary_10_1098_rsta_2020_0262 crossref_primary_10_1109_LSP_2022_3172014 crossref_primary_10_1109_ACCESS_2024_3516495 crossref_primary_10_2196_30798 crossref_primary_10_1016_j_bspc_2022_103750 crossref_primary_10_1109_ACCESS_2023_3236189 crossref_primary_10_1007_s12652_022_03868_z crossref_primary_10_1155_2022_2047576 crossref_primary_10_1109_ACCESS_2021_3095248 crossref_primary_10_1515_jisys_2023_0002 crossref_primary_10_1016_j_bspc_2022_103749 crossref_primary_10_1088_1361_6501_ad7e46 crossref_primary_10_1007_s13239_025_00777_y crossref_primary_10_1186_s13634_024_01197_1 crossref_primary_10_1109_JSEN_2022_3228517 crossref_primary_10_1038_s41598_024_71932_z crossref_primary_10_1098_rsif_2022_0317 crossref_primary_10_1155_2024_8813251 crossref_primary_10_1253_circj_CJ_22_0827 crossref_primary_10_31083_j_rcm2512463 crossref_primary_10_1002_joa3_13052 crossref_primary_10_3390_app13063474 crossref_primary_10_1088_1361_6579_ac69a8 crossref_primary_10_32604_cmc_2023_042590 crossref_primary_10_1145_3678569 crossref_primary_10_1109_ACCESS_2023_3282315 crossref_primary_10_1109_ACCESS_2024_3402359 crossref_primary_10_1109_JBHI_2020_3022989 crossref_primary_10_1155_2021_5599272 crossref_primary_10_1093_ehjdh_ztab045 crossref_primary_10_1109_ACCESS_2022_3204703 crossref_primary_10_3390_s20247307 crossref_primary_10_1093_ehjdh_ztac016 crossref_primary_10_1109_JBHI_2021_3068481 crossref_primary_10_4258_hir_2023_29_2_132 crossref_primary_10_1016_j_compbiomed_2024_107928 crossref_primary_10_3389_fphys_2022_912739 crossref_primary_10_1007_s13246_021_01072_5 crossref_primary_10_1109_ACCESS_2023_3335176 crossref_primary_10_3390_asi6050095 crossref_primary_10_3390_s21030773 crossref_primary_10_1007_s11356_021_17442_1 crossref_primary_10_1016_j_bspc_2024_107227 crossref_primary_10_1016_j_cmpb_2023_107623 crossref_primary_10_1038_s41598_022_24574_y crossref_primary_10_3390_bioengineering11030217 crossref_primary_10_1109_JBHI_2023_3310989 crossref_primary_10_1016_j_ins_2022_01_030 crossref_primary_10_1186_s12911_022_01808_7 crossref_primary_10_1016_j_ins_2021_12_083 crossref_primary_10_7856_kjcls_2024_35_3_497 crossref_primary_10_1186_s12875_024_02407_3 crossref_primary_10_1016_j_compbiomed_2022_106390 crossref_primary_10_1109_JIOT_2021_3108792 crossref_primary_10_2174_1574362417666220518120229 crossref_primary_10_1080_10255842_2025_2480264 crossref_primary_10_1053_j_jvca_2024_04_009 crossref_primary_10_1109_RBME_2023_3296938 crossref_primary_10_1016_j_bspc_2024_106388 crossref_primary_10_1016_j_bspc_2024_107236 crossref_primary_10_1016_j_bspc_2024_107478 crossref_primary_10_1016_j_bspc_2021_102674 crossref_primary_10_1016_j_compbiomed_2021_105114 crossref_primary_10_3390_s21051906 crossref_primary_10_1155_2021_2195922 crossref_primary_10_1007_s11042_023_17071_5 crossref_primary_10_1016_j_artmed_2023_102690 crossref_primary_10_1109_TBME_2023_3239527 crossref_primary_10_1097_CRD_0000000000000764 crossref_primary_10_1038_s41591_023_02396_3 crossref_primary_10_1016_j_measurement_2023_113239 crossref_primary_10_1016_j_bspc_2025_107523 crossref_primary_10_1016_j_knosys_2021_107187 crossref_primary_10_3390_s23031507 crossref_primary_10_1109_RBME_2022_3154893 crossref_primary_10_1016_j_engappai_2024_109222 crossref_primary_10_1007_s40747_024_01419_x crossref_primary_10_1063_5_0202497 crossref_primary_10_1109_ACCESS_2023_3338191 crossref_primary_10_1038_s41598_021_84374_8 crossref_primary_10_3390_app12168368 crossref_primary_10_1038_s41598_021_82520_w crossref_primary_10_1038_s44325_024_00010_0 crossref_primary_10_1007_s11760_023_02737_2 crossref_primary_10_1088_1361_6579_acaa1a crossref_primary_10_3389_frsip_2022_921973 crossref_primary_10_3390_electronics13163222 crossref_primary_10_1016_j_bspc_2022_104388 crossref_primary_10_3390_app11062758 crossref_primary_10_1016_j_hjc_2024_08_011 crossref_primary_10_3390_app14198772 crossref_primary_10_1109_JBHI_2023_3325540 crossref_primary_10_1109_JBHI_2022_3169325 crossref_primary_10_3233_IDT_240649 crossref_primary_10_1038_s41467_023_39472_8 crossref_primary_10_1016_j_dajour_2024_100489 crossref_primary_10_3390_s25051400 crossref_primary_10_1016_j_bspc_2024_107388 crossref_primary_10_1016_j_eswa_2022_118540 crossref_primary_10_1016_j_health_2024_100366 crossref_primary_10_3390_app12073332 crossref_primary_10_1016_j_patter_2023_100687 crossref_primary_10_1016_j_neucom_2023_126524 crossref_primary_10_1016_j_bspc_2021_102528 crossref_primary_10_1016_j_imed_2021_09_001 crossref_primary_10_1016_j_jelectrocard_2021_07_003 crossref_primary_10_1038_s41598_023_45184_2 crossref_primary_10_1109_TIM_2022_3232646 crossref_primary_10_1109_JBHI_2024_3411792 crossref_primary_10_1371_journal_pone_0271270 crossref_primary_10_2196_18803 crossref_primary_10_1155_2022_8413294 crossref_primary_10_3390_s21124173 crossref_primary_10_1007_s11042_023_16506_3 crossref_primary_10_3389_fphys_2023_1118360 crossref_primary_10_1016_j_ijcard_2021_05_017 crossref_primary_10_1016_j_bspc_2024_106744 crossref_primary_10_1016_j_jelectrocard_2023_11_002 crossref_primary_10_1007_s11760_024_03133_0 crossref_primary_10_1088_1361_6579_ac89cb crossref_primary_10_1109_TBME_2020_3042646 crossref_primary_10_1016_j_imu_2023_101375 crossref_primary_10_1016_j_ijin_2022_11_001 crossref_primary_10_1016_j_comcom_2021_08_002 crossref_primary_10_1109_JSEN_2022_3206225 crossref_primary_10_3390_s23031390 crossref_primary_10_1109_RBME_2024_3486439 crossref_primary_10_1007_s11936_023_01004_4 crossref_primary_10_1111_exsy_13277 crossref_primary_10_1016_j_patter_2024_101116 crossref_primary_10_3390_app12157711 crossref_primary_10_3390_bioengineering11050489 crossref_primary_10_4103_bbrj_bbrj_341_22 crossref_primary_10_1088_1361_6579_ad5cc0 crossref_primary_10_3389_fphys_2021_811661 crossref_primary_10_1109_MRL_2024_3430192 crossref_primary_10_1109_TKDE_2021_3140058 crossref_primary_10_1109_ACCESS_2024_3467181 crossref_primary_10_1016_j_patrec_2020_06_016 crossref_primary_10_1080_03091902_2022_2026508 crossref_primary_10_1016_j_bspc_2024_106160 crossref_primary_10_1007_s42979_024_03508_7 crossref_primary_10_1186_s12911_024_02764_0 crossref_primary_10_1038_s41598_022_24254_x crossref_primary_10_1016_j_bspc_2024_105997 crossref_primary_10_3389_fgene_2021_638191 crossref_primary_10_3390_jcm11237072 crossref_primary_10_1038_s41746_023_00840_9 crossref_primary_10_1186_s40635_022_00490_3 crossref_primary_10_1109_ACCESS_2022_3192390 crossref_primary_10_18632_aging_204688 crossref_primary_10_3390_bioengineering10010027 crossref_primary_10_1080_10255842_2022_2072684 crossref_primary_10_1088_2057_1976_ad40b0 crossref_primary_10_1186_s12911_021_01453_6 crossref_primary_10_3390_s24165087 crossref_primary_10_1016_j_neunet_2023_03_004 crossref_primary_10_1016_j_bspc_2022_104194 crossref_primary_10_1016_j_cmpb_2020_105847 crossref_primary_10_1109_ACCESS_2021_3095312 crossref_primary_10_1016_j_imu_2025_101624 crossref_primary_10_3389_fphys_2023_1246746 crossref_primary_10_1007_s11831_022_09720_z crossref_primary_10_3390_s23218691 crossref_primary_10_1109_ACCESS_2020_3029211 crossref_primary_10_3390_e22060595 crossref_primary_10_3390_s24247896 crossref_primary_10_1007_s44196_023_00182_0 crossref_primary_10_2147_NSS_S275252 crossref_primary_10_1109_JSEN_2022_3162691 crossref_primary_10_3390_hearts2040037 crossref_primary_10_1016_j_engappai_2022_105584 crossref_primary_10_1016_j_patcog_2024_111311 crossref_primary_10_23919_CJEE_2023_000008 crossref_primary_10_1016_j_cmpb_2023_107359 crossref_primary_10_1016_j_isci_2021_102373 crossref_primary_10_1016_j_bspc_2022_103548 crossref_primary_10_1109_OJIM_2022_3196703 crossref_primary_10_3934_mbe_2022460 crossref_primary_10_3390_s22072538 crossref_primary_10_4103_mjhs_mjhs_148_22 crossref_primary_10_35784_iapgos_6022 crossref_primary_10_3390_medicina58020210 crossref_primary_10_1109_TBME_2024_3454545 crossref_primary_10_1016_j_cmpb_2021_106312 crossref_primary_10_1002_btm2_70002 crossref_primary_10_1016_j_cmpb_2020_105740 crossref_primary_10_1016_j_bspc_2023_105265 crossref_primary_10_1109_ACCESS_2023_3325283 crossref_primary_10_1108_IJICC_11_2023_0336 crossref_primary_10_1109_TSMC_2023_3257022 crossref_primary_10_1109_ACCESS_2023_3286311 crossref_primary_10_3390_s20247246 crossref_primary_10_1088_2057_1976_adbbf5 crossref_primary_10_3390_app13053070 crossref_primary_10_1016_j_compbiomed_2024_109062 crossref_primary_10_1109_JBHI_2024_3481505 crossref_primary_10_3390_s23198328 crossref_primary_10_1016_j_bspc_2023_104963 crossref_primary_10_3390_sym17030469 crossref_primary_10_1111_exsy_13002 crossref_primary_10_1007_s42979_021_00924_x crossref_primary_10_1016_j_cvdhj_2022_07_074 crossref_primary_10_3390_bios11060188 crossref_primary_10_1016_j_bspc_2023_105922 crossref_primary_10_3934_mbe_2024189 crossref_primary_10_1007_s13755_022_00192_w crossref_primary_10_1109_JBHI_2022_3171918 crossref_primary_10_1088_1361_6579_ac826e crossref_primary_10_32604_cmc_2025_061998 crossref_primary_10_1088_1361_6579_ac6f40 crossref_primary_10_1109_ACCESS_2023_3280565 |
| Cites_doi | 10.1016/j.neucom.2019.03.083 10.1371/journal.pone.0210103 10.1088/1361-6579/aac7b7 10.1109/ACCESS.2018.2883213 10.1109/ACCESS.2020.2974712 10.1109/ACCESS.2019.2931500 10.1109/ACCESS.2019.2939947 10.1109/ACCESS.2019.2934928 10.1016/j.compbiomed.2018.12.012 10.3389/fphys.2018.01390 10.1088/1361-6579/aad7e4 10.1109/ACCESS.2019.2936516 10.1166/jmihi.2018.2442 10.1088/1361-6579/aad386 10.1038/s41598-018-33424-9 10.1038/s41598-019-56927-5 10.1016/j.compbiomed.2017.08.022 10.1038/s41598-019-57025-2 10.1109/JBHI.2019.2936583 10.1016/j.cmpb.2019.05.004 10.1109/ACCESS.2019.2946932 10.1109/ACCESS.2019.2921568 10.1016/j.jelectrocard.2019.11.046 10.1088/1361-6579/ab15a2 10.1016/j.jacc.2017.07.723 10.1109/ACCESS.2019.2927726 10.1016/j.compbiomed.2018.03.016 10.1016/j.compbiomed.2018.09.009 10.1016/j.jelectrocard.2019.08.008 10.1109/ACCESS.2019.2907076 10.1088/0967-3334/37/12/2214 10.1109/ACCESS.2019.2960116 10.1016/j.cmpb.2010.02.009 10.1109/JBHI.2018.2885139 10.1016/j.jelectrocard.2018.07.026 10.1016/j.eswa.2018.12.037 10.1016/j.camwa.2007.04.035 10.1016/j.ijcard.2005.02.007 10.1371/journal.pone.0219302 10.1016/j.jelectrocard.2007.03.008 10.1038/s41598-017-09544-z 10.1016/j.compbiomed.2018.05.013 10.1109/JBHI.2019.2911367 10.1109/ACCESS.2019.2930882 10.1109/ACCESS.2018.2886573 10.1016/j.jelectrocard.2018.08.008 10.1016/j.jelectrocard.2019.08.004 10.1109/ACCESS.2019.2939822 10.1016/j.patrec.2019.07.009 10.1371/journal.pone.0226990 10.1109/TSMC.2017.2705582 10.1016/j.eswa.2018.08.011 10.1109/JBHI.2017.2771768 10.1016/j.jelectrocard.2019.09.018 10.1109/ACCESS.2017.2707460 10.1016/j.compbiomed.2017.12.023 10.1161/circ.136.suppl_1.21042 10.1109/ACCESS.2019.2933473 10.1109/ACCESS.2019.2904095 10.1016/j.jelectrocard.2018.11.013 10.1088/1361-6579/aab297 10.1109/JBHI.2019.2942938 10.1088/1361-6579/aad5a9 10.1088/1361-6579/aae304 10.1109/TIP.2015.2475625 10.1109/ACCESS.2019.2926749 10.1016/j.neucom.2018.03.011 10.1038/s41591-018-0268-3 10.1371/journal.pone.0201059 10.1109/ACCESS.2018.2833841 10.1016/j.patrec.2018.03.028 10.1016/j.compbiomed.2017.12.007 10.1109/51.932724 10.1109/ACCESS.2019.2948857 10.1016/j.compbiomed.2018.09.027 10.1016/j.neucom.2020.01.019 10.1088/1361-6579/aad9ee 10.1109/ACCESS.2019.2921991 10.1016/j.cmpb.2019.105138 10.1109/ACCESS.2019.2910880 10.1038/s41591-018-0240-2 10.1016/j.eswa.2019.07.010 10.1016/j.cmpb.2019.105286 10.1109/ACCESS.2018.2855420 10.1109/ACCESS.2019.2912036 10.1109/ACCESS.2019.2890865 10.1007/BF02345439 10.1161/01.CIR.101.23.e215 10.1109/ACCESS.2019.2928017 10.1016/j.patrec.2019.02.016 10.1016/j.eswa.2019.05.033 10.1016/j.compbiomed.2018.07.001 10.1088/1361-6579/aaaa9d 10.1109/ACCESS.2019.2955738 10.1088/1361-6579/aad5bd 10.1016/j.neucom.2018.06.068 10.1109/ACCESS.2019.2912519 10.1109/ACCESS.2019.2920900 10.3390/s18072074 10.1109/ACCESS.2019.2950383 10.1109/ACCESS.2019.2924980 10.1109/JBHI.2019.2910082 10.1016/j.cmpb.2019.105001 10.1109/ACCESS.2018.2807700 10.1038/s41598-019-42516-z 10.1088/1361-6579/aad9ed 10.1161/01.CIR.66.1.218 10.1016/j.compbiomed.2019.04.009 10.1016/j.eswa.2018.07.030 10.1371/journal.pone.0216756 10.1109/JBHI.2019.2919732 10.1038/nature14539 10.3390/s19122828 10.1109/ACCESS.2019.2943197 10.1109/ACCESS.2019.2930770 10.1088/1361-6579/aaff04 10.1109/JBHI.2019.2957809 10.1016/j.cmpb.2016.12.005 10.1109/JBHI.2018.2871510 10.1016/j.compbiomed.2018.06.002 10.1109/ACCESS.2019.2900719 10.1088/1361-6579/ab17f0 |
| ContentType | Journal Article |
| Copyright | 2020 Elsevier Ltd Copyright © 2020 Elsevier Ltd. All rights reserved. 2020. Elsevier Ltd |
| Copyright_xml | – notice: 2020 Elsevier Ltd – notice: Copyright © 2020 Elsevier Ltd. All rights reserved. – notice: 2020. Elsevier Ltd |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7RV 7X7 7XB 88E 8AL 8AO 8FD 8FE 8FG 8FH 8FI 8FJ 8FK 8G5 ABUWG AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ GUQSH HCIFZ JQ2 K7- K9. KB0 LK8 M0N M0S M1P M2O M7P M7Z MBDVC NAPCQ P5Z P62 P64 PHGZM PHGZT PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 |
| DOI | 10.1016/j.compbiomed.2020.103801 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Nursing & Allied Health Database Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Computing Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Journals Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) Research Library ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Aerospace Collection ProQuest Central Essentials Biological Science Database ProQuest Central Technology collection Natural Science Collection ProQuest One Community College ProQuest Central Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student Research Library Prep SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database ProQuest Health & Medical Complete (Alumni) Nursing & Allied Health Database (Alumni Edition) Biological Sciences Computing Database ProQuest Health & Medical Collection Medical Database Research Library Biological Science Database (Proquest) Biochemistry Abstracts 1 Research Library (Corporate) Nursing & Allied Health Premium Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic (New) ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Research Library Prep Computer Science Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection SciTech Premium Collection ProQuest Central China ProQuest One Applied & Life Sciences Health Research Premium Collection Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Advanced Technologies & Aerospace Collection ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts Nursing & Allied Health Premium ProQuest Health & Medical Complete ProQuest One Academic UKI Edition ProQuest Nursing & Allied Health Source (Alumni) Engineering Research Database ProQuest One Academic ProQuest One Academic (New) Technology Collection Technology Research Database ProQuest One Academic Middle East (New) ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing Research Library (Alumni Edition) ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central ProQuest Health & Medical Research Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea ProQuest Research Library ProQuest Computing ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest Nursing & Allied Health Source ProQuest SciTech Collection Advanced Technologies & Aerospace Database ProQuest Medical Library Biochemistry Abstracts 1 ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE Research Library Prep MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 3 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine Engineering |
| EISSN | 1879-0534 |
| ExternalDocumentID | 32658725 10_1016_j_compbiomed_2020_103801 S0010482520301694 |
| Genre | Research Support, U.S. Gov't, Non-P.H.S Systematic Review Journal Article Research Support, N.I.H., Extramural |
| GrantInformation_xml | – fundername: NHLBI NIH HHS grantid: R56 HL138415 – fundername: NINDS NIH HHS grantid: R01 NS107291 |
| GroupedDBID | --- --K --M --Z -~X .1- .55 .DC .FO .GJ .~1 0R~ 1B1 1P~ 1RT 1~. 1~5 29F 4.4 457 4G. 53G 5GY 5VS 7-5 71M 7RV 7X7 88E 8AO 8FE 8FG 8FH 8FI 8FJ 8G5 8P~ 9JN AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AATTM AAXKI AAXUO AAYFN AAYWO ABBOA ABFNM ABJNI ABMAC ABMZM ABOCM ABUWG ABWVN ABXDB ACDAQ ACGFS ACIEU ACIUM ACIWK ACNNM ACPRK ACRLP ACRPL ACVFH ACZNC ADBBV ADCNI ADEZE ADJOM ADMUD ADNMO AEBSH AEIPS AEKER AENEX AEUPX AEVXI AFJKZ AFKRA AFPUW AFRAH AFRHN AFTJW AFXIZ AGCQF AGHFR AGQPQ AGUBO AGYEJ AHHHB AHMBA AHZHX AIALX AIEXJ AIGII AIIUN AIKHN AITUG AJRQY AJUYK AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU ANZVX AOUOD APXCP ARAPS ASPBG AVWKF AXJTR AZFZN AZQEC BBNVY BENPR BGLVJ BHPHI BKEYQ BKOJK BLXMC BNPGV BPHCQ BVXVI CCPQU CS3 DU5 DWQXO EBS EFJIC EFKBS EJD EMOBN EO8 EO9 EP2 EP3 EX3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN FYUFA G-2 G-Q GBLVA GBOLZ GNUQQ GUQSH HCIFZ HLZ HMCUK HMK HMO HVGLF HZ~ IHE J1W K6V K7- KOM LK8 LX9 M1P M29 M2O M41 M7P MO0 N9A NAPCQ O-L O9- OAUVE OZT P-8 P-9 P2P P62 PC. PHGZM PHGZT PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PUEGO Q38 R2- ROL RPZ RXW SAE SBC SCC SDF SDG SDP SEL SES SEW SPC SPCBC SSH SSV SSZ SV3 T5K TAE UAP UKHRP WOW WUQ X7M XPP Z5R ZGI ~G- 3V. AACTN AAIAV ABLVK ABYKQ AFKWA AHPSJ AJBFU AJOXV AMFUW EFLBG LCYCR M0N RIG AAYXX AFCTW AGRNS ALIPV CITATION CGR CUY CVF ECM EIF NPM 7XB 8AL 8FD 8FK FR3 JQ2 K9. M7Z MBDVC P64 PKEHL PQEST PQUKI PRINS Q9U 77I 7X8 |
| ID | FETCH-LOGICAL-c459t-d0fbc9a38a5ee78ab42d05a35e69b997c5a9e86ec3eefb1fd651aa318f5d516d3 |
| IEDL.DBID | BENPR |
| ISSN | 0010-4825 1879-0534 |
| IngestDate | Fri Sep 05 08:29:42 EDT 2025 Wed Aug 13 07:24:28 EDT 2025 Mon Jul 21 05:57:37 EDT 2025 Tue Jul 01 03:28:37 EDT 2025 Thu Apr 24 23:04:13 EDT 2025 Fri Feb 23 02:46:42 EST 2024 Tue Aug 26 16:33:58 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Deep learning Systematic review Deep neural network(s) Electrocardiogram (ECG/EKG) |
| Language | English |
| License | Copyright © 2020 Elsevier Ltd. All rights reserved. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c459t-d0fbc9a38a5ee78ab42d05a35e69b997c5a9e86ec3eefb1fd651aa318f5d516d3 |
| Notes | ObjectType-Article-2 SourceType-Scholarly Journals-1 content type line 14 ObjectType-Feature-3 ObjectType-Evidence Based Healthcare-1 ObjectType-Article-1 ObjectType-Feature-2 ObjectType-Review-3 content type line 23 ObjectType-Undefined-4 |
| PMID | 32658725 |
| PQID | 2419048936 |
| PQPubID | 1226355 |
| ParticipantIDs | proquest_miscellaneous_2423798616 proquest_journals_2419048936 pubmed_primary_32658725 crossref_citationtrail_10_1016_j_compbiomed_2020_103801 crossref_primary_10_1016_j_compbiomed_2020_103801 elsevier_sciencedirect_doi_10_1016_j_compbiomed_2020_103801 elsevier_clinicalkey_doi_10_1016_j_compbiomed_2020_103801 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | July 2020 2020-07-00 20200701 |
| PublicationDateYYYYMMDD | 2020-07-01 |
| PublicationDate_xml | – month: 07 year: 2020 text: July 2020 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: Oxford |
| PublicationTitle | Computers in biology and medicine |
| PublicationTitleAlternate | Comput Biol Med |
| PublicationYear | 2020 |
| Publisher | Elsevier Ltd Elsevier Limited |
| Publisher_xml | – name: Elsevier Ltd – name: Elsevier Limited |
| References | Habib, Karmakar, Yearwood (bib53) 2019; 7 Lee, Oh, Kim, Yoo (bib90) 2019; 24 Sellami, Hwang (bib159) 2019; 122 Golrizkhatami, Acan (bib47) 2018; 114 Hong, Zhou, Wu, Shang, Wang, Li, Xie (bib69) 2019; 40 Zhang, Xiao, Guo, Wang (bib221) 2019; 125 Yang, Gregg, Babaeizadeh (bib206) 2020 Liu, Kim (bib111) 2018 Xu, Mak, Cheung (bib203) 2019; 23 Ye, Zhu, Fu, Shen (bib208) 2019; 7 Wang, Yao (bib185) 2019 Xu, Biswal, Deshpande, Maher, Sun (bib205) 2018 Moskalenko, Zolotykh, Osipov (bib124) 2020 Oh, Ng, San Tan, Acharya (bib133) 2019; 105 Wei, Qi, Wang, Liu, Wang, Yan (bib188) 2019; 7 He, Wang, Hu, Li, Guo, Li (bib63) 2019; 7 Li, Pang, Wang, Li (bib101) 2020; 391 Andersen, Peimankar, Puthusserypady (bib4) 2019; 115 Li, Zhang, Zhao, Zhang, Liu, Zhang, Zhang, Li, Wang, Ng (bib102) 2018; 6 Mousavi, Fotoohinasab, Afghah (bib126) 2020; 15 Zhang, Zhao, Chen, Wu (bib217) 2017; 12 Andreotti, Carr, Pimentel, Mahdi, De Vos (bib5) 2017 Zhai, Tin (bib216) 2018; 6 Chu, Shen, Huang (bib29) 2019; 7 Zihlmann, Perekrestenko, Tschannen (bib231) 2017 Tison, Zhang, Delling, Deo (bib175) 2019; 12 He, Liu, Wang, Zhao, Yuan, Li, Zhang (bib62) 2019; 7 Qiu, Xiao, Shen (bib144) 2017 Chen, Chen (bib26) 2018 Hong, Fu, Zhou, Yu, Li, Wang, Cheng (bib66) 2020 Li, Feng, Wu, Yang, Bai, Yang (bib103) 2019; 7 Yildirim, Baloglu, Tan, Ciaccio, Acharya (bib210) 2019; 176 Ribeiro, Singh, Guestrin (bib150) 2018 Ballinger, Hsieh, Singh, Sohoni, Wang, Tison, Marcus, Sanchez, Maguire, Olgin (bib12) 2018 Behar, Rosenberg, Weiser-Bitoun, Shemla, Alexandrovich, Konyukhov, Yaniv (bib14) 2018; 9 Choi, Bahadori, Sun, Kulas, Schuetz, Stewart (bib28) 2016 Niu, Tang, Sun, Zhang (bib130) 2019; 24 Attia, Kapa, Lopez-Jimenez, Asirvatham, Friedman, Noseworthy (bib7) 2018; 138 Santamaria-Granados, Munoz-Organero, Ramirez-Gonzalez, Abdulhay, Arunkumar (bib156) 2018; 7 Tan, Hagiwara, Pang, Lim, Oh, Adam, San Tan, Chen, Acharya (bib171) 2018; 94 Goto, Kimura, Katsumata, Goto, Kamatani, Ichihara, Ko, Sasaki, Fukuda, Sano (bib49) 2019; 14 Zhong, Liao, Guo, Wang (bib226) 2018; 39 Doersch (bib33) 2016 Clifford, Liu, Moody, Li-wei, Silva, Li, Johnson, Mark (bib30) 2017 Parvaneh, Rubin, Rahman, Conroy, Babaeizadeh (bib137) 2018; 39 Pourbabaee, Roshtkhari, Khorasani (bib143) 2017; 48 Fotiadou, Konopczyński, Hesser, Vullings (bib40) 2020 Baloglu, Talo, Yildirim, San Tan, Acharya (bib13) 2019; 122 Deng, Dong, Socher, Li, Li, Fei-Fei (bib32) 2009 Li, Pan, Li, Jiang, Liu (bib97) 2018; 294 Xiong, Wang, Liu, Lin, Hou, Liu (bib199) 2016; 37 Yıldırım, Pławiak, Tan, Acharya (bib211) 2018; 102 Lee, Sun, Yang, Sohn, Park, Lee, Kim (bib91) 2018; 23 Attia, Kapa, Lopez-Jimenez, McKie, Ladewig, Satam, Pellikka, Enriquez-Sarano, Noseworthy, Munger (bib8) 2019; 25 Feng, Xu, Liang, Liu (bib38) 2019; 7 Camm, Malik, Bigger, Breithardt, Cerutti, Cohen, Coumel, Fallen, Kennedy, Kleiger (bib19) 1996 Lee, Kwak (bib92) 2019; 7 Faust, Shenfield, Kareem, San, Fujita, Acharya (bib37) 2018; 102 Lai, Zhang, Bu, Su, Ma (bib88) 2019; 7 Attin, Cogliati, Duan (bib10) 2017; 136 Moody (bib122) 1983 Zhou, Hong, Shang, Wu, Wang, Li, Xie (bib229) 2019 Zhang, Zhou, Zeng (bib219) 2017; 5 Han, Mao, Dally (bib56) 2016 Kiranyaz, Ince, Hamila, Gabbouj (bib82) 2015 Zhao, Zhang, Deng, Zhang (bib225) 2018; 102 Hao, Gao, Li, Zhang, Wu, Bai (bib59) 2020; 184 Attia, Friedman, Noseworthy, Lopez-Jimenez, Ladewig, Satam, Pellikka, Munger, Asirvatham, Scott (bib6) 2019; 12 Jia, Zhao, Hu, Wang, Yan, Li (bib73) 2019 Wang, Li, Li, Li, Zeng, Xie, Liu (bib180) 2019; 349 Elola, Aramendi, Irusta, Picón, Alonso, Owens, Idris (bib34) 2018 Xiong, Stiles, Zhao (bib201) 2017 Liu, Wang, Huang, Chang, Wang, He (bib112) 2020; 24 Shaker, Tantawi, Shedeed, Tolba (bib162) 2020; 8 Rubin, Parvaneh, Rahman, Conroy, Babaeizadeh (bib154) 2018; 51 Goodfellow, Pouget-Abadie, Mirza, Xu, Warde-Farley, Ozair, Courville, Bengio (bib48) 2014 Wu, Feng, Yang (bib190) 2019 Gyawali, Chen, Liu, Horacek, Sapp, Wang (bib51) 2017 Gogna, Majumdar, Ward (bib44) 2016; 64 Chang, Wu, Tseng, Chao, Ko (bib23) 2018 Sun, Zeng, He (bib169) 2019 Jiang, Zhang, Pi, Dai (bib75) 2019; 1 Camps, Rodríguez, Mincholé (bib20) 2018 Noseworthy, Attia, Brewer, Hayes, Yao, Kapa, Friedman, Lopez-Jimenez (bib131) 2020 Xiong, Nash, Cheng, Fedorov, Stiles, Zhao (bib200) 2018; 39 Smith, Walsh, Grauer, Wang, Rapin, Li, Fennell, Taboulet (bib166) 2019; 52 Parvaneh, Rubin, Babaeizadeh, Xu-Wilson (bib136) 2019; 57 Hong, Wu, Zhou, Wang, Shang, Li, Xie (bib67) 2017 Hong, Hsiao, Chung, Feng, Wu (bib65) 2019 Tison, Singh, Ohashi, Hsieh, Ballinger, Olgin, Marcus, Pletcher (bib174) 2017; 136 Picon, Irusta, Álvarez-Gila, Aramendi, Alonso-Atienza, Figuera, Ayala, Garrote, Wik, Kramer-Johansen (bib140) 2019; 14 Xiao, Xu, Pelter, Mortara, Hu (bib196) 2018 Zhang, Lin, Xiong, Du, Zhang, Liu, Hou, Liub (bib218) 2019 Dang, Sun, Zhang, Qi, Zhou, Chang (bib31) 2019 Chiang, Hsieh, Fu, Hung, Tsao, Chien (bib27) 2019; 7 Sodmann, Vollmer, Nath, Kaderali (bib167) 2018; 39 Jia, Zhao, Li, Hu, Yan, Wang, You (bib74) 2019 Hu, Ma, Liu, Hovy, Xing (bib70) 2016; vol. 1 Hong, Xiao, Ma, Li, Sun (bib68) 2019 Wang, Hou, Shao, Yan (bib182) 2019; 7 Nikolic, Bishop, Singh (bib129) 1982; 66 Strodthoff, Strodthoff (bib168) 2018 Moody, Mark (bib123) 2001; 20 Zhao, Liu, Li, Li, Wang, Lin, Li (bib223) 2019; 7 Acharya, Oh, Hagiwara, Tan, Adam, Gertych, San Tan (bib1) 2017; 89 Ghiasi, Abdollahpur, Madani, Kiani, Ghaffari (bib43) 2017 Lynn, Pan, Kim (bib114) 2019; 7 Tadesse, Zhu, Liu, Zhou, Chen, Tian, Clifton (bib170) 2019 Guglin, Thatai (bib50) 2006; 106 Vullings (bib178) 2019 Chauhan, Vig, Ahmad (bib24) 2019; 109 Raghunath, Ulloa Cerna, Jing, vanMaanen, Stough, Hartzel, Leader, Kirchner, Good, Patel (bib145) 2019; 140 Schläpfer, Wellens (bib157) 2017; 70 Li, Xu, Chen, Liu (bib95) 2019; 7 Parvaneh, Rubin (bib135) 2018 Philips (bib139) 2003 Baalman, Ramos, Lopes, Van Der Stuijt, Bleijendaal, Brouwer, Marquering, Driessen, Knops, de Groot (bib11) 2019; 140 Harada, Hayashi, Uchida (bib60) 2019; 7 Kiranyaz, Ince, Gabbouj (bib80) 2015; 63 Kiranyaz, Ince, Gabbouj (bib81) 2017; 7 Wołk (bib189) 2019 Zhou, Liu, Hooi, Cheng, Ye (bib227) 2019 Romero, Serrano (bib151) 2001 Lin, Goyal, Girshick, He, Dollár (bib108) 2017 Maidens, Slamon (bib116) 2018; 138 Schwab, Scebba, Zhang, Delai, Karlen (bib158) 2017 Xu, Li, Liu, Wu (bib202) 2019; 7 Yildirim (bib209) 2018; 96 Cao, Yao, Chen (bib21) 2019; 7 Wang, Zhang, Pan (bib179) 2019 Mincholé, Camps, Lyon, Rodríguez (bib121) 2019; 57 Xia, Xie (bib193) 2019; 7 Han, Shi (bib55) 2020; 185 Li, Zhou, Wan, Li, Mou (bib104) 2020; 58 Bousseljot, Kreiseler, Schnabel (bib15) 1995; 40 Li, Liu, Li, Shashikumar, Nemati, Shen, Clifford (bib98) 2019; 40 Shashikumar, Shah, Clifford, Nemati (bib163) 2018 Teijeiro, García, Castro, Félix (bib173) 2018; 39 Xia, Wulan, Wang, Zhang (bib192) 2018; 93 Lee, Lee, Choi, Seo, Kim (bib93) 2019; 134 Kwon, Kim, Jeon, Lee, Lee, Cho, Choi, Jeon, Kim, Kim (bib85) 2019; 14 Limam, Precioso (bib105) 2017 Oh, Ng, San Tan, Acharya (bib132) 2018; 102 Xia, Zhang, Xu, Gao, Zhang, Liu, Li (bib194) 2018; 6 Labati, Muñoz, Piuri, Sassi, Scotti (bib86) 2019; 126 Liu, Zhang, Zhang, Liao, Huang, Chang, Wang, He (bib113) 2017; 22 Rajan, Beymer, Narayan (bib146) 2018 Huang, Chen, Yao, He (bib71) 2019; 7 Yin, Zhao, Wang, Yang, Zhang (bib213) 2017; 140 Miller, Obermeyer, Mullainathan (bib120) 2019 Nguyen, Van Nguyen, Kim (bib128) 2018; 8 He, Li, Liao, Zhang, Jiang (bib61) 2019; 7 Li, Wu, Jia, Chen, Pu (bib94) 2019 Cai, Chen, Guo, Han, Shi, Ji, Wang, Zhang, Luo (bib18) 2019 Attia, Sugrue, Asirvatham, Ackerman, Kapa, Friedman, Noseworthy (bib9) 2018; 13 Lin (bib106) 2008; 55 Zhang, Li, Dai, Liu, Zhou, Wang (bib220) 2019; 7 Hammad, Liu, Wang (bib54) 2018; 7 Li, Zhang, Dai, Zhou, Wang (bib99) 2019; 7 Laguna, Mark, Goldberg, Moody (bib87) 1997 Zhu, Ye, Fu, Liu, Shen (bib230) 2019; 9 Brisk, Bond, Banks, Piadlo, Finlay, McLaughlin, McEneaney (bib16) 2019; 57 Hannun, Rajpurkar, Haghpanahi, Tison, Bourn, Turakhia, Ng (bib57) 2019; 25 Malik, Lo, Wu (bib118) 2018; 39 Lin, Li, He, Zhang, Sun (bib107) 2017 Mathews, Kambhamettu, Barner (bib119) 2018; 99 Kuznetsov, Moskalenko, Zolotykh (bib84) 2020 Porumb, Stranges, Pescapè, Pecchia (bib142) 2020; 10 Selvalingam, Alhusseini, Rogers, Krummen, Abuzaid, Zaman, Baykaner, Clopton, Bailis, Zaharia (bib160) 2019; 140 Warrick, Homsi (bib186) 2017 Maknickas, Maknickas (bib117) 2017 Goldberger, Amaral, Glass, Hausdorff, Ivanov, Mark, Mietus, Moody, Peng, Stanley (bib46) 2000; 101 Park, Yun, Kang (bib134) 2019; 7 Golany, Radinsky (bib45) 2019 Yin, Yang, Zhang, Oki (bib212) 2016; 4 Mukherjee, Choudhury, Datta, Puri, Banerjee, Singh, Ukil, Bandyopadhyay, Pal, Khandelwal (bib127) 2019; 40 Xie, Tu, Wang, Lian, Xu (bib198) 2019; 7 Wang, Zhou, Chang, Chen, Zhou (bib181) 2019 Zhou, Zhu, Nakamura, Mahito (bib228) 2018 Koh, Lee (bib83) 2019; 19 LeCun, Bengio, Hinton (bib89) 2015; 521 Al Rahhal, Bazi, Almubarak, Alajlan, Al Zuair (bib2) 2019; 7 Brock, Donahue, Simonyan (bib17) 2018 Chan, Jia, Gao, Lu, Zeng, Ma (bib22) 2015; 24 Yang, Yu, Jin, Wu, He (bib207) 2017; 65 Xia, Wulan, Wang, Zhang (bib191) 2017 Shen, Voisin, Aliamiri, Avati, Hannun, Ng (bib164) 2019 Xu, Mak, Cheung (bib204) 2020; 24 Zhao, Wang, Cai, Li, Liu (bib224) 2019 Hao, Wibowo, Majmudar, Rajput (bib58) 2019 Rubin, Parvaneh, Rahman, Conroy, Babaeizadeh (bib153) 2017 Shu, Xie, Yang, Li, Li, Liao, Xu, Yang (bib165) 2018; 18 Yu, Gao, Duan, Zhu, Wang, Jiao (bib214) 2018 Ribeiro, Singh, Guestrin (bib149) 2016 Alcaraz, Abásolo, Hornero, Rieta (bib3) 2010; 99 Farhadi, Attarodi, Dabanloo, Mohandespoor, Eslamizadeh (bib36) 2018 Hinton, Vinyals, Dean (bib64) 2015 Ronneberger, Fischer, Brox (bib152) 2015 Plesinger, Nejedly, Viscor, Halamek, Jurak (bib141) 2018; 39 Kamaleswaran, Mahajan, Akbilgic (bib78) 2018; 39 Rajan, Thiagarajan (bib147) 2018 Shah, Rubin (bib161) 2007; 40 Chen, Wang, Xie, Sang, Lv, Zhang, Yang (bib25) 2018 Wang, Xiao, Bi, Li, Zhang, Ma (bib184) 2019 Xiao, Xu, Pelter, Fidler, Badilini, Mortara, Hu (bib195) 2018; 51 Zhao, Hu, Jia, Wang, Li, Yan, You (bib222) 2019 Mousavi, Afghah (bib125) 2019 Tateno, Glass (bib172) 2001; 39 Li, Pang, Wang, Li (bib100) 2018; 314 Liu, Zhou, Cao, Wang, Wang, Zhang (bib110) 2019 Gadaleta, Rossi, Steinhubl, Quer (bi Baloglu (10.1016/j.compbiomed.2020.103801_bib13) 2019; 122 Hong (10.1016/j.compbiomed.2020.103801_bib68) 2019 Li (10.1016/j.compbiomed.2020.103801_bib102) 2018; 6 Fotiadou (10.1016/j.compbiomed.2020.103801_bib40) 2020 Lee (10.1016/j.compbiomed.2020.103801_bib90) 2019; 24 Attia (10.1016/j.compbiomed.2020.103801_bib7) 2018; 138 Wang (10.1016/j.compbiomed.2020.103801_bib182) 2019; 7 Selvalingam (10.1016/j.compbiomed.2020.103801_bib160) 2019; 140 Cai (10.1016/j.compbiomed.2020.103801_bib18) 2019 Xiong (10.1016/j.compbiomed.2020.103801_bib200) 2018; 39 Lee (10.1016/j.compbiomed.2020.103801_bib93) 2019; 134 Ghiasi (10.1016/j.compbiomed.2020.103801_bib43) 2017 Xiao (10.1016/j.compbiomed.2020.103801_bib196) 2018 Li (10.1016/j.compbiomed.2020.103801_bib95) 2019; 7 Choi (10.1016/j.compbiomed.2020.103801_bib28) 2016 Sellami (10.1016/j.compbiomed.2020.103801_bib159) 2019; 122 Chan (10.1016/j.compbiomed.2020.103801_bib22) 2015; 24 Zhang (10.1016/j.compbiomed.2020.103801_bib218) 2019 Zhong (10.1016/j.compbiomed.2020.103801_bib226) 2018; 39 Chen (10.1016/j.compbiomed.2020.103801_bib25) 2018 Limam (10.1016/j.compbiomed.2020.103801_bib105) 2017 Xia (10.1016/j.compbiomed.2020.103801_bib194) 2018; 6 Moody (10.1016/j.compbiomed.2020.103801_bib122) 1983 Mousavi (10.1016/j.compbiomed.2020.103801_bib126) 2020; 15 Oh (10.1016/j.compbiomed.2020.103801_bib132) 2018; 102 Gadaleta (10.1016/j.compbiomed.2020.103801_bib41) 2018; 138 Xiong (10.1016/j.compbiomed.2020.103801_bib201) 2017 Zhou (10.1016/j.compbiomed.2020.103801_bib229) 2019 Feng (10.1016/j.compbiomed.2020.103801_bib38) 2019; 7 Yang (10.1016/j.compbiomed.2020.103801_bib206) 2020 Malik (10.1016/j.compbiomed.2020.103801_bib118) 2018; 39 Ballinger (10.1016/j.compbiomed.2020.103801_bib12) 2018 Labati (10.1016/j.compbiomed.2020.103801_bib86) 2019; 126 Jia (10.1016/j.compbiomed.2020.103801_bib74) 2019 Hong (10.1016/j.compbiomed.2020.103801_bib69) 2019; 40 Dang (10.1016/j.compbiomed.2020.103801_bib31) 2019 Zhao (10.1016/j.compbiomed.2020.103801_bib222) 2019 Brisk (10.1016/j.compbiomed.2020.103801_bib16) 2019; 57 Parvaneh (10.1016/j.compbiomed.2020.103801_bib135) 2018 Han (10.1016/j.compbiomed.2020.103801_bib55) 2020; 185 Zihlmann (10.1016/j.compbiomed.2020.103801_bib231) 2017 Yang (10.1016/j.compbiomed.2020.103801_bib207) 2017; 65 Picon (10.1016/j.compbiomed.2020.103801_bib140) 2019; 14 Gyawali (10.1016/j.compbiomed.2020.103801_bib51) 2017 Hong (10.1016/j.compbiomed.2020.103801_bib67) 2017 He (10.1016/j.compbiomed.2020.103801_bib63) 2019; 7 Jia (10.1016/j.compbiomed.2020.103801_bib73) 2019 Warrick (10.1016/j.compbiomed.2020.103801_bib187) 2018; 39 Schläpfer (10.1016/j.compbiomed.2020.103801_bib157) 2017; 70 Wang (10.1016/j.compbiomed.2020.103801_bib184) 2019 Kiranyaz (10.1016/j.compbiomed.2020.103801_bib80) 2015; 63 Yu (10.1016/j.compbiomed.2020.103801_bib214) 2018 Li (10.1016/j.compbiomed.2020.103801_bib98) 2019; 40 Mincholé (10.1016/j.compbiomed.2020.103801_bib121) 2019; 57 Alcaraz (10.1016/j.compbiomed.2020.103801_bib3) 2010; 99 Yuen (10.1016/j.compbiomed.2020.103801_bib215) 2019; 7 Zhou (10.1016/j.compbiomed.2020.103801_bib228) 2018 Gyawali (10.1016/j.compbiomed.2020.103801_bib52) 2019; 67 Tan (10.1016/j.compbiomed.2020.103801_bib171) 2018; 94 Liu (10.1016/j.compbiomed.2020.103801_bib113) 2017; 22 Hannun (10.1016/j.compbiomed.2020.103801_bib57) 2019; 25 Erdenebayar (10.1016/j.compbiomed.2020.103801_bib35) 2019; 180 Noseworthy (10.1016/j.compbiomed.2020.103801_bib131) 2020 Tison (10.1016/j.compbiomed.2020.103801_bib175) 2019; 12 Han (10.1016/j.compbiomed.2020.103801_bib56) 2016 Zhao (10.1016/j.compbiomed.2020.103801_bib225) 2018; 102 Ghassemi (10.1016/j.compbiomed.2020.103801_bib42) 2018 Lee (10.1016/j.compbiomed.2020.103801_bib91) 2018; 23 Shah (10.1016/j.compbiomed.2020.103801_bib161) 2007; 40 Attia (10.1016/j.compbiomed.2020.103801_bib8) 2019; 25 Li (10.1016/j.compbiomed.2020.103801_bib94) 2019 Wang (10.1016/j.compbiomed.2020.103801_bib185) Harada (10.1016/j.compbiomed.2020.103801_bib60) 2019; 7 Kiranyaz (10.1016/j.compbiomed.2020.103801_bib81) 2017; 7 Lin (10.1016/j.compbiomed.2020.103801_bib108) 2017 Goodfellow (10.1016/j.compbiomed.2020.103801_bib48) 2014 Acharya (10.1016/j.compbiomed.2020.103801_bib1) 2017; 89 Camm (10.1016/j.compbiomed.2020.103801_bib19) 1996 Smith (10.1016/j.compbiomed.2020.103801_bib166) 2019; 52 Yin (10.1016/j.compbiomed.2020.103801_bib213) 2017; 140 Attin (10.1016/j.compbiomed.2020.103801_bib10) 2017; 136 Li (10.1016/j.compbiomed.2020.103801_bib97) 2018; 294 Yin (10.1016/j.compbiomed.2020.103801_bib212) 2016; 4 Parvaneh (10.1016/j.compbiomed.2020.103801_bib136) 2019; 57 Zhang (10.1016/j.compbiomed.2020.103801_bib221) 2019; 125 Ronneberger (10.1016/j.compbiomed.2020.103801_bib152) 2015 Bousseljot (10.1016/j.compbiomed.2020.103801_bib15) 1995; 40 Shaker (10.1016/j.compbiomed.2020.103801_bib162) 2020; 8 Nikolic (10.1016/j.compbiomed.2020.103801_bib129) 1982; 66 Al Rahhal (10.1016/j.compbiomed.2020.103801_bib2) 2019; 7 Park (10.1016/j.compbiomed.2020.103801_bib134) 2019; 7 Saadatnejad (10.1016/j.compbiomed.2020.103801_bib155) 2020; 24 Tateno (10.1016/j.compbiomed.2020.103801_bib172) 2001; 39 Warrick (10.1016/j.compbiomed.2020.103801_bib186) 2017 Yıldırım (10.1016/j.compbiomed.2020.103801_bib211) 2018; 102 Baalman (10.1016/j.compbiomed.2020.103801_bib11) 2019; 140 Zhang (10.1016/j.compbiomed.2020.103801_bib220) 2019; 7 He (10.1016/j.compbiomed.2020.103801_bib62) 2019; 7 Pourbabaee (10.1016/j.compbiomed.2020.103801_bib143) 2017; 48 Zhang (10.1016/j.compbiomed.2020.103801_bib219) 2017; 5 Elola (10.1016/j.compbiomed.2020.103801_bib34) 2018 Shu (10.1016/j.compbiomed.2020.103801_bib165) 2018; 18 Attia (10.1016/j.compbiomed.2020.103801_bib9) 2018; 13 Fotiadou (10.1016/j.compbiomed.2020.103801_bib39) 2019 Nguyen (10.1016/j.compbiomed.2020.103801_bib128) 2018; 8 Zhou (10.1016/j.compbiomed.2020.103801_bib227) 2019 Gogna (10.1016/j.compbiomed.2020.103801_bib44) 2016; 64 Golany (10.1016/j.compbiomed.2020.103801_bib45) 2019 LeCun (10.1016/j.compbiomed.2020.103801_bib89) 2015; 521 Lai (10.1016/j.compbiomed.2020.103801_bib88) 2019; 7 Zhao (10.1016/j.compbiomed.2020.103801_bib223) 2019; 7 Shashikumar (10.1016/j.compbiomed.2020.103801_bib163) 2018 Chu (10.1016/j.compbiomed.2020.103801_bib29) 2019; 7 Mousavi (10.1016/j.compbiomed.2020.103801_bib125) 2019 Brock (10.1016/j.compbiomed.2020.103801_bib17) 2018 Romero (10.1016/j.compbiomed.2020.103801_bib151) 2001 Kiranyaz (10.1016/j.compbiomed.2020.103801_bib82) 2015 Hinton (10.1016/j.compbiomed.2020.103801_bib64) Kwon (10.1016/j.compbiomed.2020.103801_bib85) 2019; 14 Wu (10.1016/j.compbiomed.2020.103801_bib190) 2019 Hao (10.1016/j.compbiomed.2020.103801_bib58) 2019 Faust (10.1016/j.compbiomed.2020.103801_bib37) 2018; 102 Kalyakulina (10.1016/j.compbiomed.2020.103801_bib77) 2018 Van Steenkiste (10.1016/j.compbiomed.2020.103801_bib177) 2020; 10 Rastgoo (10.1016/j.compbiomed.2020.103801_bib148) 2019; 138 Yildirim (10.1016/j.compbiomed.2020.103801_bib209) 2018; 96 Xia (10.1016/j.compbiomed.2020.103801_bib192) 2018; 93 Xu (10.1016/j.compbiomed.2020.103801_bib203) 2019; 23 Chiang (10.1016/j.compbiomed.2020.103801_bib27) 2019; 7 Zhao (10.1016/j.compbiomed.2020.103801_bib224) 2019 Qiu (10.1016/j.compbiomed.2020.103801_bib144) 2017 Xu (10.1016/j.compbiomed.2020.103801_bib205) 2018 Xiong (10.1016/j.compbiomed.2020.103801_bib199) 2016; 37 Li (10.1016/j.compbiomed.2020.103801_bib101) 2020; 391 Zhang (10.1016/j.compbiomed.2020.103801_bib217) 2017; 12 Ribeiro (10.1016/j.compbiomed.2020.103801_bib149) 2016 Xu (10.1016/j.compbiomed.2020.103801_bib202) 2019; 7 Hammad (10.1016/j.compbiomed.2020.103801_bib54) 2018; 7 Tadesse (10.1016/j.compbiomed.2020.103801_bib170) 2019 Xie (10.1016/j.compbiomed.2020.103801_bib197) 2018 Kamaleswaran (10.1016/j.compbiomed.2020.103801_bib78) 2018; 39 Hong (10.1016/j.compbiomed.2020.103801_bib65) 2019 Habib (10.1016/j.compbiomed.2020.103801_bib53) 2019; 7 Lee (10.1016/j.compbiomed.2020.103801_bib92) 2019; 7 Ye (10.1016/j.compbiomed.2020.103801_bib208) 2019; 7 Chauhan (10.1016/j.compbiomed.2020.103801_bib24) 2019; 109 Lin (10.1016/j.compbiomed.2020.103801_bib107) 2017 Maaten (10.1016/j.compbiomed.2020.103801_bib115) 2008; 9 Oh (10.1016/j.compbiomed.2020.103801_bib133) 2019; 105 Zhai (10.1016/j.compbiomed.2020.103801_bib216) 2018; 6 Behar (10.1016/j.compbiomed.2020.103801_bib14) 2018; 9 Li (10.1016/j.compbiomed.2020.103801_bib100) 2018; 314 Niu (10.1016/j.compbiomed.2020.103801_bib130) 2019; 24 Lin (10.1016/j.compbiomed.2020.103801_bib106) 2008; 55 Rajan (10.1016/j.compbiomed.2020.103801_bib147) 2018 Sun (10.1016/j.compbiomed.2020.103801_bib169) 2019 Wołk (10.1016/j.compbiomed.2020.103801_bib189) 2019 Mukherjee (10.1016/j.compbiomed.2020.103801_bib127) 2019; 40 Jimenez-Perez (10.1016/j.compbiomed.2020.103801_bib76) 2019 Porumb (10.1016/j.compbiomed.2020.103801_bib142) 2020; 10 Maknickas (10.1016/j.compbiomed.2020.103801_bib117) 2017 Parvaneh (10.1016/j.compbiomed.2020.103801_bib137) 2018; 39 Huang (10.1016/j.compbiomed.2020.103801_bib71) 2019; 7 Mathews (10.1016/j.compbiomed.2020.103801_bib119) 2018; 99 Ribeiro (10.1016/j.compbiomed.2020.103801_bib150) 2018 Lynn (10.1016/j.compbiomed.2020.103801_bib114) 2019; 7 Rubin (10.1016/j.compbiomed.2020.103801_bib153) 2017 Li (10.1016/j.compbiomed.2020.103801_bib103) 2019; 7 Strodthoff (10.1016/j.compbiomed.2020.103801_bib168) 2018 Hong (10.1016/j.compbiomed.2020.103801_bib66) 2020 Wang (10.1016/j.compbiomed.2020.103801_bib179) 2019 Xia (10.1016/j.compbiomed.2020.103801_bib191) 2017 He (10.1016/j.compbiomed.2020.103801_bib61) 2019; 7 Chen (10.1016/j.compbiomed.2020.103801_bib26) 2018 Doersch (10.1016/j.compbiomed.2020.103801_bib33) 2016 Kimura (10.1016/j.compbiomed.2020.103801_bib79) 2018; 138 Liu (10.1016/j.compbiomed.2020.103801_bib112) 2020; 24 Wang (10.1016/j.compbiomed.2020.103801_bib183) 2017 Liu (10.1016/j.compbiomed.2020.103801_bib109) 2018; 8 Sodmann (10.1016/j.compbiomed.2020.103801_bib167) 2018; 39 Koh (10.1016/j.compbiomed.2020.103801_bib83) 2019; 19 Urtnasan (10.1016/j.compbiomed.2020.103801_bib176) 2018; 39 Chang (10.1016/j.compbiomed.2020.103801_bib23) 2018 Farhadi (10.1016/j.compbiomed.2020.103801_bib36) 2018 Wang (10.1016/j.compbiomed.2020.1 |
| References_xml | – volume: 39 year: 2018 ident: bib173 article-title: Abductive reasoning as a basis to reproduce expert criteria in ecg atrial fibrillation identification publication-title: Physiol. Meas. – start-page: 2672 year: 2014 end-page: 2680 ident: bib48 article-title: Generative adversarial nets publication-title: Advances in Neural Information Processing Systems – volume: 7 start-page: 144292 year: 2019 end-page: 144302 ident: bib60 article-title: Biosignal generation and latent variable analysis with recurrent generative adversarial networks publication-title: IEEE Access – volume: 55 start-page: 680 year: 2008 end-page: 690 ident: bib106 article-title: Frequency-domain features for ecg beat discrimination using grey relational analysis-based classifier publication-title: Comput. Math. Appl. – volume: 7 start-page: 146457 year: 2019 end-page: 146469 ident: bib96 article-title: Dual-input neural network integrating feature extraction and deep learning for coronary artery disease detection using electrocardiogram and phonocardiogram publication-title: IEEE Access – start-page: 1909 year: 2019 end-page: 1916 ident: bib164 article-title: Ambulatory atrial fibrillation monitoring using wearable photoplethysmography with deep learning publication-title: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining – start-page: 227 year: 1983 end-page: 230 ident: bib122 article-title: A new method for detecting atrial fibrillation using rr intervals publication-title: Comput. Cardiol. – volume: 14 year: 2019 ident: bib140 article-title: Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia publication-title: PloS One – volume: 39 year: 2018 ident: bib176 article-title: Multiclass classification of obstructive sleep apnea/hypopnea based on a convolutional neural network from a single-lead electrocardiogram publication-title: Physiol. Meas. – volume: 39 year: 2018 ident: bib141 article-title: Parallel use of a convolutional neural network and bagged tree ensemble for the classification of holter ecg publication-title: Physiol. Meas. – start-page: 673 year: 1997 end-page: 676 ident: bib87 article-title: A database for evaluation of algorithms for measurement of qt and other waveform intervals in the ecg publication-title: Computers in Cardiology 1997 – start-page: 2559 year: 2018 end-page: 2562 ident: bib25 article-title: Region aggregation network: improving convolutional neural network for ecg characteristic detection publication-title: 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) – volume: 9 start-page: 6734 year: 2019 ident: bib230 article-title: Electrocardiogram generation with a bidirectional lstm-cnn generative adversarial network publication-title: Sci. Rep. – volume: 99 start-page: 53 year: 2018 end-page: 62 ident: bib119 article-title: A novel application of deep learning for single-lead ecg classification publication-title: Comput. Biol. Med. – volume: 39 year: 2018 ident: bib137 article-title: Analyzing single-lead short ecg recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation publication-title: Physiol. Meas. – start-page: 2633 year: 2019 end-page: 2636 ident: bib65 article-title: Ecg biometric recognition: template-free approaches based on deep learning publication-title: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) – start-page: 2565 year: 2018 end-page: 2573 ident: bib205 article-title: Raim: recurrent attentive and intensive model of multimodal patient monitoring data publication-title: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining – volume: 8 start-page: 1368 year: 2018 end-page: 1373 ident: bib109 article-title: An open access database for evaluating the algorithms of electrocardiogram rhythm and morphology abnormality detection publication-title: Journal of Medical Imaging and Health Informatics – volume: 52 start-page: 88 year: 2019 end-page: 95 ident: bib166 article-title: A deep neural network learning algorithm outperforms a conventional algorithm for emergency department electrocardiogram interpretation publication-title: J. Electrocardiol. – volume: 7 start-page: 89152 year: 2019 end-page: 89161 ident: bib21 article-title: Atrial fibrillation detection using an improved multi-scale decomposition enhanced residual convolutional neural network publication-title: IEEE Access – volume: 7 start-page: 93275 year: 2019 end-page: 93285 ident: bib53 article-title: Impact of ecg dataset diversity on generalization of cnn model for detecting qrs complex publication-title: IEEE access – volume: 138 year: 2019 ident: bib148 article-title: Automatic driver stress level classification using multimodal deep learning publication-title: Expert Syst. Appl. – year: 2001 ident: bib151 article-title: Ecg frequency domain features extraction: a new characteristic for arrhythmias classification publication-title: 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society – volume: 106 start-page: 232 year: 2006 end-page: 237 ident: bib50 article-title: Common errors in computer electrocardiogram interpretation publication-title: Int. J. Cardiol. – year: 1996 ident: bib19 article-title: Heart Rate Variability: Standards of Measurement, Physiological Interpretation and Clinical Use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology – volume: 7 start-page: 51598 year: 2019 end-page: 51607 ident: bib29 article-title: Ecg authentication method based on parallel multi-scale one-dimensional residual network with center and margin loss publication-title: IEEE Access – year: 2019 ident: bib218 article-title: Automated detection and localization of myocardial infarction with staked sparse autoencoder and treebagger – year: 2019 ident: bib189 article-title: Early and Remote Detection of Possible Heartbeat Problems with Convolutional Neural Networks and Multipart Interactive Training – volume: 7 start-page: 153751 year: 2019 end-page: 153760 ident: bib198 article-title: Feature enrichment based convolutional neural network for heartbeat classification from electrocardiogram publication-title: IEEE Access – volume: 25 start-page: 65 year: 2019 ident: bib57 article-title: Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network publication-title: Nat. Med. – volume: 12 year: 2017 ident: bib217 article-title: Ecg data compression using a neural network model based on multi-objective optimization publication-title: PloS One – start-page: 1913 year: 2019 end-page: 1916 ident: bib74 article-title: An electrocardiogram delineator via deep segmentation network publication-title: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) – volume: 102 start-page: 411 year: 2018 end-page: 420 ident: bib211 article-title: Arrhythmia detection using deep convolutional neural network with long duration ecg signals publication-title: Comput. Biol. Med. – volume: 136 year: 2017 ident: bib10 article-title: Annotating ecg signals with deep neural networks publication-title: Circulation – volume: 138 year: 2018 ident: bib7 article-title: Electrocardiographic screening for atrial fibrillation while in sinus rhythm using deep learning publication-title: Circulation – volume: 122 start-page: 75 year: 2019 end-page: 84 ident: bib159 article-title: A robust deep convolutional neural network with batch-weighted loss for heartbeat classification publication-title: Expert Syst. Appl. – volume: 24 start-page: 503 year: 2020 end-page: 514 ident: bib112 article-title: Mfb-cbrnn: a hybrid network for mi detection using 12-lead ecgs publication-title: IEEE Journal of Biomedical and Health Informatics – volume: 140 year: 2019 ident: bib145 article-title: Deep neural networks can predict 1-year mortality directly from ecg signal, even when clinically interpreted as normal publication-title: Circulation – start-page: 2551 year: 2018 end-page: 2554 ident: bib228 article-title: Premature ventricular contraction detection from ambulatory ecg using recurrent neural networks publication-title: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) – year: 2020 ident: bib206 article-title: Detection of strict left bundle branch block by neural network and a method to test detection consistency – volume: 7 start-page: 169359 year: 2019 end-page: 169370 ident: bib215 article-title: Inter-patient cnn-lstm for qrs complex detection in noisy ecg signals publication-title: IEEE Access – start-page: 3504 year: 2016 end-page: 3512 ident: bib28 article-title: RETAIN: an interpretable predictive model for healthcare using reverse time attention mechanism publication-title: Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016 – start-page: 1 year: 2017 end-page: 4 ident: bib186 article-title: Cardiac arrhythmia detection from ecg combining convolutional and long short-term memory networks publication-title: 2017 Computing in Cardiology (CinC) – volume: 180 year: 2019 ident: bib35 article-title: Deep learning approaches for automatic detection of sleep apnea events from an electrocardiogram publication-title: Comput. Methods Progr. Biomed. – start-page: 5642 year: 2019 end-page: 5645 ident: bib58 article-title: Spectro-temporal feature based multi-channel convolutional neural network for ecg beat classification publication-title: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) – year: 2018 ident: bib77 article-title: Lu Electrocardiography Database: a New Open-Access Validation Tool for Delineation Algorithms – start-page: 1 year: 2018 end-page: 3 ident: bib36 article-title: Classification of atrial fibrillation using stacked auto encoders neural networks publication-title: 2018 Computing in Cardiology Conference (CinC) – year: 2019 ident: bib179 article-title: Automatic Classification of Cad Ecg Signals with Sdae and Bidirectional Long Short-Term Term Network – volume: 99 start-page: 124 year: 2010 end-page: 132 ident: bib3 article-title: Optimal parameters study for sample entropy-based atrial fibrillation organization analysis publication-title: Comput. Methods Progr. Biomed. – volume: 7 start-page: 77849 year: 2019 end-page: 77856 ident: bib103 article-title: Classification of atrial fibrillation recurrence based on a convolution neural network with svm architecture publication-title: IEEE Access – start-page: 1 year: 2017 end-page: 9 ident: bib183 article-title: Using a random forest to inspire a neural network and improving on it publication-title: Proceedings of the 2017 SIAM International Conference on Data Mining, Houston, Texas, USA, April 27-29, 2017 – volume: 12 year: 2019 ident: bib6 article-title: Age and sex estimation using artificial intelligence from standard 12-lead ecgs publication-title: Circulation: Arrhythmia and Electrophysiology – volume: 105 start-page: 92 year: 2019 end-page: 101 ident: bib133 article-title: Automated beat-wise arrhythmia diagnosis using modified u-net on extended electrocardiographic recordings with heterogeneous arrhythmia types publication-title: Comput. Biol. Med. – year: 2018 ident: bib168 article-title: Detecting and Interpreting Myocardial Infarction Using Fully Convolutional Neural Networks – year: 2019 ident: bib76 article-title: U-net architecture for the automatic detection and delineation of the electrocardiogram publication-title: 2019 Computing in Cardiology (CinC) – year: 2020 ident: bib84 article-title: Electrocardiogram Generation and Feature Extraction Using a Variational Autoencoder – start-page: 1 year: 2017 end-page: 4 ident: bib105 article-title: Atrial fibrillation detection and ecg classification based on convolutional recurrent neural network publication-title: 2017 Computing in Cardiology (CinC) – year: 2018 ident: bib17 article-title: Large Scale gan Training for High Fidelity Natural Image Synthesis – volume: 39 start-page: 664 year: 2001 end-page: 671 ident: bib172 article-title: Automatic detection of atrial fibrillation using the coefficient of variation and density histograms of rr and δrr intervals publication-title: Med. Biol. Eng. Comput. – volume: 125 start-page: 668 year: 2019 end-page: 676 ident: bib221 article-title: Ecg-based personal recognition using a convolutional neural network publication-title: Pattern Recogn. Lett. – volume: 40 year: 2019 ident: bib98 article-title: Ventricular ectopic beat detection using a wavelet transform and a convolutional neural network publication-title: Physiol. Meas. – start-page: 248 year: 2009 end-page: 255 ident: bib32 article-title: Imagenet: a large-scale hierarchical image database publication-title: IEEE Conference on Computer Vision and Pattern Recognition – volume: 20 start-page: 45 year: 2001 end-page: 50 ident: bib123 article-title: The impact of the mit-bih arrhythmia database publication-title: IEEE Eng. Med. Biol. Mag. – start-page: 1 year: 2017 end-page: 4 ident: bib191 article-title: Atrial fibrillation detection using stationary wavelet transform and deep learning publication-title: 2017 Computing in Cardiology (CinC) – volume: 138 year: 2018 ident: bib41 article-title: Deep learning to detect atrial fibrillation from short noisy ecg segments measured with wireless sensors publication-title: Circulation – start-page: 256 year: 2018 ident: bib196 article-title: A deep learning approach to examine ischemic st changes in ambulatory ecg recordings publication-title: AMIA Summits on Translational Science Proceedings – volume: 48 start-page: 2095 year: 2017 end-page: 2104 ident: bib143 article-title: Deep convolutional neural networks and learning ecg features for screening paroxysmal atrial fibrillation patients publication-title: IEEE Transactions on Systems, Man, and Cybernetics: Systems – start-page: 1 year: 2019 end-page: 4 ident: bib224 article-title: Pvc recognition for wearable ecgs using modified frequency slice wavelet transform and convolutional neural network publication-title: 2019 Computing in Cardiology (CinC) – volume: 115 start-page: 465 year: 2019 end-page: 473 ident: bib4 article-title: A deep learning approach for real-time detection of atrial fibrillation publication-title: Expert Syst. Appl. – start-page: 1 year: 2017 end-page: 4 ident: bib51 article-title: Automatic coordinate prediction of the exit of ventricular tachycardia from 12-lead electrocardiogram publication-title: 2017 Computing in Cardiology (CinC) – volume: 93 start-page: 84 year: 2018 end-page: 92 ident: bib192 article-title: Detecting atrial fibrillation by deep convolutional neural networks publication-title: Comput. Biol. Med. – volume: 109 start-page: 14 year: 2019 end-page: 21 ident: bib24 article-title: Ecg anomaly class identification using lstm and error profile modeling publication-title: Comput. Biol. Med. – volume: 10 start-page: 1 year: 2020 end-page: 16 ident: bib142 article-title: Precision medicine and artificial intelligence: a pilot study on deep learning for hypoglycemic events detection based on ecg publication-title: Sci. Rep. – volume: 134 start-page: 66 year: 2019 end-page: 78 ident: bib93 article-title: Qrs detection method based on fully convolutional networks for capacitive electrocardiogram publication-title: Expert Syst. Appl. – start-page: 5888 year: 2019 end-page: 5894 ident: bib68 article-title: Mina: multilevel knowledge-guided attention for modeling electrocardiography signals publication-title: Tweenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI) – volume: 185 year: 2020 ident: bib55 article-title: Ml–resnet: a novel network to detect and locate myocardial infarction using 12 leads ecg publication-title: Comput. Methods Progr. Biomed. – volume: 184 year: 2020 ident: bib59 article-title: Multi-branch fusion network for myocardial infarction screening from 12-lead ecg images publication-title: Comput. Methods Progr. Biomed. – volume: 521 start-page: 436 year: 2015 end-page: 444 ident: bib89 article-title: Deep learning publication-title: Nature – start-page: 1 year: 2018 end-page: 4 ident: bib23 article-title: Af detection by exploiting the spectral and temporal characteristics of ecg signals with the lstm model publication-title: 2018 Computing in Cardiology Conference (CinC) – volume: 40 year: 2019 ident: bib69 article-title: Combining deep neural networks and engineered features for cardiac arrhythmia detection from ecg recordings publication-title: Physiol. Meas. – volume: 7 start-page: 76295 year: 2019 end-page: 76304 ident: bib95 article-title: Automated heartbeat classification using 3-d inputs based on convolutional neural network with multi-fields of view publication-title: IEEE Access – start-page: 1135 year: 2016 end-page: 1144 ident: bib149 article-title: why should I trust you?”: explaining the predictions of any classifier publication-title: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13-17, 2016 – volume: 6 start-page: 16529 year: 2018 end-page: 16538 ident: bib194 article-title: An automatic cardiac arrhythmia classification system with wearable electrocardiogram publication-title: IEEE Access – volume: 7 start-page: 34060 year: 2019 end-page: 34067 ident: bib223 article-title: Noise rejection for wearable ecgs using modified frequency slice wavelet transform and convolutional neural networks publication-title: IEEE Access – year: 2015 ident: bib64 article-title: Distilling the knowledge in a neural network. CoRR abs/1503 – year: 2019 ident: bib184 article-title: Pay attention and watch temporal correlation: a novel 1-d convolutional neural network for ecg record classification publication-title: 2019 Computing in Cardiology (CinC) – year: 2019 ident: bib169 article-title: Morphological Arrhythmia Automated Diagnosis Method Using Gray-Level Co-occurrence Matrix Enhanced Convolutional Neural Network – volume: 89 start-page: 389 year: 2017 end-page: 396 ident: bib1 article-title: A deep convolutional neural network model to classify heartbeats publication-title: Comput. Biol. Med. – volume: 7 start-page: 48392 year: 2019 end-page: 48404 ident: bib92 article-title: Personal identification using a robust eigen ecg network based on time-frequency representations of ecg signals publication-title: IEEE Access – start-page: 1500 year: 2019 end-page: 1503 ident: bib222 article-title: Deep learning based patient-specific classification of arrhythmia on ecg signal publication-title: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) – volume: 102 start-page: 168 year: 2018 end-page: 179 ident: bib225 article-title: Ecg authentication system design incorporating a convolutional neural network and generalized s-transformation publication-title: Comput. Biol. Med. – volume: 8 start-page: 35592 year: 2020 end-page: 35605 ident: bib162 article-title: Generalization of convolutional neural networks for ecg classification using generative adversarial networks publication-title: IEEE Access – volume: 7 start-page: 57 year: 2018 end-page: 67 ident: bib156 article-title: Using deep convolutional neural network for emotion detection on a physiological signals dataset (amigos) publication-title: IEEE Access – start-page: 1 year: 2017 end-page: 4 ident: bib158 article-title: Beat by beat: classifying cardiac arrhythmias with recurrent neural networks publication-title: 2017 Computing in Cardiology (CinC) – start-page: 2707 year: 2018 end-page: 2710 ident: bib111 article-title: Classification of heart diseases based on ecg signals using long short-term memory publication-title: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) – start-page: 1 year: 2017 end-page: 4 ident: bib5 article-title: Comparing feature-based classifiers and convolutional neural networks to detect arrhythmia from short segments of ecg publication-title: 2017 Computing in Cardiology (CinC) – start-page: 1527 year: 2018 end-page: 1535 ident: bib150 article-title: Anchors: high-precision model-agnostic explanations publication-title: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th Innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18) – volume: 9 start-page: 1390 year: 2018 ident: bib14 article-title: Physiozoo: a novel open access platform for heart rate variability analysis of mammalian electrocardiographic data publication-title: Front. Physiol. – volume: 1 year: 2019 ident: bib75 article-title: A novel multi-module neural network system for imbalanced heartbeats classification publication-title: Expert Syst. Appl. X – start-page: 234 year: 2015 end-page: 241 ident: bib152 article-title: U-net: convolutional networks for biomedical image segmentation publication-title: International Conference on Medical Image Computing and Computer-Assisted Intervention – volume: 94 start-page: 19 year: 2018 end-page: 26 ident: bib171 article-title: Application of stacked convolutional and long short-term memory network for accurate identification of cad ecg signals publication-title: Comput. Biol. Med. – volume: 51 start-page: S78 year: 2018 ident: bib195 article-title: Monitoring significant st changes through deep learning publication-title: J. Electrocardiol. – year: 2016 ident: bib33 article-title: Tutorial on Variational Autoencoders – volume: 24 start-page: 515 year: 2020 end-page: 523 ident: bib155 article-title: Lstm-based ecg classification for continuous monitoring on personal wearable devices publication-title: IEEE Journal of Biomedical and Health Informatics – volume: 391 start-page: 83 year: 2020 end-page: 95 ident: bib101 article-title: Toward improving ecg biometric identification using cascaded convolutional neural networks publication-title: Neurocomputing – volume: 7 start-page: 159369 year: 2019 end-page: 159378 ident: bib208 article-title: Ecg generation with sequence generative adversarial nets optimized by policy gradient publication-title: IEEE Access – volume: 7 start-page: 50431 year: 2019 end-page: 50439 ident: bib38 article-title: A probabilistic process neural network and its application in ecg classification publication-title: IEEE Access – start-page: 4433 year: 2019 end-page: 4439 ident: bib227 article-title: Beatgan: anomalous rhythm detection using adversarially generated time series publication-title: Proceedings of the 28th International Joint Conference on Artificial Intelligence – start-page: 3155 year: 2017 end-page: 3165 ident: bib107 article-title: Adversarial ranking for language generation publication-title: Advances in Neural Information Processing Systems – volume: 10 start-page: 1 year: 2020 end-page: 12 ident: bib177 article-title: Transfer learning in ecg classification from human to horse using a novel parallel neural network architecture publication-title: Sci. Rep. – start-page: 2608 year: 2015 end-page: 2611 ident: bib82 article-title: Convolutional neural networks for patient-specific ecg classification publication-title: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) – volume: 7 start-page: 145395 year: 2019 end-page: 145405 ident: bib114 article-title: A deep bidirectional gru network model for biometric electrocardiogram classification based on recurrent neural networks publication-title: IEEE Access – volume: 6 start-page: 39734 year: 2018 end-page: 39744 ident: bib102 article-title: Combining convolutional neural network and distance distribution matrix for identification of congestive heart failure publication-title: IEEE Access – volume: 23 start-page: 1574 year: 2019 end-page: 1584 ident: bib203 article-title: Towards end-to-end ecg classification with raw signal extraction and deep neural networks publication-title: IEEE Journal of Biomedical and Health Informatics – volume: 7 start-page: 60806 year: 2019 end-page: 60813 ident: bib27 article-title: Noise reduction in ecg signals using fully convolutional denoising autoencoders publication-title: IEEE Access – start-page: 1 year: 2017 end-page: 4 ident: bib201 article-title: Robust ecg signal classification for detection of atrial fibrillation using a novel neural network publication-title: 2017 Computing in Cardiology (CinC) – volume: 6 start-page: 27465 year: 2018 end-page: 27472 ident: bib216 article-title: Automated ecg classification using dual heartbeat coupling based on convolutional neural network publication-title: IEEE Access – start-page: 1303 year: 2019 end-page: 1307 ident: bib110 article-title: A lstm and cnn based assemble neural network framework for arrhythmias classification publication-title: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) – volume: 7 start-page: 9270 year: 2017 ident: bib81 article-title: Personalized monitoring and advance warning system for cardiac arrhythmias publication-title: Sci. Rep. – start-page: 1 year: 2017 end-page: 4 ident: bib30 article-title: Af Classification from a Short Single Lead Ecg Recording: the Physionet/computing in Cardiology Challenge 2017 publication-title: 2017 Computing in Cardiology (CinC) – volume: 19 start-page: 2828 year: 2019 ident: bib83 article-title: An evaluation method of safe driving for senior adults using ecg signals publication-title: Sensors – volume: 39 year: 2018 ident: bib200 article-title: Ecg signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural network publication-title: Physiol. Meas. – volume: 349 start-page: 212 year: 2019 end-page: 224 ident: bib180 article-title: Adversarial de-noising of electrocardiogram publication-title: Neurocomputing – year: 2020 ident: bib40 article-title: End-to-end trained cnn encoder-decoder network for fetal ecg signal denoising – start-page: 82 year: 2019 ident: bib120 article-title: A comparison of patient history-and ekg-based cardiac risk scores publication-title: AMIA Summits on Translational Science Proceedings – start-page: 2999 year: 2017 end-page: 3007 ident: bib108 article-title: Focal loss for dense object detection publication-title: IEEE International Conference on Computer Vision, October 22-29, 2017 – volume: 14 year: 2019 ident: bib85 article-title: Artificial intelligence algorithm for predicting mortality of patients with acute heart failure publication-title: PloS One – start-page: 1 year: 2019 ident: bib73 article-title: Detection of first-degree atrioventricular block on variable-length electrocardiogram via a multimodal deep learning method publication-title: 2019 Computing in Cardiology (CinC) – volume: 7 start-page: 7989 year: 2019 end-page: 8001 ident: bib193 article-title: A novel wearable electrocardiogram classification system using convolutional neural networks and active learning publication-title: IEEE Access – start-page: 1 year: 2018 end-page: 4 ident: bib20 article-title: Deep learning based qrs multilead delineator in electrocardiogram signals publication-title: 2018 Computing in Cardiology Conference (CinC) – start-page: 336 year: 2018 end-page: 339 ident: bib26 article-title: Finger ecg based two-phase authentication using 1d convolutional neural networks publication-title: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) – volume: 101 start-page: e215 year: 2000 end-page: e220 ident: bib46 article-title: Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals publication-title: Circulation – start-page: 4636 year: 2018 end-page: 4639 ident: bib214 article-title: Qrs detection and measurement method of ecg paper based on convolutional neural networks publication-title: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) – volume: 63 start-page: 664 year: 2015 end-page: 675 ident: bib80 article-title: Real-time patient-specific ecg classification by 1-d convolutional neural networks publication-title: IEEE (Inst. Electr. Electron. Eng.) Trans. Biomed. Eng. – year: 2003 ident: bib139 article-title: The Philips 12-lead algorithm physician's guide – volume: 39 start-page: 104005 year: 2018 ident: bib167 article-title: A convolutional neural network for ecg annotation as the basis for classification of cardiac rhythms publication-title: Physiol. Meas. – volume: 14 year: 2019 ident: bib49 article-title: Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients publication-title: PloS One – volume: 7 start-page: 182225 year: 2019 end-page: 182237 ident: bib2 article-title: Dense convolutional networks with focal loss and image generation for electrocardiogram classification publication-title: IEEE Access – volume: 7 start-page: 130074 year: 2019 end-page: 130084 ident: bib88 article-title: An automatic system for real-time identifying atrial fibrillation by using a lightweight convolutional neural network publication-title: IEEE access – volume: 314 start-page: 336 year: 2018 end-page: 346 ident: bib100 article-title: Patient-specific ecg classification by deeper cnn from generic to dedicated publication-title: Neurocomputing – volume: 138 year: 2018 ident: bib116 article-title: Artificial intelligence detects pediatric heart murmurs with cardiologist-level accuracy publication-title: Circulation – start-page: 715 year: 2018 end-page: 723 ident: bib163 article-title: Detection of paroxysmal atrial fibrillation using attention-based bidirectional recurrent neural networks publication-title: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining – volume: 24 start-page: 717 year: 2020 end-page: 727 ident: bib204 article-title: I-vector based patient adaptation of deep neural networks for automatic heartbeat classification publication-title: IEEE Journal of Biomedical and Health Informatics – year: 2019 ident: bib94 article-title: Automated heartbeat classification exploiting convolutional neural network with channel-wise attention – volume: 294 start-page: 94 year: 2018 end-page: 101 ident: bib97 article-title: A method to detect sleep apnea based on deep neural network and hidden markov model using single-lead ecg signal publication-title: Neurocomputing – start-page: 1 year: 2018 end-page: 4 ident: bib34 article-title: Deep learning for pulse detection in out-of-hospital cardiac arrest using the ecg publication-title: 2018 Computing in Cardiology Conference (CinC) – volume: 4 start-page: 6344 year: 2016 end-page: 6351 ident: bib212 article-title: Ecg monitoring system integrated with ir-uwb radar based on cnn publication-title: IEEE Access – volume: 66 start-page: 218 year: 1982 end-page: 225 ident: bib129 article-title: Sudden death recorded during holter monitoring publication-title: Circulation – volume: 57 start-page: S61 year: 2019 end-page: S64 ident: bib121 article-title: Machine learning in the electrocardiogram publication-title: J. Electrocardiol. – volume: 114 start-page: 54 year: 2018 end-page: 64 ident: bib47 article-title: Ecg classification using three-level fusion of different feature descriptors publication-title: Expert Syst. Appl. – volume: 122 start-page: 23 year: 2019 end-page: 30 ident: bib13 article-title: Classification of myocardial infarction with multi-lead ecg signals and deep cnn publication-title: Pattern Recogn. Lett. – volume: 7 start-page: 161152 year: 2019 end-page: 161166 ident: bib220 article-title: Localization of myocardial infarction with multi-lead bidirectional gated recurrent unit neural network publication-title: IEEE Access – volume: 7 start-page: 92871 year: 2019 end-page: 92880 ident: bib71 article-title: Ecg arrhythmia classification using stft-based spectrogram and convolutional neural network publication-title: IEEE Access – volume: 15 year: 2020 ident: bib126 article-title: Single-modal and multi-modal false arrhythmia alarm reduction using attention-based convolutional and recurrent neural networks publication-title: PloS One – volume: 13 year: 2018 ident: bib9 article-title: Noninvasive assessment of dofetilide plasma concentration using a deep learning (neural network) analysis of the surface electrocardiogram: a proof of concept study publication-title: PloS One – volume: 24 start-page: 5017 year: 2015 end-page: 5032 ident: bib22 article-title: Pcanet: a simple deep learning baseline for image classification? publication-title: IEEE Trans. Image Process. – start-page: 2296 year: 2017 end-page: 2299 ident: bib144 article-title: Elimination of power line interference from ecg signals using recurrent neural networks publication-title: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) – volume: 18 start-page: 2074 year: 2018 ident: bib165 article-title: A review of emotion recognition using physiological signals publication-title: Sensors – volume: 22 start-page: 1434 year: 2017 end-page: 1444 ident: bib113 article-title: Real-time multilead convolutional neural network for myocardial infarction detection publication-title: IEEE journal of biomedical and health informatics – start-page: 148 year: 2020 end-page: 152 ident: bib66 article-title: Cardiolearn: a cloud deep learning service for cardiac disease detection from electrocardiogram publication-title: Companion Proceedings of the Web Conference 2020 – volume: 23 start-page: 2375 year: 2018 end-page: 2385 ident: bib91 article-title: Bidirectional recurrent auto-encoder for photoplethysmogram denoising publication-title: IEEE Journal of Biomedical and Health Informatics – volume: 39 year: 2018 ident: bib78 article-title: A robust deep convolutional neural network for the classification of abnormal cardiac rhythm using single lead electrocardiograms of variable length publication-title: Physiol. Meas. – start-page: 246 year: 2020 end-page: 254 ident: bib124 article-title: Deep learning for ecg segmentation publication-title: Advances in Neural Computation, Machine Learning, and Cognitive Research III – start-page: 6057 year: 2019 end-page: 6063 ident: bib229 article-title: K-margin-based residual-convolution-recurrent neural network for atrial fibrillation detection publication-title: IJCAI – start-page: 1 year: 2017 end-page: 4 ident: bib43 article-title: Atrial fibrillation detection using feature based algorithm and deep convolutional neural network publication-title: 2017 Computing in Cardiology (CinC) – volume: 126 start-page: 78 year: 2019 end-page: 85 ident: bib86 article-title: Deep-ecg: convolutional neural networks for ecg biometric recognition publication-title: Pattern Recogn. Lett. – volume: 64 start-page: 2196 year: 2016 end-page: 2205 ident: bib44 article-title: Semi-supervised stacked label consistent autoencoder for reconstruction and analysis of biomedical signals publication-title: IEEE (Inst. Electr. Electron. Eng.) Trans. Biomed. Eng. – volume: 37 start-page: 2214 year: 2016 ident: bib199 article-title: A stacked contractive denoising auto-encoder for ecg signal denoising publication-title: Physiol. Meas. – start-page: 2571 year: 2018 end-page: 2574 ident: bib147 article-title: A generative modeling approach to limited channel ecg classification publication-title: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) – volume: 140 year: 2019 ident: bib11 article-title: A deep learning model to predict outcome after thoracoscopic surgery for atrial fibrillation using single beat electrocardiographic samples publication-title: Circulation – start-page: 2555 year: 2018 end-page: 2558 ident: bib197 article-title: Bidirectional recurrent neural network and convolutional neural network (bircnn) for ecg beat classification publication-title: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) – volume: 96 start-page: 189 year: 2018 end-page: 202 ident: bib209 article-title: A novel wavelet sequence based on deep bidirectional lstm network model for ecg signal classification publication-title: Comput. Biol. Med. – start-page: 1284 year: 2019 end-page: 1288 ident: bib138 article-title: An ensemble of deep recurrent neural networks for p-wave detection in electrocardiogram publication-title: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) – start-page: 1908 year: 2019 end-page: 1912 ident: bib190 article-title: A deep learning method to detect atrial fibrillation based on continuous wavelet transform publication-title: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) – start-page: 1 year: 2017 end-page: 4 ident: bib231 article-title: Convolutional recurrent neural networks for electrocardiogram classification publication-title: 2017 Computing in Cardiology (CinC) – volume: 7 start-page: 118739 year: 2019 end-page: 118748 ident: bib202 article-title: A parallel gru recurrent network model and its application to multi-channel time-varying signal classification publication-title: IEEE Access – volume: vol. 1 year: 2016 ident: bib70 article-title: Harnessing deep neural networks with logic rules publication-title: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics – year: 2019 ident: bib31 article-title: A novel deep arrhythmia-diagnosis network for atrial fibrillation classification using electrocardiogram signals publication-title: IEEE Access – volume: 12 year: 2019 ident: bib175 article-title: Automated and interpretable patient ecg profiles for disease detection, tracking, and discovery publication-title: Circulation: Cardiovascular Quality and Outcomes – volume: 39 year: 2018 ident: bib118 article-title: Sleep-wake classification via quantifying heart rate variability by convolutional neural network publication-title: Physiol. Meas. – volume: 57 start-page: S70 year: 2019 end-page: S74 ident: bib136 article-title: Cardiac arrhythmia detection using deep learning: a review publication-title: J. Electrocardiol. – volume: 24 start-page: 1321 year: 2019 end-page: 1332 ident: bib130 article-title: Inter-patient ecg classification with symbolic representations and multi-perspective convolutional neural networks publication-title: IEEE Journal of Biomedical and Health Informatics – volume: 136 year: 2017 ident: bib174 article-title: Cardiovascular risk stratification using off-the-shelf wearables and a multi-task deep learning algorithm publication-title: Circulation – volume: 51 start-page: S18 year: 2018 end-page: S21 ident: bib154 article-title: Densely connected convolutional networks for detection of atrial fibrillation from short single-lead ecg recordings publication-title: J. Electrocardiol. – volume: 40 start-page: 385 year: 2007 end-page: 390 ident: bib161 article-title: Errors in the computerized electrocardiogram interpretation of cardiac rhythm publication-title: J. Electrocardiol. – volume: 67 start-page: 1505 year: 2019 end-page: 1516 ident: bib52 article-title: Sequential factorized autoencoder for localizing the origin of ventricular activation from 12-lead electrocardiograms publication-title: IEEE (Inst. Electr. Electron. Eng.) Trans. Biomed. Eng. – volume: 39 year: 2018 ident: bib226 article-title: A deep learning approach for fetal qrs complex detection publication-title: Physiol. Meas. – volume: 140 start-page: 93 year: 2017 end-page: 110 ident: bib213 article-title: Recognition of emotions using multimodal physiological signals and an ensemble deep learning model publication-title: Comput. Methods Progr. Biomed. – start-page: 1 year: 2018 end-page: 4 ident: bib42 article-title: You snooze, you win: the physionet/computing in cardiology challenge 2018 publication-title: 2018 Computing in Cardiology Conference (CinC) – volume: 58 start-page: 105 year: 2020 end-page: 112 ident: bib104 article-title: Heartbeat classification using deep residual convolutional neural network from 2-lead electrocardiogram publication-title: J. Electrocardiol. – volume: 5 start-page: 11805 year: 2017 end-page: 11816 ident: bib219 article-title: Heartid: a multiresolution convolutional neural network for ecg-based biometric human identification in smart health applications publication-title: Ieee Access – start-page: 4262 year: 2019 end-page: 4265 ident: bib170 article-title: Cardiovascular disease diagnosis using cross-domain transfer learning publication-title: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) – volume: 24 start-page: 1265 year: 2019 end-page: 1275 ident: bib90 article-title: Synthesis of electrocardiogram v lead signals from limb lead measurement using r peak aligned generative adversarial network publication-title: IEEE Journal of Biomedical and Health Informatics – volume: 9 start-page: 2579 year: 2008 end-page: 2605 ident: bib115 article-title: Visualizing data using t-sne publication-title: J. Mach. Learn. Res. – volume: 57 start-page: S65 year: 2019 ident: bib16 article-title: Deep learning to automatically interpret images of the electrocardiogram: do we need the raw samples? publication-title: J. Electrocardiol. – year: 2019 ident: bib178 article-title: Fetal electrocardiography and deep learning for prenatal detection of congenital heart disease publication-title: 2019 Computing in Cardiology (CinC), – start-page: 557 year: 2019 end-page: 564 ident: bib45 article-title: Pgans: personalized generative adversarial networks for ecg synthesis to improve patient-specific deep ecg classification publication-title: Proceedings of the AAAI Conference on Artificial Intelligence – volume: 8 start-page: 17196 year: 2018 ident: bib128 article-title: Deep feature learning for sudden cardiac arrest detection in automated external defibrillators publication-title: Sci. Rep. – start-page: 1 year: 2018 end-page: 4 ident: bib135 article-title: Electrocardiogram monitoring and interpretation: from traditional machine learning to deep learning, and their combination publication-title: 2018 Computing in Cardiology Conference (CinC) – volume: 7 start-page: 42710 year: 2019 end-page: 42717 ident: bib61 article-title: Real-time detection of acute cognitive stress using a convolutional neural network from electrocardiographic signal publication-title: IEEE Access – volume: 7 start-page: 102119 year: 2019 end-page: 102135 ident: bib62 article-title: Automatic cardiac arrhythmia classification using combination of deep residual network and bidirectional lstm publication-title: IEEE Access – volume: 7 start-page: 85959 year: 2019 end-page: 85970 ident: bib188 article-title: A multi-class automatic sleep staging method based on long short-term memory network using single-lead electrocardiogram signals publication-title: IEEE Access – year: 2019 ident: bib185 article-title: Few-shot learning: a survey. CoRR abs/1904 – volume: 24 start-page: 407 year: 2019 end-page: 413 ident: bib72 article-title: Atrial fibrillation prediction with residual network using sensitivity & orthogonality constraints publication-title: IEEE Journal of Biomedical and Health Informatics – year: 2018 ident: bib12 article-title: Deepheart: semi-supervised sequence learning for cardiovascular risk prediction publication-title: Thirty-Second AAAI Conference on Artificial Intelligence – start-page: 1308 year: 2019 end-page: 1312 ident: bib125 article-title: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach publication-title: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) – volume: 39 year: 2018 ident: bib187 article-title: Ensembling convolutional and long short-term memory networks for electrocardiogram arrhythmia detection publication-title: Physiol. Meas. – year: 2019 ident: bib39 article-title: Deep convolutional encoder-decoder framework for fetal ecg signal denoising publication-title: 2019 Computing in Cardiology (CinC) – volume: 40 year: 2019 ident: bib127 article-title: Detection of atrial fibrillation and other abnormal rhythms from ecg using a multi-layer classifier architecture publication-title: Physiol. Meas. – volume: 7 start-page: 99964 year: 2019 end-page: 99977 ident: bib134 article-title: Preprocessing method for performance enhancement in cnn-based stemi detection from 12-lead ecg publication-title: IEEE Access – volume: 102 start-page: 278 year: 2018 end-page: 287 ident: bib132 article-title: Automated diagnosis of arrhythmia using combination of cnn and lstm techniques with variable length heart beats publication-title: Comput. Biol. Med. – volume: 40 start-page: 317 year: 1995 end-page: 318 ident: bib15 article-title: Nutzung der ekg-signaldatenbank cardiodat der ptb über das internet publication-title: Biomedizinische Technik/Biomedical Engineering – start-page: 1 year: 2018 end-page: 4 ident: bib146 article-title: Generalization studies of neural network models for cardiac disease detection using limited channel ecg publication-title: 2018 Computing in Cardiology Conference (CinC) – volume: 138 year: 2018 ident: bib79 article-title: A 10-rr-interval-based rhythm classifier using a deep neural network publication-title: Circulation – year: 2019 ident: bib18 article-title: Accurate detection of atrial fibrillation from 12-lead ecg using deep neural network publication-title: Comput. Biol. Med. – volume: 7 start-page: 109870 year: 2019 end-page: 109883 ident: bib99 article-title: Interpretability analysis of heartbeat classification based on heartbeat activity's global sequence features and bilstm-attention neural network publication-title: IEEE Access – volume: 70 start-page: 1183 year: 2017 end-page: 1192 ident: bib157 article-title: Computer-interpreted electrocardiograms: benefits and limitations publication-title: J. Am. Coll. Cardiol. – volume: 176 start-page: 121 year: 2019 end-page: 133 ident: bib210 article-title: A new approach for arrhythmia classification using deep coded features and lstm networks publication-title: Comput. Methods Progr. Biomed. – volume: 7 start-page: 26527 year: 2018 end-page: 26542 ident: bib54 article-title: Multimodal biometric authentication systems using convolution neural network based on different level fusion of ecg and fingerprint publication-title: IEEE Access – year: 2019 ident: bib181 article-title: Deep ensemble detection of congestive heart failure using short-term rr intervals – volume: 140 year: 2019 ident: bib160 article-title: Developing convolutional neural networks for deep learning of ventricular action potentials to predict risk for ventricular arrhythmias publication-title: Circulation – volume: 65 start-page: 1662 year: 2017 end-page: 1671 ident: bib207 article-title: Localization of origins of premature ventricular contraction by means of convolutional neural network from 12-lead ecg publication-title: IEEE (Inst. Electr. Electron. Eng.) Trans. Biomed. Eng. – start-page: 1 year: 2017 end-page: 4 ident: bib117 article-title: Atrial fibrillation classification using qrs complex features and lstm publication-title: 2017 Computing in Cardiology (CinC) – volume: 102 start-page: 327 year: 2018 end-page: 335 ident: bib37 article-title: Automated detection of atrial fibrillation using long short-term memory network with rr interval signals publication-title: Comput. Biol. Med. – volume: 7 start-page: 100910 year: 2019 end-page: 100922 ident: bib182 article-title: Ecg arrhythmias detection using auxiliary classifier generative adversarial network and residual network publication-title: IEEE Access – year: 2020 ident: bib131 article-title: Assessing and mitigating bias in medical artificial intelligence: the effects of race and ethnicity on a deep learning model for ecg analysis publication-title: Circulation: Arrhythmia and Electrophysiology – volume: 25 start-page: 70 year: 2019 ident: bib8 article-title: Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram publication-title: Nat. Med. – volume: 7 start-page: 85985 year: 2019 end-page: 85994 ident: bib63 article-title: Simultaneous human health monitoring and time-frequency sparse representation using eeg and ecg signals publication-title: IEEE Access – start-page: 1 year: 2017 end-page: 4 ident: bib67 article-title: Encase: an ensemble classifier for ecg classification using expert features and deep neural networks publication-title: 2017 Computing in Cardiology (CinC) – year: 2016 ident: bib56 article-title: Deep compression: compressing deep neural network with pruning, trained quantization and huffman coding publication-title: 4th International Conference on Learning Representations – start-page: 1 year: 2017 end-page: 4 ident: bib153 article-title: Densely connected convolutional networks and signal quality analysis to detect atrial fibrillation using short single-lead ecg recordings publication-title: 2017 Computing in Cardiology (CinC) – start-page: 715 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib163 article-title: Detection of paroxysmal atrial fibrillation using attention-based bidirectional recurrent neural networks – start-page: 234 year: 2015 ident: 10.1016/j.compbiomed.2020.103801_bib152 article-title: U-net: convolutional networks for biomedical image segmentation – start-page: 1 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib224 article-title: Pvc recognition for wearable ecgs using modified frequency slice wavelet transform and convolutional neural network – year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib12 article-title: Deepheart: semi-supervised sequence learning for cardiovascular risk prediction – volume: 40 start-page: 317 year: 1995 ident: 10.1016/j.compbiomed.2020.103801_bib15 article-title: Nutzung der ekg-signaldatenbank cardiodat der ptb über das internet publication-title: Biomedizinische Technik/Biomedical Engineering – volume: 138 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib116 article-title: Artificial intelligence detects pediatric heart murmurs with cardiologist-level accuracy publication-title: Circulation – volume: 349 start-page: 212 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib180 article-title: Adversarial de-noising of electrocardiogram publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.03.083 – volume: 14 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib49 article-title: Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients publication-title: PloS One doi: 10.1371/journal.pone.0210103 – volume: 39 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib176 article-title: Multiclass classification of obstructive sleep apnea/hypopnea based on a convolutional neural network from a single-lead electrocardiogram publication-title: Physiol. Meas. doi: 10.1088/1361-6579/aac7b7 – volume: 7 start-page: 57 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib156 article-title: Using deep convolutional neural network for emotion detection on a physiological signals dataset (amigos) publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2883213 – volume: 8 start-page: 35592 year: 2020 ident: 10.1016/j.compbiomed.2020.103801_bib162 article-title: Generalization of convolutional neural networks for ecg classification using generative adversarial networks publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2974712 – ident: 10.1016/j.compbiomed.2020.103801_bib139 – start-page: 1 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib20 article-title: Deep learning based qrs multilead delineator in electrocardiogram signals – volume: 7 start-page: 102119 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib62 article-title: Automatic cardiac arrhythmia classification using combination of deep residual network and bidirectional lstm publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2931500 – volume: 7 start-page: 145395 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib114 article-title: A deep bidirectional gru network model for biometric electrocardiogram classification based on recurrent neural networks publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2939947 – year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib178 article-title: Fetal electrocardiography and deep learning for prenatal detection of congenital heart disease – volume: 7 start-page: 144292 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib60 article-title: Biosignal generation and latent variable analysis with recurrent generative adversarial networks publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2934928 – volume: 105 start-page: 92 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib133 article-title: Automated beat-wise arrhythmia diagnosis using modified u-net on extended electrocardiographic recordings with heterogeneous arrhythmia types publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2018.12.012 – volume: 9 start-page: 1390 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib14 article-title: Physiozoo: a novel open access platform for heart rate variability analysis of mammalian electrocardiographic data publication-title: Front. Physiol. doi: 10.3389/fphys.2018.01390 – volume: 39 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib173 article-title: Abductive reasoning as a basis to reproduce expert criteria in ecg atrial fibrillation identification publication-title: Physiol. Meas. doi: 10.1088/1361-6579/aad7e4 – volume: 7 start-page: 118739 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib202 article-title: A parallel gru recurrent network model and its application to multi-channel time-varying signal classification publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2936516 – start-page: 2672 year: 2014 ident: 10.1016/j.compbiomed.2020.103801_bib48 article-title: Generative adversarial nets – year: 2020 ident: 10.1016/j.compbiomed.2020.103801_bib40 – volume: 8 start-page: 1368 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib109 article-title: An open access database for evaluating the algorithms of electrocardiogram rhythm and morphology abnormality detection publication-title: Journal of Medical Imaging and Health Informatics doi: 10.1166/jmihi.2018.2442 – volume: 39 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib187 article-title: Ensembling convolutional and long short-term memory networks for electrocardiogram arrhythmia detection publication-title: Physiol. Meas. doi: 10.1088/1361-6579/aad386 – volume: 8 start-page: 17196 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib128 article-title: Deep feature learning for sudden cardiac arrest detection in automated external defibrillators publication-title: Sci. Rep. doi: 10.1038/s41598-018-33424-9 – year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib17 – start-page: 1 year: 2017 ident: 10.1016/j.compbiomed.2020.103801_bib30 article-title: Af Classification from a Short Single Lead Ecg Recording: the Physionet/computing in Cardiology Challenge 2017 – volume: 10 start-page: 1 year: 2020 ident: 10.1016/j.compbiomed.2020.103801_bib142 article-title: Precision medicine and artificial intelligence: a pilot study on deep learning for hypoglycemic events detection based on ecg publication-title: Sci. Rep. doi: 10.1038/s41598-019-56927-5 – volume: 89 start-page: 389 year: 2017 ident: 10.1016/j.compbiomed.2020.103801_bib1 article-title: A deep convolutional neural network model to classify heartbeats publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2017.08.022 – start-page: 336 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib26 article-title: Finger ecg based two-phase authentication using 1d convolutional neural networks – volume: 1 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib75 article-title: A novel multi-module neural network system for imbalanced heartbeats classification publication-title: Expert Syst. Appl. X – volume: 10 start-page: 1 year: 2020 ident: 10.1016/j.compbiomed.2020.103801_bib177 article-title: Transfer learning in ecg classification from human to horse using a novel parallel neural network architecture publication-title: Sci. Rep. doi: 10.1038/s41598-019-57025-2 – volume: 24 start-page: 1265 issue: 5 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib90 article-title: Synthesis of electrocardiogram v lead signals from limb lead measurement using r peak aligned generative adversarial network publication-title: IEEE Journal of Biomedical and Health Informatics doi: 10.1109/JBHI.2019.2936583 – volume: 140 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib145 article-title: Deep neural networks can predict 1-year mortality directly from ecg signal, even when clinically interpreted as normal publication-title: Circulation – start-page: 1 year: 2017 ident: 10.1016/j.compbiomed.2020.103801_bib5 article-title: Comparing feature-based classifiers and convolutional neural networks to detect arrhythmia from short segments of ecg – volume: 176 start-page: 121 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib210 article-title: A new approach for arrhythmia classification using deep coded features and lstm networks publication-title: Comput. Methods Progr. Biomed. doi: 10.1016/j.cmpb.2019.05.004 – volume: 7 start-page: 161152 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib220 article-title: Localization of myocardial infarction with multi-lead bidirectional gated recurrent unit neural network publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2946932 – volume: 7 start-page: 85985 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib63 article-title: Simultaneous human health monitoring and time-frequency sparse representation using eeg and ecg signals publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2921568 – year: 2020 ident: 10.1016/j.compbiomed.2020.103801_bib84 – volume: 58 start-page: 105 year: 2020 ident: 10.1016/j.compbiomed.2020.103801_bib104 article-title: Heartbeat classification using deep residual convolutional neural network from 2-lead electrocardiogram publication-title: J. Electrocardiol. doi: 10.1016/j.jelectrocard.2019.11.046 – volume: 40 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib69 article-title: Combining deep neural networks and engineered features for cardiac arrhythmia detection from ecg recordings publication-title: Physiol. Meas. doi: 10.1088/1361-6579/ab15a2 – volume: 70 start-page: 1183 year: 2017 ident: 10.1016/j.compbiomed.2020.103801_bib157 article-title: Computer-interpreted electrocardiograms: benefits and limitations publication-title: J. Am. Coll. Cardiol. doi: 10.1016/j.jacc.2017.07.723 – volume: 7 start-page: 93275 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib53 article-title: Impact of ecg dataset diversity on generalization of cnn model for detecting qrs complex publication-title: IEEE access doi: 10.1109/ACCESS.2019.2927726 – volume: 96 start-page: 189 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib209 article-title: A novel wavelet sequence based on deep bidirectional lstm network model for ecg signal classification publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2018.03.016 – volume: 102 start-page: 411 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib211 article-title: Arrhythmia detection using deep convolutional neural network with long duration ecg signals publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2018.09.009 – volume: 57 start-page: S61 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib121 article-title: Machine learning in the electrocardiogram publication-title: J. Electrocardiol. doi: 10.1016/j.jelectrocard.2019.08.008 – year: 2020 ident: 10.1016/j.compbiomed.2020.103801_bib131 article-title: Assessing and mitigating bias in medical artificial intelligence: the effects of race and ethnicity on a deep learning model for ecg analysis – year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib94 – volume: 7 start-page: 42710 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib61 article-title: Real-time detection of acute cognitive stress using a convolutional neural network from electrocardiographic signal publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2907076 – volume: 37 start-page: 2214 year: 2016 ident: 10.1016/j.compbiomed.2020.103801_bib199 article-title: A stacked contractive denoising auto-encoder for ecg signal denoising publication-title: Physiol. Meas. doi: 10.1088/0967-3334/37/12/2214 – volume: 138 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib79 article-title: A 10-rr-interval-based rhythm classifier using a deep neural network publication-title: Circulation – start-page: 1 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib34 article-title: Deep learning for pulse detection in out-of-hospital cardiac arrest using the ecg – volume: 9 start-page: 2579 year: 2008 ident: 10.1016/j.compbiomed.2020.103801_bib115 article-title: Visualizing data using t-sne publication-title: J. Mach. Learn. Res. – volume: 7 start-page: 182225 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib2 article-title: Dense convolutional networks with focal loss and image generation for electrocardiogram classification publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2960116 – volume: 65 start-page: 1662 year: 2017 ident: 10.1016/j.compbiomed.2020.103801_bib207 article-title: Localization of origins of premature ventricular contraction by means of convolutional neural network from 12-lead ecg publication-title: IEEE (Inst. Electr. Electron. Eng.) Trans. Biomed. Eng. – volume: 99 start-page: 124 year: 2010 ident: 10.1016/j.compbiomed.2020.103801_bib3 article-title: Optimal parameters study for sample entropy-based atrial fibrillation organization analysis publication-title: Comput. Methods Progr. Biomed. doi: 10.1016/j.cmpb.2010.02.009 – volume: 23 start-page: 2375 issue: 6 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib91 article-title: Bidirectional recurrent auto-encoder for photoplethysmogram denoising publication-title: IEEE Journal of Biomedical and Health Informatics doi: 10.1109/JBHI.2018.2885139 – volume: 51 start-page: S78 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib195 article-title: Monitoring significant st changes through deep learning publication-title: J. Electrocardiol. doi: 10.1016/j.jelectrocard.2018.07.026 – volume: 122 start-page: 75 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib159 article-title: A robust deep convolutional neural network with batch-weighted loss for heartbeat classification publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2018.12.037 – volume: 55 start-page: 680 year: 2008 ident: 10.1016/j.compbiomed.2020.103801_bib106 article-title: Frequency-domain features for ecg beat discrimination using grey relational analysis-based classifier publication-title: Comput. Math. Appl. doi: 10.1016/j.camwa.2007.04.035 – start-page: 2633 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib65 article-title: Ecg biometric recognition: template-free approaches based on deep learning – volume: 106 start-page: 232 year: 2006 ident: 10.1016/j.compbiomed.2020.103801_bib50 article-title: Common errors in computer electrocardiogram interpretation publication-title: Int. J. Cardiol. doi: 10.1016/j.ijcard.2005.02.007 – volume: 14 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib85 article-title: Artificial intelligence algorithm for predicting mortality of patients with acute heart failure publication-title: PloS One doi: 10.1371/journal.pone.0219302 – volume: 138 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib7 article-title: Electrocardiographic screening for atrial fibrillation while in sinus rhythm using deep learning publication-title: Circulation – volume: 40 start-page: 385 year: 2007 ident: 10.1016/j.compbiomed.2020.103801_bib161 article-title: Errors in the computerized electrocardiogram interpretation of cardiac rhythm publication-title: J. Electrocardiol. doi: 10.1016/j.jelectrocard.2007.03.008 – volume: 7 start-page: 9270 year: 2017 ident: 10.1016/j.compbiomed.2020.103801_bib81 article-title: Personalized monitoring and advance warning system for cardiac arrhythmias publication-title: Sci. Rep. doi: 10.1038/s41598-017-09544-z – start-page: 1 year: 2017 ident: 10.1016/j.compbiomed.2020.103801_bib231 article-title: Convolutional recurrent neural networks for electrocardiogram classification – volume: 99 start-page: 53 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib119 article-title: A novel application of deep learning for single-lead ecg classification publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2018.05.013 – year: 2001 ident: 10.1016/j.compbiomed.2020.103801_bib151 article-title: Ecg frequency domain features extraction: a new characteristic for arrhythmias classification – start-page: 1 year: 2017 ident: 10.1016/j.compbiomed.2020.103801_bib153 article-title: Densely connected convolutional networks and signal quality analysis to detect atrial fibrillation using short single-lead ecg recordings – volume: 24 start-page: 515 issue: 2 year: 2020 ident: 10.1016/j.compbiomed.2020.103801_bib155 article-title: Lstm-based ecg classification for continuous monitoring on personal wearable devices publication-title: IEEE Journal of Biomedical and Health Informatics doi: 10.1109/JBHI.2019.2911367 – start-page: 4433 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib227 article-title: Beatgan: anomalous rhythm detection using adversarially generated time series – volume: 140 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib160 article-title: Developing convolutional neural networks for deep learning of ventricular action potentials to predict risk for ventricular arrhythmias publication-title: Circulation – volume: 7 start-page: 100910 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib182 article-title: Ecg arrhythmias detection using auxiliary classifier generative adversarial network and residual network publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2930882 – start-page: 2565 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib205 article-title: Raim: recurrent attentive and intensive model of multimodal patient monitoring data – volume: 7 start-page: 26527 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib54 article-title: Multimodal biometric authentication systems using convolution neural network based on different level fusion of ecg and fingerprint publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2886573 – start-page: 1 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib73 article-title: Detection of first-degree atrioventricular block on variable-length electrocardiogram via a multimodal deep learning method – volume: 51 start-page: S18 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib154 article-title: Densely connected convolutional networks for detection of atrial fibrillation from short single-lead ecg recordings publication-title: J. Electrocardiol. doi: 10.1016/j.jelectrocard.2018.08.008 – volume: 57 start-page: S70 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib136 article-title: Cardiac arrhythmia detection using deep learning: a review publication-title: J. Electrocardiol. doi: 10.1016/j.jelectrocard.2019.08.004 – volume: 7 start-page: 130074 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib88 article-title: An automatic system for real-time identifying atrial fibrillation by using a lightweight convolutional neural network publication-title: IEEE access doi: 10.1109/ACCESS.2019.2939822 – start-page: 1 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib146 article-title: Generalization studies of neural network models for cardiac disease detection using limited channel ecg – start-page: 3504 year: 2016 ident: 10.1016/j.compbiomed.2020.103801_bib28 article-title: RETAIN: an interpretable predictive model for healthcare using reverse time attention mechanism – volume: 125 start-page: 668 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib221 article-title: Ecg-based personal recognition using a convolutional neural network publication-title: Pattern Recogn. Lett. doi: 10.1016/j.patrec.2019.07.009 – start-page: 2551 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib228 article-title: Premature ventricular contraction detection from ambulatory ecg using recurrent neural networks – year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib31 article-title: A novel deep arrhythmia-diagnosis network for atrial fibrillation classification using electrocardiogram signals – volume: 15 year: 2020 ident: 10.1016/j.compbiomed.2020.103801_bib126 article-title: Single-modal and multi-modal false arrhythmia alarm reduction using attention-based convolutional and recurrent neural networks publication-title: PloS One doi: 10.1371/journal.pone.0226990 – volume: 48 start-page: 2095 year: 2017 ident: 10.1016/j.compbiomed.2020.103801_bib143 article-title: Deep convolutional neural networks and learning ecg features for screening paroxysmal atrial fibrillation patients publication-title: IEEE Transactions on Systems, Man, and Cybernetics: Systems doi: 10.1109/TSMC.2017.2705582 – start-page: 1909 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib164 article-title: Ambulatory atrial fibrillation monitoring using wearable photoplethysmography with deep learning – volume: 115 start-page: 465 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib4 article-title: A deep learning approach for real-time detection of atrial fibrillation publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2018.08.011 – ident: 10.1016/j.compbiomed.2020.103801_bib64 – volume: 22 start-page: 1434 year: 2017 ident: 10.1016/j.compbiomed.2020.103801_bib113 article-title: Real-time multilead convolutional neural network for myocardial infarction detection publication-title: IEEE journal of biomedical and health informatics doi: 10.1109/JBHI.2017.2771768 – volume: 57 start-page: S65 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib16 article-title: Deep learning to automatically interpret images of the electrocardiogram: do we need the raw samples? publication-title: J. Electrocardiol. doi: 10.1016/j.jelectrocard.2019.09.018 – volume: 5 start-page: 11805 year: 2017 ident: 10.1016/j.compbiomed.2020.103801_bib219 article-title: Heartid: a multiresolution convolutional neural network for ecg-based biometric human identification in smart health applications publication-title: Ieee Access doi: 10.1109/ACCESS.2017.2707460 – volume: 94 start-page: 19 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib171 article-title: Application of stacked convolutional and long short-term memory network for accurate identification of cad ecg signals publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2017.12.023 – year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib189 – volume: 136 year: 2017 ident: 10.1016/j.compbiomed.2020.103801_bib174 article-title: Cardiovascular risk stratification using off-the-shelf wearables and a multi-task deep learning algorithm publication-title: Circulation doi: 10.1161/circ.136.suppl_1.21042 – volume: 7 start-page: 109870 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib99 article-title: Interpretability analysis of heartbeat classification based on heartbeat activity's global sequence features and bilstm-attention neural network publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2933473 – year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib181 – volume: 7 start-page: 48392 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib92 article-title: Personal identification using a robust eigen ecg network based on time-frequency representations of ecg signals publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2904095 – volume: 52 start-page: 88 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib166 article-title: A deep neural network learning algorithm outperforms a conventional algorithm for emergency department electrocardiogram interpretation publication-title: J. Electrocardiol. doi: 10.1016/j.jelectrocard.2018.11.013 – volume: 39 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib226 article-title: A deep learning approach for fetal qrs complex detection publication-title: Physiol. Meas. doi: 10.1088/1361-6579/aab297 – volume: 24 start-page: 1321 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib130 article-title: Inter-patient ecg classification with symbolic representations and multi-perspective convolutional neural networks publication-title: IEEE Journal of Biomedical and Health Informatics doi: 10.1109/JBHI.2019.2942938 – start-page: 1308 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib125 article-title: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach – volume: 39 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib118 article-title: Sleep-wake classification via quantifying heart rate variability by convolutional neural network publication-title: Physiol. Meas. doi: 10.1088/1361-6579/aad5a9 – volume: 39 start-page: 104005 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib167 article-title: A convolutional neural network for ecg annotation as the basis for classification of cardiac rhythms publication-title: Physiol. Meas. doi: 10.1088/1361-6579/aae304 – volume: 24 start-page: 5017 year: 2015 ident: 10.1016/j.compbiomed.2020.103801_bib22 article-title: Pcanet: a simple deep learning baseline for image classification? publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2015.2475625 – volume: 7 start-page: 89152 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib21 article-title: Atrial fibrillation detection using an improved multi-scale decomposition enhanced residual convolutional neural network publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2926749 – volume: 294 start-page: 94 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib97 article-title: A method to detect sleep apnea based on deep neural network and hidden markov model using single-lead ecg signal publication-title: Neurocomputing doi: 10.1016/j.neucom.2018.03.011 – volume: 25 start-page: 65 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib57 article-title: Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network publication-title: Nat. Med. doi: 10.1038/s41591-018-0268-3 – start-page: 2559 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib25 article-title: Region aggregation network: improving convolutional neural network for ecg characteristic detection – year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib39 article-title: Deep convolutional encoder-decoder framework for fetal ecg signal denoising – start-page: 4262 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib170 article-title: Cardiovascular disease diagnosis using cross-domain transfer learning – volume: 140 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib11 article-title: A deep learning model to predict outcome after thoracoscopic surgery for atrial fibrillation using single beat electrocardiographic samples publication-title: Circulation – volume: 13 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib9 article-title: Noninvasive assessment of dofetilide plasma concentration using a deep learning (neural network) analysis of the surface electrocardiogram: a proof of concept study publication-title: PloS One doi: 10.1371/journal.pone.0201059 – start-page: 1 year: 2017 ident: 10.1016/j.compbiomed.2020.103801_bib43 article-title: Atrial fibrillation detection using feature based algorithm and deep convolutional neural network – volume: 6 start-page: 27465 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib216 article-title: Automated ecg classification using dual heartbeat coupling based on convolutional neural network publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2833841 – year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib169 – volume: 126 start-page: 78 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib86 article-title: Deep-ecg: convolutional neural networks for ecg biometric recognition publication-title: Pattern Recogn. Lett. doi: 10.1016/j.patrec.2018.03.028 – start-page: 1500 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib222 article-title: Deep learning based patient-specific classification of arrhythmia on ecg signal – start-page: 1303 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib110 article-title: A lstm and cnn based assemble neural network framework for arrhythmias classification – volume: 93 start-page: 84 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib192 article-title: Detecting atrial fibrillation by deep convolutional neural networks publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2017.12.007 – volume: 20 start-page: 45 year: 2001 ident: 10.1016/j.compbiomed.2020.103801_bib123 article-title: The impact of the mit-bih arrhythmia database publication-title: IEEE Eng. Med. Biol. Mag. doi: 10.1109/51.932724 – volume: 7 start-page: 153751 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib198 article-title: Feature enrichment based convolutional neural network for heartbeat classification from electrocardiogram publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2948857 – volume: 102 start-page: 168 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib225 article-title: Ecg authentication system design incorporating a convolutional neural network and generalized s-transformation publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2018.09.027 – start-page: 246 year: 2020 ident: 10.1016/j.compbiomed.2020.103801_bib124 article-title: Deep learning for ecg segmentation – start-page: 1 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib42 article-title: You snooze, you win: the physionet/computing in cardiology challenge 2018 – volume: 391 start-page: 83 year: 2020 ident: 10.1016/j.compbiomed.2020.103801_bib101 article-title: Toward improving ecg biometric identification using cascaded convolutional neural networks publication-title: Neurocomputing doi: 10.1016/j.neucom.2020.01.019 – volume: 39 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib141 article-title: Parallel use of a convolutional neural network and bagged tree ensemble for the classification of holter ecg publication-title: Physiol. Meas. doi: 10.1088/1361-6579/aad9ee – volume: 7 start-page: 76295 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib95 article-title: Automated heartbeat classification using 3-d inputs based on convolutional neural network with multi-fields of view publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2921991 – start-page: 82 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib120 article-title: A comparison of patient history-and ekg-based cardiac risk scores publication-title: AMIA Summits on Translational Science Proceedings – volume: 185 year: 2020 ident: 10.1016/j.compbiomed.2020.103801_bib55 article-title: Ml–resnet: a novel network to detect and locate myocardial infarction using 12 leads ecg publication-title: Comput. Methods Progr. Biomed. doi: 10.1016/j.cmpb.2019.105138 – start-page: 1 year: 2017 ident: 10.1016/j.compbiomed.2020.103801_bib51 article-title: Automatic coordinate prediction of the exit of ventricular tachycardia from 12-lead electrocardiogram – volume: 7 start-page: 50431 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib38 article-title: A probabilistic process neural network and its application in ecg classification publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2910880 – start-page: 248 year: 2009 ident: 10.1016/j.compbiomed.2020.103801_bib32 article-title: Imagenet: a large-scale hierarchical image database – volume: 25 start-page: 70 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib8 article-title: Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram publication-title: Nat. Med. doi: 10.1038/s41591-018-0240-2 – start-page: 1 year: 2017 ident: 10.1016/j.compbiomed.2020.103801_bib158 article-title: Beat by beat: classifying cardiac arrhythmias with recurrent neural networks – volume: 64 start-page: 2196 year: 2016 ident: 10.1016/j.compbiomed.2020.103801_bib44 article-title: Semi-supervised stacked label consistent autoencoder for reconstruction and analysis of biomedical signals publication-title: IEEE (Inst. Electr. Electron. Eng.) Trans. Biomed. Eng. – volume: 138 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib148 article-title: Automatic driver stress level classification using multimodal deep learning publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2019.07.010 – volume: 184 year: 2020 ident: 10.1016/j.compbiomed.2020.103801_bib59 article-title: Multi-branch fusion network for myocardial infarction screening from 12-lead ecg images publication-title: Comput. Methods Progr. Biomed. doi: 10.1016/j.cmpb.2019.105286 – ident: 10.1016/j.compbiomed.2020.103801_bib185 – start-page: 1135 year: 2016 ident: 10.1016/j.compbiomed.2020.103801_bib149 article-title: why should I trust you?”: explaining the predictions of any classifier – volume: 6 start-page: 39734 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib102 article-title: Combining convolutional neural network and distance distribution matrix for identification of congestive heart failure publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2855420 – volume: vol. 1 year: 2016 ident: 10.1016/j.compbiomed.2020.103801_bib70 article-title: Harnessing deep neural networks with logic rules – start-page: 1284 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib138 article-title: An ensemble of deep recurrent neural networks for p-wave detection in electrocardiogram – year: 2020 ident: 10.1016/j.compbiomed.2020.103801_bib206 – volume: 67 start-page: 1505 issue: 5 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib52 article-title: Sequential factorized autoencoder for localizing the origin of ventricular activation from 12-lead electrocardiograms publication-title: IEEE (Inst. Electr. Electron. Eng.) Trans. Biomed. Eng. – volume: 7 start-page: 60806 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib27 article-title: Noise reduction in ecg signals using fully convolutional denoising autoencoders publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2912036 – volume: 7 start-page: 7989 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib193 article-title: A novel wearable electrocardiogram classification system using convolutional neural networks and active learning publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2890865 – volume: 39 start-page: 664 year: 2001 ident: 10.1016/j.compbiomed.2020.103801_bib172 article-title: Automatic detection of atrial fibrillation using the coefficient of variation and density histograms of rr and δrr intervals publication-title: Med. Biol. Eng. Comput. doi: 10.1007/BF02345439 – volume: 101 start-page: e215 year: 2000 ident: 10.1016/j.compbiomed.2020.103801_bib46 article-title: Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals publication-title: Circulation doi: 10.1161/01.CIR.101.23.e215 – volume: 7 start-page: 92871 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib71 article-title: Ecg arrhythmia classification using stft-based spectrogram and convolutional neural network publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2928017 – volume: 122 start-page: 23 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib13 article-title: Classification of myocardial infarction with multi-lead ecg signals and deep cnn publication-title: Pattern Recogn. Lett. doi: 10.1016/j.patrec.2019.02.016 – volume: 134 start-page: 66 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib93 article-title: Qrs detection method based on fully convolutional networks for capacitive electrocardiogram publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2019.05.033 – volume: 102 start-page: 327 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib37 article-title: Automated detection of atrial fibrillation using long short-term memory network with rr interval signals publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2018.07.001 – volume: 136 year: 2017 ident: 10.1016/j.compbiomed.2020.103801_bib10 article-title: Annotating ecg signals with deep neural networks publication-title: Circulation – start-page: 1527 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib150 article-title: Anchors: high-precision model-agnostic explanations – start-page: 2555 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib197 article-title: Bidirectional recurrent neural network and convolutional neural network (bircnn) for ecg beat classification – year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib77 – volume: 39 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib78 article-title: A robust deep convolutional neural network for the classification of abnormal cardiac rhythm using single lead electrocardiograms of variable length publication-title: Physiol. Meas. doi: 10.1088/1361-6579/aaaa9d – year: 2016 ident: 10.1016/j.compbiomed.2020.103801_bib33 – year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib76 article-title: U-net architecture for the automatic detection and delineation of the electrocardiogram – start-page: 1 year: 2017 ident: 10.1016/j.compbiomed.2020.103801_bib186 article-title: Cardiac arrhythmia detection from ecg combining convolutional and long short-term memory networks – volume: 7 start-page: 169359 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib215 article-title: Inter-patient cnn-lstm for qrs complex detection in noisy ecg signals publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2955738 – start-page: 2608 year: 2015 ident: 10.1016/j.compbiomed.2020.103801_bib82 article-title: Convolutional neural networks for patient-specific ecg classification – volume: 39 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib137 article-title: Analyzing single-lead short ecg recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation publication-title: Physiol. Meas. doi: 10.1088/1361-6579/aad5bd – start-page: 1 year: 2017 ident: 10.1016/j.compbiomed.2020.103801_bib105 article-title: Atrial fibrillation detection and ecg classification based on convolutional recurrent neural network – start-page: 1 year: 2017 ident: 10.1016/j.compbiomed.2020.103801_bib201 article-title: Robust ecg signal classification for detection of atrial fibrillation using a novel neural network – start-page: 1 year: 2017 ident: 10.1016/j.compbiomed.2020.103801_bib67 article-title: Encase: an ensemble classifier for ecg classification using expert features and deep neural networks – start-page: 673 year: 1997 ident: 10.1016/j.compbiomed.2020.103801_bib87 article-title: A database for evaluation of algorithms for measurement of qt and other waveform intervals in the ecg – volume: 314 start-page: 336 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib100 article-title: Patient-specific ecg classification by deeper cnn from generic to dedicated publication-title: Neurocomputing doi: 10.1016/j.neucom.2018.06.068 – volume: 12 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib6 article-title: Age and sex estimation using artificial intelligence from standard 12-lead ecgs publication-title: Circulation: Arrhythmia and Electrophysiology – start-page: 227 year: 1983 ident: 10.1016/j.compbiomed.2020.103801_bib122 article-title: A new method for detecting atrial fibrillation using rr intervals publication-title: Comput. Cardiol. – year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib184 article-title: Pay attention and watch temporal correlation: a novel 1-d convolutional neural network for ecg record classification – volume: 7 start-page: 51598 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib29 article-title: Ecg authentication method based on parallel multi-scale one-dimensional residual network with center and margin loss publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2912519 – volume: 7 start-page: 77849 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib103 article-title: Classification of atrial fibrillation recurrence based on a convolution neural network with svm architecture publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2920900 – volume: 18 start-page: 2074 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib165 article-title: A review of emotion recognition using physiological signals publication-title: Sensors doi: 10.3390/s18072074 – volume: 7 start-page: 159369 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib208 article-title: Ecg generation with sequence generative adversarial nets optimized by policy gradient publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2950383 – start-page: 5642 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib58 article-title: Spectro-temporal feature based multi-channel convolutional neural network for ecg beat classification – volume: 7 start-page: 85959 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib188 article-title: A multi-class automatic sleep staging method based on long short-term memory network using single-lead electrocardiogram signals publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2924980 – start-page: 1913 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib74 article-title: An electrocardiogram delineator via deep segmentation network – volume: 24 start-page: 503 issue: 2 year: 2020 ident: 10.1016/j.compbiomed.2020.103801_bib112 article-title: Mfb-cbrnn: a hybrid network for mi detection using 12-lead ecgs publication-title: IEEE Journal of Biomedical and Health Informatics doi: 10.1109/JBHI.2019.2910082 – start-page: 1 year: 2017 ident: 10.1016/j.compbiomed.2020.103801_bib117 article-title: Atrial fibrillation classification using qrs complex features and lstm – volume: 180 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib35 article-title: Deep learning approaches for automatic detection of sleep apnea events from an electrocardiogram publication-title: Comput. Methods Progr. Biomed. doi: 10.1016/j.cmpb.2019.105001 – start-page: 2296 year: 2017 ident: 10.1016/j.compbiomed.2020.103801_bib144 article-title: Elimination of power line interference from ecg signals using recurrent neural networks – volume: 6 start-page: 16529 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib194 article-title: An automatic cardiac arrhythmia classification system with wearable electrocardiogram publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2807700 – volume: 9 start-page: 6734 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib230 article-title: Electrocardiogram generation with a bidirectional lstm-cnn generative adversarial network publication-title: Sci. Rep. doi: 10.1038/s41598-019-42516-z – volume: 39 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib200 article-title: Ecg signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural network publication-title: Physiol. Meas. doi: 10.1088/1361-6579/aad9ed – start-page: 2571 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib147 article-title: A generative modeling approach to limited channel ecg classification – start-page: 256 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib196 article-title: A deep learning approach to examine ischemic st changes in ambulatory ecg recordings publication-title: AMIA Summits on Translational Science Proceedings – year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib218 – year: 1996 ident: 10.1016/j.compbiomed.2020.103801_bib19 – start-page: 5888 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib68 article-title: Mina: multilevel knowledge-guided attention for modeling electrocardiography signals – volume: 66 start-page: 218 year: 1982 ident: 10.1016/j.compbiomed.2020.103801_bib129 article-title: Sudden death recorded during holter monitoring publication-title: Circulation doi: 10.1161/01.CIR.66.1.218 – start-page: 4636 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib214 article-title: Qrs detection and measurement method of ecg paper based on convolutional neural networks – volume: 63 start-page: 664 year: 2015 ident: 10.1016/j.compbiomed.2020.103801_bib80 article-title: Real-time patient-specific ecg classification by 1-d convolutional neural networks publication-title: IEEE (Inst. Electr. Electron. Eng.) Trans. Biomed. Eng. – volume: 109 start-page: 14 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib24 article-title: Ecg anomaly class identification using lstm and error profile modeling publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2019.04.009 – volume: 114 start-page: 54 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib47 article-title: Ecg classification using three-level fusion of different feature descriptors publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2018.07.030 – year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib18 article-title: Accurate detection of atrial fibrillation from 12-lead ecg using deep neural network publication-title: Comput. Biol. Med. – volume: 14 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib140 article-title: Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia publication-title: PloS One doi: 10.1371/journal.pone.0216756 – start-page: 1 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib135 article-title: Electrocardiogram monitoring and interpretation: from traditional machine learning to deep learning, and their combination – start-page: 1 year: 2017 ident: 10.1016/j.compbiomed.2020.103801_bib191 article-title: Atrial fibrillation detection using stationary wavelet transform and deep learning – start-page: 6057 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib229 article-title: K-margin-based residual-convolution-recurrent neural network for atrial fibrillation detection publication-title: IJCAI – volume: 24 start-page: 717 issue: 3 year: 2020 ident: 10.1016/j.compbiomed.2020.103801_bib204 article-title: I-vector based patient adaptation of deep neural networks for automatic heartbeat classification publication-title: IEEE Journal of Biomedical and Health Informatics doi: 10.1109/JBHI.2019.2919732 – start-page: 1 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib36 article-title: Classification of atrial fibrillation using stacked auto encoders neural networks – volume: 521 start-page: 436 year: 2015 ident: 10.1016/j.compbiomed.2020.103801_bib89 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – volume: 19 start-page: 2828 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib83 article-title: An evaluation method of safe driving for senior adults using ecg signals publication-title: Sensors doi: 10.3390/s19122828 – volume: 7 start-page: 146457 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib96 article-title: Dual-input neural network integrating feature extraction and deep learning for coronary artery disease detection using electrocardiogram and phonocardiogram publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2943197 – start-page: 1908 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib190 article-title: A deep learning method to detect atrial fibrillation based on continuous wavelet transform – volume: 4 start-page: 6344 year: 2016 ident: 10.1016/j.compbiomed.2020.103801_bib212 article-title: Ecg monitoring system integrated with ir-uwb radar based on cnn publication-title: IEEE Access – start-page: 557 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib45 article-title: Pgans: personalized generative adversarial networks for ecg synthesis to improve patient-specific deep ecg classification – volume: 7 start-page: 99964 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib134 article-title: Preprocessing method for performance enhancement in cnn-based stemi detection from 12-lead ecg publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2930770 – start-page: 1 year: 2017 ident: 10.1016/j.compbiomed.2020.103801_bib183 article-title: Using a random forest to inspire a neural network and improving on it – start-page: 2999 year: 2017 ident: 10.1016/j.compbiomed.2020.103801_bib108 article-title: Focal loss for dense object detection – volume: 40 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib127 article-title: Detection of atrial fibrillation and other abnormal rhythms from ecg using a multi-layer classifier architecture publication-title: Physiol. Meas. doi: 10.1088/1361-6579/aaff04 – volume: 24 start-page: 407 issue: 2 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib72 article-title: Atrial fibrillation prediction with residual network using sensitivity & orthogonality constraints publication-title: IEEE Journal of Biomedical and Health Informatics doi: 10.1109/JBHI.2019.2957809 – start-page: 2707 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib111 article-title: Classification of heart diseases based on ecg signals using long short-term memory – start-page: 148 year: 2020 ident: 10.1016/j.compbiomed.2020.103801_bib66 article-title: Cardiolearn: a cloud deep learning service for cardiac disease detection from electrocardiogram – volume: 12 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib175 article-title: Automated and interpretable patient ecg profiles for disease detection, tracking, and discovery publication-title: Circulation: Cardiovascular Quality and Outcomes – year: 2016 ident: 10.1016/j.compbiomed.2020.103801_bib56 article-title: Deep compression: compressing deep neural network with pruning, trained quantization and huffman coding – volume: 140 start-page: 93 year: 2017 ident: 10.1016/j.compbiomed.2020.103801_bib213 article-title: Recognition of emotions using multimodal physiological signals and an ensemble deep learning model publication-title: Comput. Methods Progr. Biomed. doi: 10.1016/j.cmpb.2016.12.005 – start-page: 1 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib23 article-title: Af detection by exploiting the spectral and temporal characteristics of ecg signals with the lstm model – volume: 23 start-page: 1574 issue: 4 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib203 article-title: Towards end-to-end ecg classification with raw signal extraction and deep neural networks publication-title: IEEE Journal of Biomedical and Health Informatics doi: 10.1109/JBHI.2018.2871510 – year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib179 – volume: 138 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib41 article-title: Deep learning to detect atrial fibrillation from short noisy ecg segments measured with wireless sensors publication-title: Circulation – year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib168 – volume: 102 start-page: 278 year: 2018 ident: 10.1016/j.compbiomed.2020.103801_bib132 article-title: Automated diagnosis of arrhythmia using combination of cnn and lstm techniques with variable length heart beats publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2018.06.002 – volume: 7 start-page: 34060 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib223 article-title: Noise rejection for wearable ecgs using modified frequency slice wavelet transform and convolutional neural networks publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2900719 – volume: 12 year: 2017 ident: 10.1016/j.compbiomed.2020.103801_bib217 article-title: Ecg data compression using a neural network model based on multi-objective optimization publication-title: PloS One – volume: 40 year: 2019 ident: 10.1016/j.compbiomed.2020.103801_bib98 article-title: Ventricular ectopic beat detection using a wavelet transform and a convolutional neural network publication-title: Physiol. Meas. doi: 10.1088/1361-6579/ab17f0 – start-page: 3155 year: 2017 ident: 10.1016/j.compbiomed.2020.103801_bib107 article-title: Adversarial ranking for language generation |
| SSID | ssj0004030 |
| Score | 2.6599011 |
| SecondaryResourceType | review_article |
| Snippet | The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results... BackgroundThe electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved... |
| SourceID | proquest pubmed crossref elsevier |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 103801 |
| SubjectTerms | Annotations Artificial intelligence Artificial neural networks Biomedical engineering Biometric Identification Biometrics Cardiac arrhythmia Cardiology Classification Data mining Deep Learning Deep neural network(s) Diagnostic software Diagnostic systems EKG Electrocardiogram (ECG/EKG) Electrocardiography Engineering Health care Health informatics Humans Identification International conferences Keywords Localization Machine learning Neural networks Neural Networks, Computer Noise reduction Physiology Recurrent neural networks Scientific papers Signal processing Sleep Sleep Stages Systematic review |
| SummonAdditionalLinks | – databaseName: ScienceDirect Journal Collection dbid: AIKHN link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3dS8MwED90gvgifju_iOBrcW2atNGnIcpU1BcF30KaD1GkG277_7006YagMPCxbQ7SS-7yO_K7O4AzJwqRlponWWpMkjtqE8WtTnyp9lwJp5VpWL6PfPCS372y1yW4anNhPK0y-v7g0xtvHd-cR22ej97ffY4vhhIY4GQe1XORL8NKRgVnHVjp394PHufpkT0aMlHQ5XiBSOgJNC_P3A6Z7hgsZqFgeOwQ88sp9RcKbU6jmw1YjzCS9MNMN2HJ1luw-hAvyrfBPo08sJ7WTcFUompDdNs2ZUyGjhhrRyS2jHgjoY_0mCCCJbExjm6Iqp67RTyL9IL0ybzqMwkZLzvwcnP9fDVIYkeFROdMTBLTc5UWipaKWVuUqsoz02OKMstFJUShmRK2xLWi1roqdYazVCk0e8cMS7mhu9Cph7XdB0KdcSkuK8X4Ja8qrgx1Gbq_onKOcie6ULQalDqWG_ddLz5lyyv7kHPdS697GXTfhXQmOQolNxaQEe0iyTalFJ2gxHNhAdnLmeyPrbeg9FG7J2Q0_7FEWCR6vqwP78Lp7DMarr-NUbUdTv2YjBai5CmO2Qt7afa7iKlZWWTs4F9TO4Q1_xTIxUfQmXxN7TFCqEl1Ek3kG83RHOg priority: 102 providerName: Elsevier |
| Title | Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review |
| URI | https://www.clinicalkey.com/#!/content/1-s2.0-S0010482520301694 https://dx.doi.org/10.1016/j.compbiomed.2020.103801 https://www.ncbi.nlm.nih.gov/pubmed/32658725 https://www.proquest.com/docview/2419048936 https://www.proquest.com/docview/2423798616 |
| Volume | 122 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Nj9MwEB3tbiXEHhAsH1tYKiNxDTRxbMcghAraUkAUhFipN8vxBxJCaaHtld_OOLZbDoB6ySWZKMmMx-P4zXsAj70UsmwML6rS2qL21BWaO1MEqvZaS2-07VG-cz67qt8t2OII5rkXJsAqc07sE7VdmvCP_CnONHIcmFL4y9WPIqhGhd3VLKGhk7SCfdFTjB3DAFNyg3E_eHU5__R53yk5prEpBbNPjYujhO2JiK8A4o5N77hurCJ3eBKL-cuE9a-CtJ-YpjfhRqooySSGwC04ct0ZnP7BM3gG1z6kHfTb4D6uQsW97XomVaI7S0zWU1mTpSfWuRVJWhJfSRSYXhMsbUlSzDE9gjWAukiAlz4jE7KngyaxFeYOXE0vv7yeFUlqoTA1k5vCjn1rpKaNZs6JRrd1ZcdMU-a4bKUUhmnpGnQidc63pbeclVpjPvDMspJbehdOumXnzoFQb32J_qa4sKnblmtLfYV5UbTeU-7lEET-nsokHvIgh_FdZcDZN7X3hAqeUNETQyh3lqvIxXGAjcwuU7nXFLOjwgnjANvnO9tUj8Q640DrixwhKuWFtdpH8RAe7U7jiA7bNLpzy224pqJCNrzEa-7FyNq9LhbbrBEVu___mz-A6-FJIqz4Ak42P7fuIRZPm3YEx09-lXgUC4HHZvpmBIPJ2_ez-SiNld_mGx_2 |
| linkProvider | ProQuest |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwEB6VrcTjUEF5bVvASHCM2MSxE4MqVKDVlrYLQq3Um3H8qFSh7NLdFeLP8dsYx_YuB0B76Tkey8nY43H8zfcBvHCiEnmteVbkxmSlozZT3OrMU7WXSjitTIfyHfHhWfnxnJ2vwa9UC-NhlSkmdoHajLX_R_4Kdxox8Ewp_O3ke-ZVo_ztapLQUFFawex2FGOxsOPI_vyBR7jp7uEH9PfLojjYP30_zKLKQKZLJmaZGbhGC0VrxaytatWUhRkwRZnlohGi0kwJW-P4qbWuyZ3hLFcKl4JjhuXcUOz3BqyXvsK1B-vv9kefvywrMwc0FMFgtCvxMBaxRAFh5kHjocgez6lF4CqP4jR_2SD_lQB3G-HBXdiIGSzZC1PuHqzZdhPu_MFruAk3T-KN_X2wnyY-w5-3HXMrUa0hOum3TMnYEWPthETtigsSBK2nBFNpEhV6dIeY9SAy4uGsr8keWdJPk1B68wDOruWjP4ReO27tYyDUGZfj_KJ4kCqbhitDXYFxuGqco9yJPlTpe0odec-9_MY3mQBul3LpCek9IYMn-pAvLCeB-2MFG5FcJlNtK0ZjiRvUCrZvFrYx_wl5zYrWO2mGyBiHpnK5avrwfPEYI4i_FlKtHc99m4JWouY5tnkUZtbidTG5Z3VVsK3_d_4Mbg1PT47l8eHoaBtu-1EFSPMO9GZXc_sEE7dZ8zSuDgJfr3tB_gY-dFq4 |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1bb9MwFD4anTTBA4Jx6zbASPAYrYljJx6a0GCrNgZlQkzam3F8mTShtKytEH-RX8VxbLc8AOrLnpsTJTk-F9ff-T6Al05UIq81z4rcmKx01GaKW515qvZSCaeV6VC-I358Xr6_YBdr8CvNwnhYZcqJXaI2Y-3_I9_FSiMGnimF77oIizg7HL6ZfM-8gpQ_aU1yGirKLJj9jm4sDnmc2p8_cDs33T85RN-_Korh0Zd3x1lUHMh0ycQsMwPXaKForZi1Va2asjADpiizXDRCVJopYWt8F2qta3JnOMuVwrBwzLCcG4r3vQXrFVbJsgfrb49GZ5-XU5oDGgZiMPOVuDGLuKKANvMA8jBwj3vWIvCWR6GavxTLfzXDXVEc3oO7sZslB2H53Yc1227CnT84Djdh42M8vX8A9tPEd_vztmNxJao1RCctlykZO2KsnZCoY3FJgrj1lGBbTaJaj-7Qsx5QRjy0dY8ckCUVNQljOA_h_EY--iPotePWPgFCnXE5rjWKm6qyabgy1BWYk6vGOcqd6EOVvqfUkQPdS3F8kwnsdiWXnpDeEzJ4og_5wnISeEBWsBHJZTLNuWJmllisVrB9vbCNvVDocVa03kkrRMacNJXLCOrDi8XPmE38EZFq7XjuryloJWqe4zWPw8pavC42-qyuCrb1_5s_hw0MTPnhZHS6Dbf9QwV08w70Ztdz-xR7uFnzLAYHga83HY-_AQzdXuQ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Opportunities+and+challenges+of+deep+learning+methods+for+electrocardiogram+data%3A+A+systematic+review&rft.jtitle=Computers+in+biology+and+medicine&rft.au=Hong%2C+Shenda&rft.au=Zhou%2C+Yuxi&rft.au=Shang%2C+Junyuan&rft.au=Xiao%2C+Cao&rft.date=2020-07-01&rft.pub=Elsevier+Ltd&rft.issn=0010-4825&rft.volume=122&rft_id=info:doi/10.1016%2Fj.compbiomed.2020.103801&rft.externalDocID=S0010482520301694 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0010-4825&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0010-4825&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0010-4825&client=summon |