Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals
Stroke is the third highest cause of death worldwide after cancer and heart disease, and the number of stroke diseases due to aging is set to at least triple by 2030. As the top three causes of death worldwide are all related to chronic disease, the importance of healthcare is increasing even more....
Saved in:
Published in | Applied sciences Vol. 11; no. 4; p. 1761 |
---|---|
Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
Basel
MDPI AG
01.02.2021
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | Stroke is the third highest cause of death worldwide after cancer and heart disease, and the number of stroke diseases due to aging is set to at least triple by 2030. As the top three causes of death worldwide are all related to chronic disease, the importance of healthcare is increasing even more. Models that can predict real-time health conditions and diseases using various healthcare services are attracting increasing attention. Most diagnosis and prediction methods of stroke for the elderly involve imaging techniques such as magnetic resonance imaging (MRI). It is difficult to rapidly and accurately diagnose and predict stroke diseases due to the long testing times and high costs associated with MRI. Thus, in this paper, we design and implement a health monitoring system that can predict the precursors of stroke diseases in the elderly in real time during daily walking. First, raw electroencephalography (EEG) data from six channels were preprocessed via Fast Fourier Transform (FFT). The raw EEG power values were then extracted from the raw spectra: alpha (α), beta (β), gamma (γ), delta (δ), and theta (θ) as well as the low β, high β, and θ to β ratio, respectively. The experiments in this paper confirm that the important features of EEG biometric signals alone during walking can accurately determine stroke precursors and occurrence in the elderly with more than 90% accuracy. Further, the Random Forest algorithm with quartiles and Z-score normalization validates the clinical significance and performance of the system proposed in this paper with a 92.51% stroke prediction accuracy. The proposed system can be implemented at a low cost, and it can be applied for early disease detection and prediction using the precursor symptoms of real-time stroke. Furthermore, it is expected that it will be able to detect other diseases such as cancer and heart disease in the future. |
---|---|
AbstractList | Stroke is the third highest cause of death worldwide after cancer and heart disease, and the number of stroke diseases due to aging is set to at least triple by 2030. As the top three causes of death worldwide are all related to chronic disease, the importance of healthcare is increasing even more. Models that can predict real-time health conditions and diseases using various healthcare services are attracting increasing attention. Most diagnosis and prediction methods of stroke for the elderly involve imaging techniques such as magnetic resonance imaging (MRI). It is difficult to rapidly and accurately diagnose and predict stroke diseases due to the long testing times and high costs associated with MRI. Thus, in this paper, we design and implement a health monitoring system that can predict the precursors of stroke diseases in the elderly in real time during daily walking. First, raw electroencephalography (EEG) data from six channels were preprocessed via Fast Fourier Transform (FFT). The raw EEG power values were then extracted from the raw spectra: alpha (α), beta (β), gamma (γ), delta (δ), and theta (θ) as well as the low β, high β, and θ to β ratio, respectively. The experiments in this paper confirm that the important features of EEG biometric signals alone during walking can accurately determine stroke precursors and occurrence in the elderly with more than 90% accuracy. Further, the Random Forest algorithm with quartiles and Z-score normalization validates the clinical significance and performance of the system proposed in this paper with a 92.51% stroke prediction accuracy. The proposed system can be implemented at a low cost, and it can be applied for early disease detection and prediction using the precursor symptoms of real-time stroke. Furthermore, it is expected that it will be able to detect other diseases such as cancer and heart disease in the future. Stroke is the third highest cause of death worldwide after cancer and heart disease, and the number of stroke diseases due to aging is set to at least triple by 2030. As the top three causes of death worldwide are all related to chronic disease, the importance of healthcare is increasing even more. Models that can predict real-time health conditions and diseases using various healthcare services are attracting increasing attention. Most diagnosis and prediction methods of stroke for the elderly involve imaging techniques such as magnetic resonance imaging (MRI). It is difficult to rapidly and accurately diagnose and predict stroke diseases due to the long testing times and high costs associated with MRI. Thus, in this paper, we design and implement a health monitoring system that can predict the precursors of stroke diseases in the elderly in real time during daily walking. First, raw electroencephalography (EEG) data from six channels were preprocessed via Fast Fourier Transform (FFT). The raw EEG power values were then extracted from the raw spectra: alpha (α), beta (β), gamma (γ), delta (δ), and theta (θ) as well as the lowβ, highβ, andθtoβratio, respectively. The experiments in this paper confirm that the important features of EEG biometric signals alone during walking can accurately determine stroke precursors and occurrence in the elderly with more than 90% accuracy. Further, the Random Forest algorithm with quartiles and Z-score normalization validates the clinical significance and performance of the system proposed in this paper with a 92.51% stroke prediction accuracy. The proposed system can be implemented at a low cost, and it can be applied for early disease detection and prediction using the precursor symptoms of real-time stroke. Furthermore, it is expected that it will be able to detect other diseases such as cancer and heart disease in the future. |
Author | Ho, Chee Meng Benjamin Yu, Jaehak Lee, Hansung Choi, Yoon-A Jun, Jong-Arm Pyo, Cheol-Sig Park, Sejin |
Author_xml | – sequence: 1 givenname: Yoon-A surname: Choi fullname: Choi, Yoon-A – sequence: 2 givenname: Sejin surname: Park fullname: Park, Sejin – sequence: 3 givenname: Jong-Arm surname: Jun fullname: Jun, Jong-Arm – sequence: 4 givenname: Chee Meng Benjamin orcidid: 0000-0001-5473-9597 surname: Ho fullname: Ho, Chee Meng Benjamin – sequence: 5 givenname: Cheol-Sig surname: Pyo fullname: Pyo, Cheol-Sig – sequence: 6 givenname: Hansung orcidid: 0000-0002-6519-4120 surname: Lee fullname: Lee, Hansung – sequence: 7 givenname: Jaehak orcidid: 0000-0003-0281-7666 surname: Yu fullname: Yu, Jaehak |
BookMark | eNp9UctOwzAQtBBIvHriByJxRAG_nRwBFahUxKHA1drYmzYlxMEOh_49KUWIE3uZnd3RaFdzTPa70CEhZ4xeClHSK-h7xqhkRrM9csSp0bkY2f6f_pBMUlrTsUomCkaPSPUIbtV0mM8RYtd0y_wGEvps2nqM7SZbDDG8YfYYumYIcdxni00a8D17SVsybdGNCuwc9itowzJCv9pkr80AbbZolh206ZQc1CPg5AdPyMvd9Pn2IZ8_3c9ur-e5EyUfci09M7WHqjQSsZbSFYX2RgvgzHgDCLXSBUetOauB80oqRTmVWILx1HFxQmY7Xx9gbfvYvEPc2ACN_R6EuLQQh8a1aLWnhUclRCW85FRVigpTaMekUKagOHqd77z6GD4-MQ12HT7j9hvLlZCalkqZf1Wy5IwxIeioutipXAwpRax_b2PUbpOzf5ITX1Vdi4w |
CitedBy_id | crossref_primary_10_1080_00038628_2024_2360910 crossref_primary_10_1016_j_bspc_2023_105295 crossref_primary_10_3390_s21134269 crossref_primary_10_1142_S0219519423500781 crossref_primary_10_38124_ijisrt_IJISRT24APR2566 crossref_primary_10_1007_s11227_021_04209_1 crossref_primary_10_34198_ejms_14124_133150 crossref_primary_10_1016_j_health_2022_100116 crossref_primary_10_1109_ACCESS_2022_3169284 crossref_primary_10_1109_COMST_2023_3256323 crossref_primary_10_1016_j_bspc_2023_105454 |
Cites_doi | 10.5853/jos.2020.01928 10.1503/cmaj.140355 10.1016/S1474-4422(10)70164-2 10.1016/j.jneumeth.2019.03.017 10.1016/S0197-2456(03)00072-2 10.5853/jos.2016.01935 10.4218/etrij.2018-0118 10.1016/S0140-6736(11)60325-5 10.4218/etrij.2018-0630 10.3390/app10196791 10.1109/EMBC.2019.8857679 10.1046/j.1440-1681.1999.03081.x 10.5853/jos.2018.01305 10.1016/j.clinph.2015.07.014 10.3390/math8071115 10.1161/01.STR.25.11.2220 10.1016/j.nicl.2014.02.003 10.1016/j.neunet.2005.01.006 10.1111/j.1600-0404.1998.tb07321.x 10.1109/PlatCon.2019.8668961 10.1136/bmjopen-2019-032087 10.1109/TPAMI.2004.105 10.1109/TNSRE.2019.2940485 10.1109/TITB.2006.879600 10.1109/EMBC.2018.8512231 10.1007/978-3-319-49557-6_9 10.1161/01.STR.25.1.40 10.1212/WNL.0b013e3181b78425 10.1016/j.cmpb.2005.06.011 10.3390/s20040969 10.1109/TNNLS.2018.2789927 10.1212/01.WNL.0000163510.79351.AF 10.1080/1086508X.2005.11079517 10.1016/j.cmpb.2018.04.012 10.1161/01.STR.32.3.656 10.1109/EMBC.2019.8857234 10.1016/j.mehy.2019.109315 10.1212/WNL.35.12.1714 10.3390/s18051383 10.3390/s19071738 |
ContentType | Journal Article |
Copyright | 2021. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
Copyright_xml | – notice: 2021. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
DBID | AAYXX CITATION ABUWG AFKRA AZQEC BENPR CCPQU DWQXO PIMPY PQEST PQQKQ PQUKI PRINS DOA |
DOI | 10.3390/app11041761 |
DatabaseName | CrossRef ProQuest Central (Alumni) ProQuest Central ProQuest Central Essentials ProQuest Central ProQuest One Community College ProQuest Central Korea Publicly Available Content Database ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China Directory of Open Access Journals |
DatabaseTitle | CrossRef Publicly Available Content Database ProQuest Central ProQuest One Academic UKI Edition ProQuest Central Essentials ProQuest Central Korea ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Academic ProQuest Central China |
DatabaseTitleList | Publicly Available Content Database Publicly Available Content Database CrossRef |
Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Engineering Sciences (General) |
EISSN | 2076-3417 |
ExternalDocumentID | oai_doaj_org_article_6d08de533b3d4205b503786c1435780e 10_3390_app11041761 |
GroupedDBID | .4S 2XV 5VS 7XC 8CJ 8FE 8FG 8FH AADQD AAFWJ AAYXX ABJCF ADBBV AFKRA AFPKN AFZYC ALMA_UNASSIGNED_HOLDINGS APEBS ARAPS ARCSS ATCPS BBNVY BCNDV BENPR BHPHI BKSAR CCPQU CITATION CZ9 D1I D1J D1K GROUPED_DOAJ HCIFZ IAO ITC K6- K6V K7- KB. KC. KQ8 L6V LK5 LK8 M0K M7P M7R M7S MODMG M~E N95 OK1 P62 PATMY PCBAR PDBOC PIMPY PROAC PYCSY RIG TUS ABUWG AZQEC DWQXO PQEST PQQKQ PQUKI PRINS |
ID | FETCH-LOGICAL-c392t-64d17fdab974eef44c886d763a217d7aeaf5682e6621fa22b4550204e9a7d0c23 |
IEDL.DBID | 8FG |
ISSN | 2076-3417 |
IngestDate | Thu Jul 04 21:12:09 EDT 2024 Fri Sep 13 03:11:10 EDT 2024 Fri Sep 13 03:07:43 EDT 2024 Fri Aug 23 04:44:20 EDT 2024 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 4 |
Language | English |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c392t-64d17fdab974eef44c886d763a217d7aeaf5682e6621fa22b4550204e9a7d0c23 |
ORCID | 0000-0002-6519-4120 0000-0003-0281-7666 0000-0001-5473-9597 |
OpenAccessLink | https://www.proquest.com/docview/2534609557/abstract/?pq-origsite=%requestingapplication% |
PQID | 2492111330 |
PQPubID | 2032433 |
ParticipantIDs | doaj_primary_oai_doaj_org_article_6d08de533b3d4205b503786c1435780e proquest_journals_2534609557 proquest_journals_2492111330 crossref_primary_10_3390_app11041761 |
PublicationCentury | 2000 |
PublicationDate | 2021-02-01 |
PublicationDateYYYYMMDD | 2021-02-01 |
PublicationDate_xml | – month: 02 year: 2021 text: 2021-02-01 day: 01 |
PublicationDecade | 2020 |
PublicationPlace | Basel |
PublicationPlace_xml | – name: Basel |
PublicationTitle | Applied sciences |
PublicationYear | 2021 |
Publisher | MDPI AG |
Publisher_xml | – name: MDPI AG |
References | Acharya (ref_32) 2018; 161 Schneider (ref_35) 2005; 45 Kannel (ref_16) 1984; 4 Finnigan (ref_34) 2016; 127 Oh (ref_46) 2004; 26 Chambon (ref_29) 2019; 321 Kim (ref_3) 2017; 19 Sakhavi (ref_26) 2018; 29 Acharya (ref_30) 2005; 80 Johansson (ref_4) 1999; 26 Belanger (ref_14) 1994; 25 Hanifa (ref_41) 2010; 1 Musuka (ref_15) 2015; 187 Subasi (ref_20) 2005; 18 Shanthi (ref_38) 2009; 3 Lee (ref_13) 2010; 28 Varelas (ref_36) 2017; 14 ref_22 Toraman (ref_25) 2019; 131 ref_28 (ref_19) 2019; 41 ref_27 Tian (ref_31) 2019; 27 Litwak (ref_8) 2005; 64 Gottesman (ref_5) 2010; 9 Benbadis (ref_24) 2009; 73 Bentley (ref_40) 2014; 4 Lee (ref_11) 2020; 42 Amini (ref_45) 2013; 4 Bushnell (ref_10) 2001; 32 ref_33 Pikija (ref_7) 2018; 20 ref_39 ref_37 Williams (ref_23) 1985; 35 Guler (ref_21) 2007; 11 Carroll (ref_17) 2003; 24 Seo (ref_1) 2020; 22 ref_47 Lyden (ref_12) 1994; 25 ref_44 ref_43 ref_42 Zhang (ref_18) 2019; 9 Langhorne (ref_9) 2011; 377 ref_2 Korpelainen (ref_6) 1998; 98 ref_49 ref_48 |
References_xml | – volume: 22 start-page: 412 year: 2020 ident: ref_1 article-title: National Trends in Clinical Outcomes of Endovascular Therapy for Ischemic Stroke in South Korea between 2008 and 2016 publication-title: J. Stroke doi: 10.5853/jos.2020.01928 contributor: fullname: Seo – ident: ref_49 – volume: 187 start-page: 887 year: 2015 ident: ref_15 article-title: Diagnosis and management of acute ischemic stroke: Speed is critical publication-title: Can. Med Assoc. J. doi: 10.1503/cmaj.140355 contributor: fullname: Musuka – volume: 9 start-page: 895 year: 2010 ident: ref_5 article-title: Predictors and assessment of cognitive dysfunction resulting from ischaemic stroke publication-title: Lancet Neurol. doi: 10.1016/S1474-4422(10)70164-2 contributor: fullname: Gottesman – volume: 321 start-page: 64 year: 2019 ident: ref_29 article-title: DOSED: A deep learning approach to detect multiple sleep micro-events in EEG signal publication-title: J. Neurosci. Methods doi: 10.1016/j.jneumeth.2019.03.017 contributor: fullname: Chambon – volume: 24 start-page: 682 year: 2003 ident: ref_17 article-title: On the use and utility of the Weibull model in the analysis of survival data publication-title: Control. Clin. Trials doi: 10.1016/S0197-2456(03)00072-2 contributor: fullname: Carroll – volume: 19 start-page: 28 year: 2017 ident: ref_3 article-title: Spontaneous Intracerebral Hemorrhage: Management publication-title: J. Stroke doi: 10.5853/jos.2016.01935 contributor: fullname: Kim – volume: 42 start-page: 217 year: 2020 ident: ref_11 article-title: Automated epileptic seizure waveform detection method based on the feature of the mean slope of wavelet coefficient counts using a hidden Markov model and EEG signals publication-title: ETRI J. doi: 10.4218/etrij.2018-0118 contributor: fullname: Lee – volume: 4 start-page: 267 year: 1984 ident: ref_16 article-title: Latest perspectives on cigarette smoking and cardiovascular disease: The Framingham Study publication-title: J. Card. Rehabil. contributor: fullname: Kannel – volume: 377 start-page: 1693 year: 2011 ident: ref_9 article-title: Stroke rehabilitation publication-title: Lancet doi: 10.1016/S0140-6736(11)60325-5 contributor: fullname: Langhorne – volume: 41 start-page: 452 year: 2019 ident: ref_19 article-title: SDN-based wireless body area network routing algorithm for healthcare architecture publication-title: ETRI J. doi: 10.4218/etrij.2018-0630 – ident: ref_42 doi: 10.3390/app10196791 – volume: 3 start-page: 10 year: 2009 ident: ref_38 article-title: Designing an artificial neural network model for the prediction of thrombo-embolic stroke publication-title: Int. J. Biom. Bioinform. contributor: fullname: Shanthi – volume: 4 start-page: S245 year: 2013 ident: ref_45 article-title: Prediction and Control of Stroke by Data Mining publication-title: Int. J. Prev. Med. contributor: fullname: Amini – ident: ref_37 doi: 10.1109/EMBC.2019.8857679 – volume: 26 start-page: 563 year: 1999 ident: ref_4 article-title: Hypertension Mechanisms Causing Stroke publication-title: Clin. Exp. Pharmacol. Physiol. doi: 10.1046/j.1440-1681.1999.03081.x contributor: fullname: Johansson – ident: ref_48 – volume: 20 start-page: 373 year: 2018 ident: ref_7 article-title: Higher Blood Pressure during Endovascular Thrombectomy in Anterior Circulation Stroke Is Associated with Better Outcomes publication-title: J. Stroke doi: 10.5853/jos.2018.01305 contributor: fullname: Pikija – volume: 1 start-page: 47 year: 2010 ident: ref_41 article-title: Stroke risk prediction through non-linear support vector classification models publication-title: Int. J. Adv. Res. Comput. Sci. contributor: fullname: Hanifa – volume: 127 start-page: 1452 year: 2016 ident: ref_34 article-title: Defining abnormal slow EEG activity in acute ischaemic stroke: Delta/alpha ratio as an optimal QEEG index publication-title: Clin. Neurophysiol. doi: 10.1016/j.clinph.2015.07.014 contributor: fullname: Finnigan – ident: ref_44 doi: 10.3390/math8071115 – volume: 25 start-page: 2220 year: 1994 ident: ref_12 article-title: Improved reliability of the NIH Stroke Scale using video training. NINDS TPA Stroke Study Group publication-title: Stroke doi: 10.1161/01.STR.25.11.2220 contributor: fullname: Lyden – volume: 4 start-page: 635 year: 2014 ident: ref_40 article-title: Prediction of stroke thrombolysis outcome using CT brain machine learning publication-title: NeuroImage Clin. doi: 10.1016/j.nicl.2014.02.003 contributor: fullname: Bentley – volume: 18 start-page: 985 year: 2005 ident: ref_20 article-title: Wavelet neural network classification of EEG signals by using AR model with MLE preprocessing publication-title: Neural Netw. doi: 10.1016/j.neunet.2005.01.006 contributor: fullname: Subasi – volume: 98 start-page: 400 year: 1998 ident: ref_6 article-title: Sexual dysfunction in stroke patients publication-title: Acta Neurol. Scand. doi: 10.1111/j.1600-0404.1998.tb07321.x contributor: fullname: Korpelainen – ident: ref_43 doi: 10.1109/PlatCon.2019.8668961 – volume: 9 start-page: e032087 year: 2019 ident: ref_18 article-title: Time to recurrence after first-ever ischaemic stroke within 3 years and its risk factors in Chinese population: A prospective cohort study publication-title: BMJ Open doi: 10.1136/bmjopen-2019-032087 contributor: fullname: Zhang – volume: 28 start-page: 13 year: 2010 ident: ref_13 article-title: Development of a stroke prediction model for Korean publication-title: J. Korean Neurol. Assoc. contributor: fullname: Lee – volume: 26 start-page: 1424 year: 2004 ident: ref_46 article-title: Hybrid genetic algorithms for feature selection publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2004.105 contributor: fullname: Oh – volume: 27 start-page: 1962 year: 2019 ident: ref_31 article-title: Deep Multi-View Feature Learning for EEG-Based Epileptic Seizure Detection publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2019.2940485 contributor: fullname: Tian – ident: ref_47 – volume: 11 start-page: 117 year: 2007 ident: ref_21 article-title: Multiclass Support Vector Machines for EEG-Signals Classification publication-title: IEEE Trans. Inf. Technol. Biomed. doi: 10.1109/TITB.2006.879600 contributor: fullname: Guler – ident: ref_33 doi: 10.1109/EMBC.2018.8512231 – volume: 14 start-page: 155 year: 2017 ident: ref_36 article-title: Ischemic Stroke, Hyperperfusion Syndrome, Cerebral Sinus Thrombosis, and Critical Care Seizures publication-title: Seizures Crit. Care doi: 10.1007/978-3-319-49557-6_9 contributor: fullname: Varelas – volume: 25 start-page: 40 year: 1994 ident: ref_14 article-title: Stroke risk profile: Adjustment for antihypertensive medication. The Framingham Study publication-title: Stroke doi: 10.1161/01.STR.25.1.40 contributor: fullname: Belanger – volume: 73 start-page: 843 year: 2009 ident: ref_24 article-title: Interrater reliability of EEG-video monitoring publication-title: Neurology doi: 10.1212/WNL.0b013e3181b78425 contributor: fullname: Benbadis – volume: 80 start-page: 37 year: 2005 ident: ref_30 article-title: Non-linear analysis of EEG signals at various sleep stages publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2005.06.011 contributor: fullname: Acharya – ident: ref_22 doi: 10.3390/s20040969 – volume: 29 start-page: 5619 year: 2018 ident: ref_26 article-title: Learning Temporal Information for Brain-Computer Interface Using Convolutional Neural Networks publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2018.2789927 contributor: fullname: Sakhavi – volume: 64 start-page: 1888 year: 2005 ident: ref_8 article-title: Social isolation and outcomes post stroke publication-title: Neurology doi: 10.1212/01.WNL.0000163510.79351.AF contributor: fullname: Litwak – volume: 45 start-page: 102 year: 2005 ident: ref_35 article-title: Regional Attenuation without Delta (RAWOD): A distinctive EEG pattern that can aid in the diagnosis and management of severe acute ischemic stroke publication-title: Am. J. Electroneurodiagnostic Technol. doi: 10.1080/1086508X.2005.11079517 contributor: fullname: Schneider – ident: ref_2 – volume: 161 start-page: 103 year: 2018 ident: ref_32 article-title: Automated EEG-based screening of depression using deep convolutional neural network publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2018.04.012 contributor: fullname: Acharya – volume: 32 start-page: 656 year: 2001 ident: ref_10 article-title: Retrospective assessment of initial stroke severity: Comparison of the NIH stroke scale and the Canadian neurological scale publication-title: Stroke doi: 10.1161/01.STR.32.3.656 contributor: fullname: Bushnell – ident: ref_39 doi: 10.1109/EMBC.2019.8857234 – volume: 131 start-page: 109315 year: 2019 ident: ref_25 article-title: Is it possible to detect cerebral dominance via EEG signals by using deep learning? publication-title: Med Hypotheses doi: 10.1016/j.mehy.2019.109315 contributor: fullname: Toraman – volume: 35 start-page: 1714 year: 1985 ident: ref_23 article-title: Interobserver variability in EEG interpretation publication-title: Neurology doi: 10.1212/WNL.35.12.1714 contributor: fullname: Williams – ident: ref_27 doi: 10.3390/s18051383 – ident: ref_28 doi: 10.3390/s19071738 |
SSID | ssj0000913810 |
Score | 2.2858129 |
Snippet | Stroke is the third highest cause of death worldwide after cancer and heart disease, and the number of stroke diseases due to aging is set to at least triple... |
SourceID | doaj proquest crossref |
SourceType | Open Website Aggregation Database |
StartPage | 1761 |
SubjectTerms | Aging Blood pressure Brain diseases Brain research Cardiovascular diseases Coronary artery disease Disability Disease detection EEG Electroencephalography Epilepsy Experiments Fast Fourier transformations Fourier transforms Geriatrics Health care Heart diseases Hemorrhage Imaging techniques Learning algorithms machine learning Magnetic resonance imaging Medical imaging Monitoring systems Older people Patients Precursors Predictions Real time real-time health monitoring Risk factors Signs and symptoms Stroke stroke disease analysis stroke prediction Telemedicine Testing time |
SummonAdditionalLinks | – databaseName: Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1BS8MwFA6ykx7ETcXplBx20EOwTdIkPTrZEGFe5mS3kjTpFGUbWz34781LO6koePHahkd4r3nfe_Tl-xDq29TjHhWWeHDShLtCEJPLnCTGQH8hdB60DscP4m7K72fJrCH1BTNhFT1w5bhrYSNlnS9KDLOcRolJIiaVyAHnpYpcyL5x0mimQg5OY6Cuqi7kMd_Xw_9gj3Q8liL-BkGBqf9HIg7oMjpA-3VZiG-q7bTRjlt00F6DLLCD2vUx3ODLmiv66hCZcRiGdKTmSZ2TgYcli4cgvv32gSflevnqcHVywQ6uKMpxGBXwy4IIDphdPestfTV-AiURPHmZA7nyEZqOho-3d6SWTSC5L3ZKIriNZWG18a2CcwXnuVLC-jyiffthpXa6SISiTggaF5pSAxebacRdqqWNcsqOUWuxXLgThFUuWQzCpspq6LUU3Ez1BrQ1EUuF7qL-1pPZqmLHyHxXAQ7PGg7vogF4-WsJUFqHBz7QWR3o7K9Ad1FvG6OsPmebDPgOfbZmLPr9dcI4MOol8vQ_tnCGdimMtISh7R5qlet3d-5rktJchM_vEytL3QI priority: 102 providerName: Directory of Open Access Journals |
Title | Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals |
URI | https://www.proquest.com/docview/2492111330/abstract/ https://www.proquest.com/docview/2534609557/abstract/ https://doaj.org/article/6d08de533b3d4205b503786c1435780e |
Volume | 11 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1LTxsxEB7xuJQD4lFEKEQ-cICDhdf2ep0TIihpVAlUQam4rfzaUBUlIWwP_ff1eHcjUKte1ysfbM_js2e-D-DUD2Lc48rTGJwMlaFS1LrC0dxaxBfKuKR1eHOrJg_yy2P-uAaTrhcGyyo7n5gctZ87vCO_4LmQSI4WAbyxeAvg6ovLxQtF_Sh8Z23FNNZhM0NOPOwZH39e3bYg-6XOWNOgJyLOx_fhGPlkVqjsXUhKzP1_OeYUbcY7sN2mieSq2dddWAuzPdh6Qx64B7utWb6Ss5Y7-nwf7E0qjgy05U2d0mEMU56MUIz7-Te5r5fzn4E0lozzkIaynKTSgfhbEsXBaRdPpqOzJt9RWYTc_5gi2fJHeBiPvl1PaCujQF1MfmqqpM-KyhsboUMIlZROa-WjXzERjvjCBFPlSvOgFM8qw7nFRmfOZBiYwjPHxQFszOazcAhEu0JkKHSqvUHspbFTNU5gvGVioEwPTruVLBcNW0YZUQYuePlmwXswxFVe_YIU1-nDfDktW4splWfah5iNWuElZ7nNmSi0cpjgFZqFHhx3e1S2dvdaIv9h9N5CsH8Prw7R0f-HP8EHjsUrqTz7GDbq5a9wErOP2vbTwerD5nB0-_WunzD8H4MA3Eg |
link.rule.ids | 315,786,790,870,2115,12792,21416,27957,27958,33408,33779,43635,43840,74392,74659 |
linkProvider | ProQuest |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1LbxMxEB7RcqAcEG1BBAr40EN7sPDaXts5VRQ1pI_00hb1tvJrQ1WUhCQc-Pf1eJ2oCMTVtnwYe172zPcB7Id-8ntcBZqck6Uytoo6rz2tncP8QlmfuQ5Hl2p4I89u69vy4LYoZZUrm5gNdZh6fCP_xGshERyt1keznxRZo_B3tVBobMBTKZLrxE7xwdf1GwtiXpqKdW15ImX3-Cuc_J2stKr-cEQZr_8vc5x9zOAlvCjBIfncneY2PImTHXj-CDJwB7aLMi7IQUGMPtwFN8olkZEWtNQxPU7OKZATpOD-8ZtcLefT-0g6_cV9SAdUTnLBQFqWqXBw29l3uwKxJt-QT4Rc3Y0RYvkV3AxOrr8MaSFPoD6FPEuqZKh0G6xLCUOMrZTeGBWSNbEpCQnaRtvWyvCoFK9ay7nD9mbOZOxbHZjn4jVsTqaT-AaI8VpUSG9qgsWMy2B_atrABsdEX9ke7K8k2cw6jIwm5RYo8OaRwHtwjFJeL0Fg6zwwnY-boieNCsyEmGJQJ4LkrHY1E9ooj2GdNiz2YG91Rk3RtkWDqIfJZgvB_j29vjpv_z_9EZ4Nr0cXzcXp5fk72OJYvpILtPdgczn_Fd-n-GPpPuRL9gBMKNjn |
linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1LbxMxELaglRAcKlpAhBbwoQc4WHhtr-2cEIFEbaFRRSnqbeXXBgRKQpIe-u-Z8TpREVWvtuXDeJ72-PsIOYx9iHtCRwbByTGVWs18MIHV3mN9oV3IXIenY310oU4u68vS_7QsbZVrn5gddZwFvCN_J2qpEBwNCvi2tEWcfRq9n_9hyCCFL62FTuM-2TZK16Dh24Ph-Ozr5sYFETBtxbtPehJqfXwjhuinKqOrf8JSRu__zznniDN6THZKqkg_dGe7S-6l6R55dANAcI_sFtNc0jcFP_rtE-JPc4NkYgU7dcIGEKoiHSIh9-9rer5azH4l2lkz7kM72HKa2wdgWSbGwW3nP9wa0pp-R3YRev5zgoDLT8nFaPjt4xErVAosQAK0YlrFyrTReSgfUmqVCtbqCL7FQUkSjUuurbUVSWtRtU4Ij5-dBVep70zkQchnZGs6m6bnhNpgZIVkpzY6rL8s_laFDVz0XPa165HDtSSbeYeY0UClgQJvbgi8RwYo5c0ShLnOA7PFpClW0-jIbUyQkXoZleC1r7k0VgdM8ozlqUcO1mfUFNtbNoiBCB5cSn779EaRXtw9_Zo8AA1rvhyPP--ThwJ7WXK39gHZWi2u0ktIRlb-VdGyvyUt3oo |
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=Machine-Learning-Based+Elderly+Stroke+Monitoring+System+Using+Electroencephalography+Vital+Signals&rft.jtitle=Applied+sciences&rft.au=Choi%2C+Yoon-A&rft.au=Park%2C+Sejin&rft.au=Jun%2C+Jong-Arm&rft.au=Ho%2C+Chee+Meng+Benjamin&rft.date=2021-02-01&rft.issn=2076-3417&rft.eissn=2076-3417&rft.volume=11&rft.issue=4&rft.spage=1761&rft_id=info:doi/10.3390%2Fapp11041761&rft.externalDBID=n%2Fa&rft.externalDocID=10_3390_app11041761 |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2076-3417&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2076-3417&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2076-3417&client=summon |