An Adaptive Weight Learning-Based Multitask Deep Network for Continuous Blood Pressure Estimation Using Electrocardiogram Signals
Estimating blood pressure via combination analysis with electrocardiogram and photoplethysmography signals has attracted growing interest in continuous monitoring patients' health conditions. However, most wearable/portal monitoring devices generally acquire only one kind of physiological signa...
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Published in | Sensors (Basel, Switzerland) Vol. 21; no. 5; p. 1595 |
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Abstract | Estimating blood pressure via combination analysis with electrocardiogram and photoplethysmography signals has attracted growing interest in continuous monitoring patients' health conditions. However, most wearable/portal monitoring devices generally acquire only one kind of physiological signals due to the consideration of energy cost, device weight and size, etc. In this study, a novel adaptive weight learning-based multitask deep learning framework based on single lead electrocardiogram signals is proposed for continuous blood pressure estimation. Specifically, the proposed method utilizes a 2-layer bidirectional long short-term memory network as the sharing layer, followed by three identical architectures of 2-layer fully connected networks for task-specific blood pressure estimation. To learn the importance of task-specific losses automatically, an adaptive weight learning scheme based on the trend of validation loss is proposed. Extensive experiment results on Physionet Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) II waveform database demonstrate that the proposed method using electrocardiogram signals obtains estimating performance of 0.12±10.83 mmHg, 0.13±5.90 mmHg, and 0.08±6.47 mmHg for systolic blood pressure, diastolic blood pressure, and mean arterial pressure, respectively. It can meet the requirements of the British Hypertension Society standard and US Association of Advancement of Medical Instrumentation standard with a considerable margin. Combined with a wearable/portal electrocardiogram device, the proposed model can be deployed to a healthcare system to provide a long-term continuous blood pressure monitoring service, which would help to reduce the incidence of malignant complications to hypertension. |
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AbstractList | Estimating blood pressure via combination analysis with electrocardiogram and photoplethysmography signals has attracted growing interest in continuous monitoring patients’ health conditions. However, most wearable/portal monitoring devices generally acquire only one kind of physiological signals due to the consideration of energy cost, device weight and size, etc. In this study, a novel adaptive weight learning-based multitask deep learning framework based on single lead electrocardiogram signals is proposed for continuous blood pressure estimation. Specifically, the proposed method utilizes a 2-layer bidirectional long short-term memory network as the sharing layer, followed by three identical architectures of 2-layer fully connected networks for task-specific blood pressure estimation. To learn the importance of task-specific losses automatically, an adaptive weight learning scheme based on the trend of validation loss is proposed. Extensive experiment results on Physionet Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) II waveform database demonstrate that the proposed method using electrocardiogram signals obtains estimating performance of
0.12
±
10.83
mmHg,
0.13
±
5.90
mmHg, and
0.08
±
6.47
mmHg for systolic blood pressure, diastolic blood pressure, and mean arterial pressure, respectively. It can meet the requirements of the British Hypertension Society standard and US Association of Advancement of Medical Instrumentation standard with a considerable margin. Combined with a wearable/portal electrocardiogram device, the proposed model can be deployed to a healthcare system to provide a long-term continuous blood pressure monitoring service, which would help to reduce the incidence of malignant complications to hypertension. Estimating blood pressure via combination analysis with electrocardiogram and photoplethysmography signals has attracted growing interest in continuous monitoring patients’ health conditions. However, most wearable/portal monitoring devices generally acquire only one kind of physiological signals due to the consideration of energy cost, device weight and size, etc. In this study, a novel adaptive weight learning-based multitask deep learning framework based on single lead electrocardiogram signals is proposed for continuous blood pressure estimation. Specifically, the proposed method utilizes a 2-layer bidirectional long short-term memory network as the sharing layer, followed by three identical architectures of 2-layer fully connected networks for task-specific blood pressure estimation. To learn the importance of task-specific losses automatically, an adaptive weight learning scheme based on the trend of validation loss is proposed. Extensive experiment results on Physionet Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) II waveform database demonstrate that the proposed method using electrocardiogram signals obtains estimating performance of 0.12±10.83 mmHg, 0.13±5.90 mmHg, and 0.08±6.47 mmHg for systolic blood pressure, diastolic blood pressure, and mean arterial pressure, respectively. It can meet the requirements of the British Hypertension Society standard and US Association of Advancement of Medical Instrumentation standard with a considerable margin. Combined with a wearable/portal electrocardiogram device, the proposed model can be deployed to a healthcare system to provide a long-term continuous blood pressure monitoring service, which would help to reduce the incidence of malignant complications to hypertension. Estimating blood pressure via combination analysis with electrocardiogram and photoplethysmography signals has attracted growing interest in continuous monitoring patients’ health conditions. However, most wearable/portal monitoring devices generally acquire only one kind of physiological signals due to the consideration of energy cost, device weight and size, etc. In this study, a novel adaptive weight learning-based multitask deep learning framework based on single lead electrocardiogram signals is proposed for continuous blood pressure estimation. Specifically, the proposed method utilizes a 2-layer bidirectional long short-term memory network as the sharing layer, followed by three identical architectures of 2-layer fully connected networks for task-specific blood pressure estimation. To learn the importance of task-specific losses automatically, an adaptive weight learning scheme based on the trend of validation loss is proposed. Extensive experiment results on Physionet Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) II waveform database demonstrate that the proposed method using electrocardiogram signals obtains estimating performance of0.12±10.83mmHg,0.13±5.90mmHg, and0.08±6.47mmHg for systolic blood pressure, diastolic blood pressure, and mean arterial pressure, respectively. It can meet the requirements of the British Hypertension Society standard and US Association of Advancement of Medical Instrumentation standard with a considerable margin. Combined with a wearable/portal electrocardiogram device, the proposed model can be deployed to a healthcare system to provide a long-term continuous blood pressure monitoring service, which would help to reduce the incidence of malignant complications to hypertension. |
Author | Wang, Hailiang Li, Ye Fan, Xiaomao Tsui, Kwok Leung Zhao, Yang |
AuthorAffiliation | 2 School of Design, Hong Kong Polytechnic University, Hong Kong, China; hailiang.wang@polyu.edu.hk 3 School of Data Science, City University of Hong Kong, Hong Kong, China; kltsui@cityu.edu.hk 4 Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; ye.li@siat.ac.cn 1 School of Computer Science, South China Normal University, Guangzhou 510631, China; xmfan@scnu.edu.cn |
AuthorAffiliation_xml | – name: 2 School of Design, Hong Kong Polytechnic University, Hong Kong, China; hailiang.wang@polyu.edu.hk – name: 1 School of Computer Science, South China Normal University, Guangzhou 510631, China; xmfan@scnu.edu.cn – name: 4 Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; ye.li@siat.ac.cn – name: 3 School of Data Science, City University of Hong Kong, Hong Kong, China; kltsui@cityu.edu.hk |
Author_xml | – sequence: 1 givenname: Xiaomao surname: Fan fullname: Fan, Xiaomao organization: School of Computer Science, South China Normal University, Guangzhou 510631, China – sequence: 2 givenname: Hailiang surname: Wang fullname: Wang, Hailiang organization: School of Design, Hong Kong Polytechnic University, Hong Kong, China – sequence: 3 givenname: Yang surname: Zhao fullname: Zhao, Yang organization: School of Data Science, City University of Hong Kong, Hong Kong, China – sequence: 4 givenname: Ye surname: Li fullname: Li, Ye organization: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China – sequence: 5 givenname: Kwok Leung surname: Tsui fullname: Tsui, Kwok Leung organization: School of Data Science, City University of Hong Kong, Hong Kong, China |
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CitedBy_id | crossref_primary_10_1016_j_asoc_2023_110520 crossref_primary_10_1016_j_eswa_2023_122812 crossref_primary_10_2196_38454 crossref_primary_10_1007_s10462_022_10353_8 crossref_primary_10_1109_JSEN_2023_3272921 crossref_primary_10_3390_electronics13010179 crossref_primary_10_3390_jsan12010002 crossref_primary_10_1016_j_bspc_2022_103850 crossref_primary_10_1016_j_bspc_2023_105354 crossref_primary_10_3390_s21186264 |
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Snippet | Estimating blood pressure via combination analysis with electrocardiogram and photoplethysmography signals has attracted growing interest in continuous... |
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StartPage | 1595 |
SubjectTerms | Blood Pressure Blood Pressure Determination continuous blood pressure Deep learning electrocardiogram Electrocardiography Estimation Humans Hypertension Hypertension - diagnosis Methods multiple tasks Neural networks Photoplethysmography Physiology Telemedicine Waveforms Wavelet transforms Wearable technology weights learning |
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Title | An Adaptive Weight Learning-Based Multitask Deep Network for Continuous Blood Pressure Estimation Using Electrocardiogram Signals |
URI | https://www.ncbi.nlm.nih.gov/pubmed/33668778 https://www.proquest.com/docview/2494779530/abstract/ https://search.proquest.com/docview/2498485682 https://pubmed.ncbi.nlm.nih.gov/PMC7956522 https://doaj.org/article/ed6eb98ecd6644b2a9d469b82afadf2e |
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