A Machine Learning-Empowered System for Long-Term Motion-Tolerant Wearable Monitoring of Blood Pressure and Heart Rate With Ear-ECG/PPG
In this paper, we propose a fully ear-worn long-term blood pressure (BP) and heart rate (HR) monitor to achieve a higher wearability. Moreover, to enable practical application scenarios, we present a machine learning framework to deal with severe motion artifacts induced by head movements. We sugges...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2017-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/7933339/ |
| _version_ | 1818853893422972928 |
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| author | Qingxue Zhang Xuan Zeng Wenchuang Hu Dian Zhou |
| author_facet | Qingxue Zhang Xuan Zeng Wenchuang Hu Dian Zhou |
| author_sort | Qingxue Zhang |
| collection | DOAJ |
| description | In this paper, we propose a fully ear-worn long-term blood pressure (BP) and heart rate (HR) monitor to achieve a higher wearability. Moreover, to enable practical application scenarios, we present a machine learning framework to deal with severe motion artifacts induced by head movements. We suggest situating all electrocardiogram (ECG) and photoplethysmography (PPG) sensors behind two ears to achieve a super wearability, and successfully acquire weak ear-ECG/PPG signals using a semi-customized platform. After introducing head motions toward real-world application scenarios, we apply a support vector machine classifier to learn and identify raw heartbeats from motion artifacts-impacted signals. Furthermore, we propose an unsupervised learning algorithm to automatically filter out residual distorted/faking heartbeats, for ECG-to-PPG pulse transit time (PTT) and HR estimation. Specifically, we introduce a dynamic time warping-based learning approach to quantify distortion conditions of raw heartbeats referring to a high-quality heartbeat pattern, which are then compared with a threshold to perform purification. The heartbeat pattern and the distortion threshold are learned by a K-medoids clustering approach and a histogram triangle method, respectively. Afterward, we perform a comparative analysis on ten PTT or PTT&HR-based BP learning models. Based on an acquired data set, the BP and HR estimation using the proposed algorithm has an error of -1.4±5.2 mmHg and 0.8±2.7 beats/min, respectively, both much lower than the state-of-the-art approaches. These results demonstrate the capability of the proposed machine learning-empowered system in ear-ECG/PPG acquisition and motion-tolerant BP/HR estimation. This proof-of-concept system is expected to illustrate the feasibility of ear-ECG/PPG-based motion-tolerant BP/HR monitoring. |
| first_indexed | 2024-12-19T07:44:03Z |
| format | Article |
| id | doaj.art-b5840345a437479a92572eb9759e2337 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T07:44:03Z |
| publishDate | 2017-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b5840345a437479a92572eb9759e23372022-12-21T20:30:24ZengIEEEIEEE Access2169-35362017-01-015105471056110.1109/ACCESS.2017.27074727933339A Machine Learning-Empowered System for Long-Term Motion-Tolerant Wearable Monitoring of Blood Pressure and Heart Rate With Ear-ECG/PPGQingxue Zhang0https://orcid.org/0000-0001-7125-7928Xuan Zeng1Wenchuang Hu2Dian Zhou3Department of Electrical Engineering, The University of Texas at Dallas, Richardson, TX, USADepartment of Microelectronics, Fudan University, Shanghai, ChinaDepartment of Electrical Engineering, The University of Texas at Dallas, Richardson, TX, USADepartment of Electrical Engineering, The University of Texas at Dallas, Richardson, TX, USAIn this paper, we propose a fully ear-worn long-term blood pressure (BP) and heart rate (HR) monitor to achieve a higher wearability. Moreover, to enable practical application scenarios, we present a machine learning framework to deal with severe motion artifacts induced by head movements. We suggest situating all electrocardiogram (ECG) and photoplethysmography (PPG) sensors behind two ears to achieve a super wearability, and successfully acquire weak ear-ECG/PPG signals using a semi-customized platform. After introducing head motions toward real-world application scenarios, we apply a support vector machine classifier to learn and identify raw heartbeats from motion artifacts-impacted signals. Furthermore, we propose an unsupervised learning algorithm to automatically filter out residual distorted/faking heartbeats, for ECG-to-PPG pulse transit time (PTT) and HR estimation. Specifically, we introduce a dynamic time warping-based learning approach to quantify distortion conditions of raw heartbeats referring to a high-quality heartbeat pattern, which are then compared with a threshold to perform purification. The heartbeat pattern and the distortion threshold are learned by a K-medoids clustering approach and a histogram triangle method, respectively. Afterward, we perform a comparative analysis on ten PTT or PTT&HR-based BP learning models. Based on an acquired data set, the BP and HR estimation using the proposed algorithm has an error of -1.4±5.2 mmHg and 0.8±2.7 beats/min, respectively, both much lower than the state-of-the-art approaches. These results demonstrate the capability of the proposed machine learning-empowered system in ear-ECG/PPG acquisition and motion-tolerant BP/HR estimation. This proof-of-concept system is expected to illustrate the feasibility of ear-ECG/PPG-based motion-tolerant BP/HR monitoring.https://ieeexplore.ieee.org/document/7933339/Wearable computersblood pressureheart ratephotoplethysmogramelectrocardiographypulse transit time |
| spellingShingle | Qingxue Zhang Xuan Zeng Wenchuang Hu Dian Zhou A Machine Learning-Empowered System for Long-Term Motion-Tolerant Wearable Monitoring of Blood Pressure and Heart Rate With Ear-ECG/PPG IEEE Access Wearable computers blood pressure heart rate photoplethysmogram electrocardiography pulse transit time |
| title | A Machine Learning-Empowered System for Long-Term Motion-Tolerant Wearable Monitoring of Blood Pressure and Heart Rate With Ear-ECG/PPG |
| title_full | A Machine Learning-Empowered System for Long-Term Motion-Tolerant Wearable Monitoring of Blood Pressure and Heart Rate With Ear-ECG/PPG |
| title_fullStr | A Machine Learning-Empowered System for Long-Term Motion-Tolerant Wearable Monitoring of Blood Pressure and Heart Rate With Ear-ECG/PPG |
| title_full_unstemmed | A Machine Learning-Empowered System for Long-Term Motion-Tolerant Wearable Monitoring of Blood Pressure and Heart Rate With Ear-ECG/PPG |
| title_short | A Machine Learning-Empowered System for Long-Term Motion-Tolerant Wearable Monitoring of Blood Pressure and Heart Rate With Ear-ECG/PPG |
| title_sort | machine learning empowered system for long term motion tolerant wearable monitoring of blood pressure and heart rate with ear ecg ppg |
| topic | Wearable computers blood pressure heart rate photoplethysmogram electrocardiography pulse transit time |
| url | https://ieeexplore.ieee.org/document/7933339/ |
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