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ShanghaiTech University Knowledge Management System
Noise reduction in capacitive ECG measurements by dedicated modelling and Kalman filtering | |
2024-06 | |
会议录名称 | 2024 IEEE INTERNATIONAL SYMPOSIUM ON MEDICAL MEASUREMENTS AND APPLICATIONS |
ISSN | 2837-5874 |
发表状态 | 已发表 |
DOI | 10.1109/MeMeA60663.2024.10596907 |
摘要 | Electrocardiography (ECG) measurements have been extensively used for monitoring and diagnosing cardiovascular diseases (CVDs). Traditional ECG measured with wet electrodes may be unsuitable for long-term monitoring due to the use of gels that may cause skin problems. A capacitive electrode can measure an ECG though an isolating layer and is therefore a good alternative to wet electrode for long-term ambulatory monitoring. However, current application of capacitive ECG (cECG) focuses mainly on heart-rate analysis due to its high sensitivity to noise. In the present study, by using a dynamic ECG model, we propose an extended Kalman filter (EKF) for cECG noise removal. For evaluation, cECG signals were recorded from 8 healthy subjects and processed by the proposed method. The denoised cECG were then compared with the traditional ECG recorded simultaneously using gel electrodes using the Pearson's correlation coefficient (CC) as performance metric. To assess the quality of the detailed ECG waveform, such as P and T waves, CCs were also calculated after removing the QRS complex from both signals. Our results show EKF to produce promising results in both CCs, outperforming the state-of-the-art methods. Particularly, the observed high CC in the detailed ECG waves, i.e., 0.81 on average, indicates the feasibility of morphological analysis using cECG, enabling possible clinical application of cECG. |
关键词 | Biomedical signal processing Correlation methods Electrodes Extended Kalman filters Noise abatement Ambulatory monitoring Capacitive electrocardiography Capacitive electrodes Cardiovascular disease Dedicated modelling Dynamic electrocardiography model Isolating layer Kalman-filtering Long term monitoring Model filtering |
会议名称 | 2024 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2024 |
会议地点 | Eindhoven, Netherlands |
会议日期 | 26-28 June 2024 |
URL | 查看原文 |
收录类别 | EI |
语种 | 英语 |
EI入藏号 | 20243316885131 |
EI主题词 | Electrocardiography |
EI分类号 | 461.6 Medicine and Pharmacology ; 701.1 Electricity: Basic Concepts and Phenomena ; 716.1 Information Theory and Signal Processing ; 751.4 Acoustic Noise ; 922.2 Mathematical Statistics |
原始文献类型 | Conference article (CA) |
来源库 | IEEE |
文献类型 | 会议论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/362296 |
专题 | 信息科学与技术学院_硕士生 信息科学与技术学院_博士生 信息科学与技术学院_PI研究组_徐林组 |
共同第一作者 | Wu Yichao |
通讯作者 | Xu L(徐林) |
作者单位 | 1.School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China 2.Eindhoven University of Technology, Eindhoven, the Netherlands |
第一作者单位 | 信息科学与技术学院 |
通讯作者单位 | 信息科学与技术学院 |
第一作者的第一单位 | 信息科学与技术学院 |
推荐引用方式 GB/T 7714 | Lin Runwei,Wu Yichao,Cheng Anyi,et al. Noise reduction in capacitive ECG measurements by dedicated modelling and Kalman filtering[C],2024. |
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