CEEMDAN-IMFx-PCA-CICA: an improved single-channel blind source separation in multimedia environment for motion artifact
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ORIGINAL ARTICLE
CEEMDAN-IMFx-PCA-CICA: an improved single-channel blind source separation in multimedia environment for motion artifact reduction in ambulatory ECG Fan Xiong1 · Dongyi Chen1 Received: 16 June 2020 / Accepted: 14 August 2020 © The Author(s) 2020
Abstract Long-term monitoring of ECG via wearable monitoring systems has already been widely adopted to detect and prevent heart diseases. However, one of the main issues faced by wearable ECG monitoring systems is that motion artifacts significantly affect the systems’ stability and reliability. Therefore, motion artifact reduction is a very challenging task in filtering and processing physiological signals. Based on the existing algorithms and ECG prior knowledge, in this paper, we propose an algorithm, CEEMDAN-IMFx-PCA-CICA, for motion artifact reduction in ambulatory ECG signals using single-channel blind source separation technique. Our algorithm first utilizes CEEMDAN to decompose the mixed signals into IMFs (intrinsic mode function) containing different source signal features, thereby forming new multi-dimensional signals. Using the correlation between IMFx (IMF component with the most ECG features) and each IMF, and PCA are then applied to reduce the dimension of each IMF. Finally, the blind separation of the source ECG signals is achieved by using CICA with IMFx as the constraint reference component. The results of our experiments indicate that our algorithm outperformed CEEMDANCICA, CEEMDAN-PCA-CICA, and improved CEEMDAN-PCA-CICA. Besides, the number of iterations of the CICA is significantly reduced; the separated source signal is better; the obtained result is stable. Furthermore, the separated ECG signal has a higher correlation with the source ECG signal and a lower RRMSE, especially in the case of high noise-to-signal ratios. Keywords Empirical mode decomposition · Motion artifact · Single-channel blind source separation · Wearable ECG monitoring system
Introduction Electrocardiogram (ECG) records a significant amount of relevant information, such as heart health status, heart rate variability, psycho-physiologic status, and so forth. It often serves as one of the main bases for diagnosing cardiac diseases [1]. Long-term and dynamic monitoring of early heart diseases or sudden heart attacks can capture transient, non-sustained, abnormal ECG changes that are critical for diagnosing heart diseases, evaluating therapeutic effects, and saving lives [2]. Therefore, long-term routine ECG monitoring is a vital way for diagnosing and controlling
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Dongyi Chen [email protected] School of Automation Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China
heart diseases [3]. Since wearable ECG monitoring systems enable long-term, dynamic, unobtrusive, and unrestrictive ECG monitoring to use gel-free fabric dry electrodes, they are suitable for ubiquitous ambulatory ECG monitoring. In particular, wearable single-lead ECG monitoring systems, e.g. chest straps, vest
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