A novel method based on Adaptive Periodic Segment Matrix and Singular Value Decomposition for removing EMG artifact in ECG signal. (September 2020)

• Record Type: Journal Article
• Authors: Chen, Xieqi; Lin, Jianhui; Huang, Chenguang; He, Liu
• Is Part Of: Biomedical signal processing and control. Volume 62(2020)
• Publication Date: 2020-09
• Subjects: ECG signal -- EMG artifacts -- Adaptive Periodic Segment Matrix -- Singular Value Decomposition -- Periodic segment estimation method; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2020.102060
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Proposed a novel ECG extraction method integrated Adaptive Periodic Segment Matrix (APSM) and Singular Value Decomposition (SVD). The concept of the Adaptive Periodic Segment Matrix is introduced. The quantitative and qualitative analysis have been compared in proposed method. The application in heart disease identification are analyzed by proposed method. Abstract: The Electrocardiogram (ECG) signals are usually used to detect and monitor human health. However, the Electromyogram (EMG) artifacts also can be obtained during measurement, these make difficult for doctors in correct diagnosis. In general, ECG signals are periodic while EMG artifacts are non-stationary and overlapped in the frequency domain. According to these characteristics, it is necessary to extract clean ECG signals from EMG artifacts by using the periodic separation method. A novel Adaptive Periodic Segment Matrix (APSM) based on Singular Value Decomposition (SVD) is proposed for extracting clean ECG signals from EMG artifacts. Firstly, a periodic segment estimation method is proposed by obtaining an average periodic length and RR intervals constraint via envelope spectrum of the measured signal. Secondly, the R wave peaks and their positions of the ECG signals are detected by these. After that, APSM with rank one is formed using R wave peaks and the calculated RR intervals constraint, then SVD is processed on this matrix, the restructured ECG signals will be obtained by the first maximal singular value of the formed matrix. The validation of proposed method is made by applying the algorithm to ECG records from different four databases. Quantitative and qualitative analyses have been made and compared with other methods. The results show that the proposed APSM-SVD method is effective for EMG artifacts removal and clean ECG signals extraction. The R peak, P wave, QRS complex and ST segment can be preserved in reconstructed ECG signals.