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

Xieqi Chen,Jianhui Lin,Chenguang Huang,Liu He

doi:10.1016/j.bspc.2020.102060

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.

Highlights

The Electrocardiogram (ECG) signals are usually used to detect and monitor human health
This causes the baselines of reconstructed results in Discrete Wavelet Transform (DWT) and Singular Spectrum Analysis (SSA) are similar to the noisy ECG signal, and cannot detect the actual baseline of the clean ECG signal.The reconstructed signal in Periodic Segment Matrix (PSM)-Singular Value Decomposition (SVD) has a phase shift compared with Adaptive Periodic Segment Matrix (APSM)-SVD.But the effect of baseline shift is better than other methods
A new periodic segment estimation method and an Adaptive Periodic Segment Matrix based on SVD has been proposed to extract the clean ECG signal in noisy ECG signal with EMG artifact

Summary

Introduction

The Electrocardiogram (ECG) signals are usually used to detect and monitor human health. The measured signal contains the artifacts like electrode contact noise, muscle contraction interference, baseline wander, and some instrumental noise generated by the ECG collecting device[3]. All of these can corrupt the ECG and lead to a wrong diagnosis and identification. Statistical techniques such as Principal Component Analysis[4], Independent Component Analysis[5], and Neural Networks[6] have been used to extract a relatively noise-free signal from noisy ECG. Those techniques cannot reduce some specific noise in ECG signal

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Results

Discussion

Conclusion