Background: The Electrocardiogram (ECG) signals are usually used to detect and monitor human health. However, the Electromyogram (EMG) artifact also can be obtained during measurement, these make difficult for doctors in correct diagnosis. In general, the ECG signal is periodic while EMG artifact is non-stationary and has overlapped with it under the frequency domain. According to these characteristics, it is necessary to extract clean ECG signals from noisy EMG artifact signals by using the periodic separation method.
Method: A novel Adaptive Periodic Segment Matrix (APSM) based on Singular Value Decomposition (SVD) is proposed for extracting clean ECG from EMG artifact. 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 its position 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 maximum singular value of the formed matrix. The validation of proposed method is made by applying the algorithm to ECG records from the MIT-BIH Arrhythmia Database. The zero-mean percent root-mean-square difference (PRD 1 ), Cross-correlation coefficient and output signal to noise ratio (SNR output ) have been calculated for presenting the algorithm performance by comparison with other methods. Finally two heart disease cases have been studied for P wave and ST segment detection under noisy ECG with EMG artifact.
Results: The proposed methods achieved significant improvement in output signal-to-noise ratio, percentage root-mean-square differences and lead to the higher value of cross-correlation coefficient between the original (clean) ECG and the denoised ECG signal. Also, the reconstructed ECG signal can be better able to follow the trend of original (clean) ECG signal under the EMG noise.
Conclusion: The proposed periodic segment estimation method can adaptively find the periodic length in ECG signal by using envelope spectrum. Also, the more strict rank one trajectory matrix has been formed in APSM by using R wave peaks and RR intervals constraint. The results show that the proposed APSM-SVD method is effective for EMG artifact removal and extracting the clean ECG signal. The R peak, P wave, QRS complex and ST segment can be preserved in the reconstructed ECG signal.