An Improved Adaptive Periodical Segment Matrix for Processing EMG Artifacts in ECG Signal Detection

Abstract

This paper proposed a new ECG denoising approach based on singular value decomposition (SVD) for EMG (electromyogram) artifacts reduction. Unlike the traditional method like discrete wavelet transform and classical bandpass filter weakly noise reduction performance by frequency domain overlapping, we propose to remove EMG by an improved adaptive matrix construction and achieve high signal to noise ratio (SNR), the ECG signal were firstly spitted a number of heartbeats to construct the trajectory matrix; then the trajectory matrix was decomposed by SVD; at last, the decision rules used to reconstruct the clear signal, the proposed method is evaluated on the MIT-BIH arrhythmia database, the result shows our method attain a high improvement of signal to noise ratio output (SNR) and lower signal distortion.