A novel method based on shifted rank-1 reconstruction for removing EMG artifacts in ECG signals

Abstract

Electrocardiogram (ECG) signal is an essential feature of human health monitoring and detection. Electromyogram (EMG) artifacts will be involved as the noise distortion and make it difficult for the doctor’s diagnosis. By comparing with the characteristics of these two signals, ECG signals have periodicity while EMG artifacts are random. However, they overlap in the frequency domain. According to the periodicity of ECG signals, several methods of extracting ECG signals from EMG artifacts are proposed. The core of ECG extraction is to form a rank-1 trajectory matrix for reconstruction. However, due to the EMG artifacts, most formed matrices are not strict rank-1 matrices, and the length of traversal segment selected for pure ECG reconstructions is not easily determined. Therefore, a new ECG extraction method based on shifted rank-1 reconstruction is proposed. Given an approximated traversal segment length, the trajectory matrix is constructed. Through the optimization of lambda in the low rank matrix, the constructed approximation matrix is approximated by shift vectors to obtain a strict rank-1 matrix. Pure ECG signals can be reconstructed via the singular values of shifted matrix. The validation of the proposed method is made by applying the algorithm to ECG records from four different databases. The quantitative and qualitative analysis are carried out and compared with other methods. The results indicate that the proposed SR1 method can remove EMG artifacts and does not require any specific traversal segment lengths to extract clean ECG signals. The QRS complexes and ST segments can be retained in reconstructed ECG signals.