A unified sparse signal decomposition and reconstruction framework for elimination of muscle artifacts from ECG signal

Abstract

Removal of muscle artifacts from the ECG signals is crucial for a reliable and accurate measurement of local features of ECG signals. In this paper, we present an automatic method for removal of muscle artifacts from ECG signals, based on four steps: decomposing ECG signal using sparse signal decomposition on mixed dictionaries; obtaining QRS complex signal; determining time-instants of R-peak; and removal of muscle artifacts from ECG signal. The noise reduction performance of the proposed method is tested and validated using ECG signals taken from a standard MIT-BIH Arrhythmia database. The reconstructed signals are assessed using both subjective quality assessment test and objective quality assessment metrics. Performance evaluation results show that the proposed method outperforms other existing ECG denoising methods inadequately removing the muscle artifacts without significantly distorting the morphologies of P-wave, QRS-complex and T-wave of the ECG signals.