Motion artifact removal based on periodical property for ECG monitoring with wearable systems

Motion artifact removal (MR) is one of the essential issues in processing raw ECG signals since it could not be simply solved by using classic filtering. In this paper, a QRS detection based Motion Artifact Removal algorithm (QRSMR) is proposed. The proposed method detects the entire QRS complex and removes the noise between two QRS complexes, while recovering P and T-waves. As verified in the tests on simulated noisy ECG signals, QRSMR outputs with seriously contaminated ECG signals have an increase of the correlation with their original clean signals from 40% to nearly 80%, demonstrating the improved noise removal ability of QRSMR. Moreover, in the tests on real ECG signals measured on volunteers with a flexible wearable ECG monitoring device developed at Fudan University, QRSMR is able to recover P-wave and T-wave from the contaminated signal, which shows its enhanced performance on motion artifact reduction comparing with adaptive filtering method and other methods based only on empirical mode decomposition.