Motion Artifact Removal in Ambulatory ECG Signal for Heart Rate Variability Analysis

Abstract

This work presents an efficient method for motion artifact removal from ambulatory electrocardiogram (ECG) signal for heart rate variability (HRV) in wearable/portable healthcare devices. HRV is the fluctuation in the time interval between the adjacent heartbeats. Motion artifacts affect HRV analysis by creating some outliers. A two-phase method using stationary wavelet transform with level thresholding (SWT-LT) is used to remove motion artifact from the ECG signal. Multi-channel system prototype is used for ambulatory ECG signal recording which is developed using commercial integrated circuit components. Motion artifact affected ECG signals are recorded by emulating daily activity movements. Recorded ECG database (60 signals) and Motion Artifact Contaminated ECG Database (27 signals) are used for validation of the proposed SWT-LT method. Implemented results show that the proposed SWT-LT method removes various in-band motion artifacts efficiently with an average correlation coefficient of 0.9337 and an average normalized mean square error of 0.012 which are better than the other reported methods. The proposed method has shown improvement in features of HRV analysis by removing outliers due to motion artifact from the ECG signal which is verified using MATLAB app HRVTool 1.03 developed by Marcus Vollmer.