Comparative Analysis of PCA and Wavelet based Motion Artifact Detection and Spectral Characterization in W-ECG

The use of wearable ECG recorders is becoming common nowadays for the people suffering from cardiac disorders. Although it is a convenient option for hospitalization, it has an inherent drawback of recorded ECG being contaminated by motion artifacts due to various body movement activities of the wearer. In this paper, the spectral characteristics of motion artifacts occurring in wearable ECG (W-ECG) signals have been studied using principal component analysis (PCA) and wavelet transform. The residuals of PCA and wavelet transform characterize the spectral behaviour of the motion artifacts occurring in WECG signals. The ECG signals have been acquired from Biopac MP-36 system and a self-developed wearable ECG recorder. The performance is evaluated by power spectral density (PSD) plots of PCA residual errors as well as statistical parameters like mean, median and variance of PCA and wavelet residuals. The PSD plots indicate that the peak frequency of the motion artifacts occurring due to various body movements (like left arm up-down, right arm up-down, left and right legs up-down, waist twist, walking and sitting up-down) is located around 5-15 Hz, coinciding with the ECG spectrum. Key-Words: Wearable ECG (W-ECG), PCA, Wavelet transform, Motion artifacts, PSD