Impact Analysis of Body Movement in Ambulatory ECG

Abstract

Ambulatory ECG analysis is adversely affected by motion artifacts induced due to body movements. Knowledge of the extent of motion artifacts facilitates better ECG analysis. In [1], an unsupervised method using recursive principal component analysis (RPCA) was used to detect transitions between body movements. In this paper, we endeavour to quantify the impact of various types of body movements on the extent of ECG motion artifact using the RPCA error signal. For this purpose, acceleration data from different body parts i.e. arm(s), leg and waist, have been obtained using commercially available motion sensors, in conjunction with ECG signal, while carrying out routine body movement activities like climbing stairs, walking, twisting, and arm movements, at three different intensity levels: slow, medium and fast. The acceleration magnitudes and the RPCA error sequence are found to be well correlated, thus validating the body movement impact analysis, and also indicating the suitability of the method for quantification of body movement kinematics from the ECG signal itself in the absence of any accelerometer sensors.