Motion artifact removal using cascade adaptive filtering for ambulatory ECG monitoring system

Abstract

A motion artifact removal method with a two-stage cascade LMS adaptive filter is proposed for an ambulatory ECG monitoring system. The first LMS stage consisting of analog feedback prevents the signal saturation to reduce the input dynamic range. An adaptive step-size LMS algorithm is introduced and employed for the second LMS stage. The adaptive step-size algorithm can achieve fast convergence to track large sudden motion artifact quickly, while preventing the distortion of the ECG component. The filtering performance is evaluated by the heart beat detection, measured by sensitivity (Se) and positive predictive value (+p), and the performance is increased by 9.8% and 6.48%, respectively, compared to the unfiltered signal at the worst case with -25dB SNR. The proposed motion artifact method is implemented on an ambulatory ECG monitoring module, and the real-time measurement shows a significant performance improvement.