A closed-loop system for artifact mitigation in ambulatory electrocardiogram monitoring

Motion artifacts interfere with electrocardiogram (ECG) detection and information processing. In this paper, we present an independent component analysis based technique to mitigate these signal artifacts. We propose a new statistical measure to enable an automatic identification and removal of independent components, which correspond to the sources of noise. For the first time, we also present a signal-dependent closed-loop system for the quality assessment of the denoised ECG. In one experiment, noisy data is obtained by the addition of calibrated amounts of noise from the MIT-BIH NST database to the AHA ECG database. Arrhythmia classification based on a state-of-the-art algorithm with the direct use of noisy data thus obtained shows sensitivity and positive predictivity values of 87.7% and 90.0%, respectively, at an input signal SNR of -9 dB. Detection with the use of ECG data denoised by the proposed approach exhibits significant improvement in the performance of the classifier with the corresponding results being 96.5% and 99.1%, respectively. In a related lab trial, we demonstrate a reduction in RMS error of instantaneous heart rate estimates from 47.2% to 7.0% with the use of 56 minutes of denoised ECG from four physically active subjects. To validate our experiments, we develop a closed-loop, ambulatory ECG monitoring platform, which consumes 2.17 mW of power and delivers a data rate of 33 kbps over a dedicated UWB link.