Adaptive Modulation Spectral Filtering For Improved Electrocardiogram Quality Enhancement

Advances in portable electrocardiogram (ECG) monitoring devices has allowed for new cardiovascular applications to emerge beyond diagnostics, such as stress detection, sleep disorder characterization, mood recognition, activity surveillance, or fitness monitoring, to name a few. Such devices, however, are prone to artifacts, particularly due to movement, thus rendering heart rate and heart rate variability (HRV) metrics useless. To address this issue, this paper proposes a new ECG quality enhancement algorithm based on filtering in the so-called spectro-temporal (or modulation spectral) domain. Our experiments show that this new signal representation accurately separates ECG signal and noise components, thus allowing for adaptive filtering to improve signal quality even in extremely noisy settings. Experimental results show the proposed algorithm outperforming a state-of-the-art wavelet-based enhancement algorithm in terms of signal-to-noise ratio improvement, as well as ECG kurtosis; the latter has been widely used in the literature as an ECG quality index. The obtained findings suggest that the proposed algorithm can be used to enhance the quality of wearable ECG monitors even in extreme conditions, thus can play a key role in athletic peak performance training/monitoring.