Adaptive spatio-temporal filtering of disturbed ECGs: a multi-channel approach to heartbeat detection in smart clothing

Abstract

Intermittent disturbances are common in ECG signals recorded with smart clothing: this is mainly because of displacement of the electrodes over the skin. We evaluated a novel adaptive method for spatio-temporal filtering for heartbeat detection in noisy multi-channel ECGs including short signal interruptions in single channels. Using multi-channel database recordings (12-channel ECGs from 10 healthy subjects), the results showed that multi-channel spatio-temporal filtering outperformed regular independent component analysis. We also recorded seven channels of ECG using a T-shirt with textile electrodes. Ten healthy subjects performed different sequences during a 10-min recording: resting, standing, flexing breast muscles, walking and pushups. Using adaptive multi-channel filtering, the sensitivity and precision was above 97% in nine subjects. Adaptive multi-channel spatio-temporal filtering can be used to detect heartbeats in ECGs with high noise levels. One application is heartbeat detection in noisy ECG recordings obtained by integrated textile electrodes in smart clothing.

Sparse Coding of Cardiac Signals for Automated Component Selection after Blind Source Separation

“…Using measurement techniques like textile electrodes for wearable sensing, the recorded electrocardiogram (ECG) is of non-standard nature compared to its clinical counterpart. Moreover, the minimal-conductive measurement principle which allows for a flexible health monitoring is also strongly affected by movement artifacts [1].…”

“…the ECG). Again, higher-order statistics have been utilized in [1,7]. Moreover, template matching was applied in [8].…”

“…Our novel approach aims at directly identifying ECG components in BSS outputs based on a sparse representation of each component. The sparse code itself thereby features QRS waveforms and their temporal behavior, expressed in "spike trains" typical for spatio-temporal BSS on ECG [1]. A comparison is also made with other frequently applied methods which are based on higher-order statistics.…”

“…Using measurement techniques like textile electrodes for wearable sensing, the recorded electrocardiogram (ECG) is of non-standard nature compared to its clinical counterpart. Moreover, the minimal-conductive measurement principle which allows for a flexible health monitoring is also strongly affected by movement artifacts [1].Blind Source Separation (BSS) is a signal processing technique capable of separating signal mixtures (e.g. mixtures of ECG and distortions) into its constituting components [2].…”

“…mixtures of ECG and distortions) into its constituting components [2]. Spatio-temporal Independent Component Analysis (ICA) based on the FastICA algorithm [2] is one realization of BSS which has shown a superior performance on wearable data compared to the standard ICA [1]. Despite its ability to separate ECG from distortions, ICA is typically only solved up to a permutation (i.e.…”