Abstract
Novel minimum-contact vital signs monitoring techniques like textile or capacitive electrocardiogram (ECG) provide new opportunities for health monitoring. These techniques are sensitive to artifacts and require handling of unstable signal quality. Spatio-temporal blind source separation (BSS) is capable of processing suchlike multichannel signals. However, BSS's permutation indeterminacy requires the selection of the cardiac signal (i.e., the component resembling the electric cardiac activity) after its separation from artifacts. This study evaluates different concepts for solving permutation indeterminacy. Novel automated component selection routines based on heartbeat detections are compared with standard concepts, as using higher order moments or frequency-domain features, for solving permutation indeterminacy in spatio-temporal BSS. BSS was applied to a textile and a capacitive ECG dataset of healthy subjects performing a motion protocol, and to the MIT-BIH Arrhythmia Database. The performance of the subsequent component selection was evaluated by means of the heartbeat detection accuracy (ACC) using an automatically selected single component. The proposed heartbeat-detection-based selection routines significantly outperformed the standard selectors based on Skewness, Kurtosis, and frequency-domain features, especially for datasets containing motion artifacts. For arrhythmia data, beat analysis by sparse coding outperformed simple periodicity tests of the detected heartbeats. Component selection routines based on heartbeat detections are capable of reliably selecting cardiac signals after spatio-temporal BSS in case of severe motion artifacts and arrhythmia. The availability of robust cardiac component selectors for solving permutation indeterminacy facilitates the usage of spatio-temporal BSS to extract cardiac signals in artifact-sensitive minimum-contact vital signs monitoring techniques.
Highlights
T HE telemedical management of early diagnosis and rehabilitation of diseases provides major opportunities for reorganizing an increasingly expensive healthcare system
Despite the Kurtosis selector for textile ECG (tECG) and the Skewness and Kurtosis selector for cECG data, we find highly significant ACC increases by the selected blind source separation (BSS) component compared to the average of input channels
That component selection methods based on peak detections (RCODE, PeriodTest) and their rhythm evaluation regarding potential cardiac behaviour are capable of handling spatio-temporal BSS outputs of different data nature
Summary
T HE telemedical management of early diagnosis and rehabilitation of diseases provides major opportunities for reorganizing an increasingly expensive healthcare system. The ambulatory monitoring of health and stress offers new applications for monitoring people’s wellness while performing safety-critical tasks, e.g. driving vehicles [1], [2]. Applicable ambulatory measurement techniques include drycontact and noncontact biopotential electrodes [3]. Electrode implementations like textile or polymeric electrodes for wearable sensing or capacitive electrodes for seat-integrated sensing through clothes have been successfully proven to record the electrocardiogram (ECG) [1], [2], [4]. The recorded ECG is of non-standard nature when compared to its clinical counterpart. The minimal-conductive measurement principle, which allows flexible health monitoring, is strongly affected by movement artifacts [3]. The resulting decreased coverage and accuracy of a single channel can be addressed by exploiting the redundancy of a multichannel setup [4], [5]
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