Key result
Heartbeat-detection-based selection routines significantly outperformed standard selectors based on Skewness, Kurtosis, and frequency-domain features for selecting cardiac signals after blind source separation in datasets with motion artifacts.
Heartbeat-detection-based component selection routines reliably identify cardiac signals after spatio-temporal blind source separation, facilitating robust minimum-contact vital signs monitoring.
May enhance post-BSS cardiac signal selection amid motion artifacts; leaves open prospective validation for minimum-contact monitoring.
OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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. SIGNIFICANCE: 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.
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Wedekind et al. (2018) studied ECG signal processing (healthy and arrhythmia) (n=68). Heartbeat-detection-based selection routines (RCODE, PeriodTest) vs. Standard selectors (Skewness, Kurtosis, CASCSEL) was evaluated on Heartbeat detection accuracy (ACC) of the selected single component. Heartbeat-detection-based selection routines significantly outperformed standard selectors based on Skewness, Kurtosis, and frequency-domain features for selecting cardiac signals after blind source separation in datasets with motion artifacts.
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