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Robust Methods for Automated Selection of Cardiac Signals After Blind Source Separation
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2018 (English)In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 65, no 10, p. 2248-2258Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018. Vol. 65, no 10, p. 2248-2258
Keywords [en]
Biomedical signal processing, blind source separation, cardiac signals, electrocardiogram, dependent component analysis, permutation indeterminacy, sparse coding, spatio-temporal processing
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:umu:diva-152393DOI: 10.1109/TBME.2017.2788701ISI: 000445233200013PubMedID: 29993470OAI: oai:DiVA.org:umu-152393DiVA, id: diva2:1254139
Funder
Swedish Research Council, 2015-04677Available from: 2018-10-08 Created: 2018-10-08 Last updated: 2018-10-08Bibliographically approved

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Wiklund, Urban

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