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Motion Artifacts in Capacitive ECG Monitoring Systems: A Review of Existing Models and Reduction Techniques

aut.relation.journalMedical and Biological Engineering and Computing
dc.contributor.authorKhalili, Matin
dc.contributor.authorGholamHosseini, Hamid
dc.contributor.authorLowe, Andrew
dc.contributor.authorKuo, Matthew MY
dc.date.accessioned2024-07-22T22:24:03Z
dc.date.available2024-07-22T22:24:03Z
dc.date.issued2024-07-20
dc.description.abstractCurrent research focuses on improving electrocardiogram (ECG) monitoring systems to enable real-time and long-term usage, with a specific focus on facilitating remote monitoring of ECG data. This advancement is crucial for improving cardiovascular health by facilitating early detection and management of cardiovascular disease (CVD). To efficiently meet these demands, user-friendly and comfortable ECG sensors that surpass wet electrodes are essential. This has led to increased interest in ECG capacitive electrodes, which facilitate signal detection without requiring gel preparation or direct conductive contact with the body. This feature makes them suitable for wearables or integrated measurement devices. However, ongoing research is essential as the signals they measure often lack sufficient clinical accuracy due to susceptibility to interferences, particularly Motion Artifacts (MAs). While our primary focus is on studying MAs, we also address other limitations crucial for designing a high Signal-to-Noise Ratio (SNR) circuit and effectively mitigating MAs. The literature on the origins and models of MAs in capacitive electrodes is insufficient, which we aim to address alongside discussing mitigation methods. We bring attention to digital signal processing approaches, especially those using reference signals like Electrode-Tissue Impedance (ETI), as highly promising. Finally, we discuss its challenges, proposed solutions, and offer insights into future research directions.
dc.identifier.citationMedical and Biological Engineering and Computing, ISSN: 0140-0118 (Print); 1741-0444 (Online), Springer. doi: 10.1007/s11517-024-03165-1
dc.identifier.doi10.1007/s11517-024-03165-1
dc.identifier.issn0140-0118
dc.identifier.issn1741-0444
dc.identifier.urihttp://hdl.handle.net/10292/17809
dc.languageeng
dc.publisherSpringer
dc.relation.urihttps://link.springer.com/article/10.1007/s11517-024-03165-1
dc.rightsOpen Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
dc.rights.accessrightsOpenAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectAdaptive filtering
dc.subjectDigital signal processing
dc.subjectECG capacitive electrodes
dc.subjectElectrode-tissue impedance
dc.subjectMotion artifact
dc.subjectAdaptive filtering
dc.subjectDigital signal processing
dc.subjectECG capacitive electrodes
dc.subjectElectrode-tissue impedance
dc.subjectMotion artifact
dc.subject0903 Biomedical Engineering
dc.subject0906 Electrical and Electronic Engineering
dc.subjectBiomedical Engineering
dc.subject4003 Biomedical engineering
dc.subject4603 Computer vision and multimedia computation
dc.subject4611 Machine learning
dc.titleMotion Artifacts in Capacitive ECG Monitoring Systems: A Review of Existing Models and Reduction Techniques
dc.typeJournal Article
pubs.elements-id562952

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