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Patient-Specific Electrocardiogram Monitoring by Model-Based Stochastic Anomaly Detection
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology. (Embedded systems)ORCID iD: 0000-0002-9945-2650
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.ORCID iD: 0000-0002-6608-250x
2020 (English)In: 2020 European Control Conference (ECC), 2020, p. 735-740Conference paper, Published paper (Refereed)
Abstract [en]

A novel model-based method for patient-specific detection of deformed electrocardiogram (ECG) beats is proposed and tested. Five parameters of a patient-specific nonlinear ECG model are estimated from data by nonlinear least-squares optimization. The normal variability of the model parameters is captured by estimated probability density functions. A binary classifier, based on stochastic anomaly detection methods, along with a pre-tuned classification threshold, is employed for detecting the abnormal ECG beats. We demonstrate the utility of the proposed approach by validating it on annotated arrhythmia data recorded under clinical conditions.

Place, publisher, year, edition, pages
2020. p. 735-740
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:uu:diva-437321DOI: 10.23919/ECC51009.2020.9143590ISI: 000613138000129ISBN: 978-3-90714-402-2 (electronic)ISBN: 978-1-7281-8813-3 (print)OAI: oai:DiVA.org:uu-437321DiVA, id: diva2:1536339
Conference
2020 European Control Conference (ECC), 12-15 May, St. Petersburg, Russia
Available from: 2021-03-10 Created: 2021-03-10 Last updated: 2023-10-31Bibliographically approved

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Albaba, AdnanMedvedev, Alexander

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