Novel Approach for Automatic Detection of Atrial Fibrillation Based on Inter Beat Intervals and Support Vector Machine

Rasmus S. Andersen, Erik S. Poulsen, Sadasivan Puthusserypady

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Atrial fibrillation (AF) is the most common cardiac arrhythmia associated with a major economic burden for the society. Automatic detection of AF in long term recordings can efficiently assist in early diagnosis and management of comorbidities associated with AF. This study presents a novel approach for AF detection based on Inter Beat Intervals (IBI) extracted from long term electrocardiogram (ECG) recordings. Five time-domain features are extracted from the IBIs and a Support Vector Machine (SVM) is used for classification. The results are compared to a state of the art algorithm based on raw ECG. Both algorithms are evaluated on the MIT-BIH Atrial Fibrillation database resulting in equally high classification performance (Sensitivity≥ 95%). The proposed approach requires detection of R-peaks in the ECG signal but allows for significantly reduced computation time without loss of performance.
Original languageEnglish
Title of host publicationProceedings of 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
PublisherIEEE
Publication date2017
Pages2039-2042
ISBN (Print)978-1-5090-2809-2
DOIs
Publication statusPublished - 2017
Event39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - International Convention Center, Jeju Island, Jeju, Korea, Republic of
Duration: 11 Jul 201715 Jul 2017

Conference

Conference39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
LocationInternational Convention Center, Jeju Island
CountryKorea, Republic of
CityJeju
Period11/07/201715/07/2017

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