A novel approach for automatic detection of Atrial Fibrillation based on Inter Beat Intervals and Support Vector Machine

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...

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Bibliographic Details
Main Authors: Andersen, Rasmus S., Poulsen, Erik S., Puthusserypady, Sadasivan
Format: Conference Proceeding
Language:English
Subjects:
Electrocardiography
Entropy
Feature extraction
Heart rate variability
Rail to rail inputs
Support vector machines
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Summary: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.
ISSN:1557-170X
2694-0604
DOI:10.1109/EMBC.2017.8037253