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A Comprehensive Study of Complexity and Performance of Automatic Detection of Atrial Fibrillation: Classification of Long ECG Recordings Based on the PhysioNet Computing in Cardiology Challenge 2017
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.ORCID iD: 0000-0002-1313-0934
2020 (English)In: Biomedical Engineering & Physics Express, E-ISSN 2057-1976, Vol. 6, no 2, article id 025010Article in journal (Refereed) Published
Abstract [en]

Objective: The 2017 PhysioNet/CinC Challenge focused on automatic classification of atrial fibrillation (AF) in short ECGs. This study aimed to evaluate the use of the data and results from the challenge for detection of AF in longer ECGs, taken from three other PhysioNet datasets.

Approach: The used data-driven models were based on features extracted from ECG recordings, calculated according to three solutions from the challenge. A Random Forest classifier was trained with the data from the challenge. The performance was evaluated on all non-overlapping 30 s segments in all recordings from three MIT-BIH datasets. Fifty-six models were trained using different feature sets, both before and after applying three feature reduction techniques.

Main Results: Based on rhythm annotations, the AF proportion was 0.00 in the MIT-BIH Normal Sinus Rhythm (N = 46083 segments), 0.10 in the MIT-BIH Arrhythmia (N = 2880), and 0.41 in the MIT-BIH Atrial Fibrillation (N = 28104) dataset. For the best performing model, the corresponding detected proportions of AF were 0.00, 0.11 and 0.36 using all features, and 0.01, 0.10 and 0.38 when using the 15 best performing features.

Significance: The results obtained on the MIT-BIH datasets indicate that the training data and solutions from the 2017 Physionet/Cinc Challenge can be useful tools for developing robust AF detectors also in longer ECG recordings, even when using a low number of carefully selected features. The use of feature selection allows significantly reducing the number of features while preserving the classification performance, which can be important when building low-complexity AF classifiers on ECG devices with constrained computational and energy resources.

Place, publisher, year, edition, pages
Institute of Physics Publishing (IOPP), 2020. Vol. 6, no 2, article id 025010
Keywords [en]
atrial fibrillation detection, computational complexity, feature selection, 2017 PhysioNet/CinC Challenge
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:umu:diva-174283DOI: 10.1088/2057-1976/ab6e1eISI: 000525707800001Scopus ID: 2-s2.0-85081976560OAI: oai:DiVA.org:umu-174283DiVA, id: diva2:1459723
Available from: 2020-08-20 Created: 2020-08-20 Last updated: 2023-03-24Bibliographically approved

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Wiklund, Urban

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