Convolutional neural networks predict the onset of paroxysmal atrial fibrillation: Theory and applications

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dc.authorscopusid57296901000
dc.authorscopusid6504389809
dc.authorscopusid6505878570
dc.authorscopusid35558579000
dc.contributor.authorSurucu, M.
dc.contributor.authorIsler, Y.
dc.contributor.authorPerc, M.
dc.contributor.authorKara, R.
dc.date.accessioned2021-12-01T18:38:52Z
dc.date.available2021-12-01T18:38:52Z
dc.date.issued2021
dc.department[Belirlenecek]en_US
dc.description.abstractIn this study, we aimed to detect paroxysmal atrial fibrillation episodes before they occur so that patients can take precautions before putting their and others' lives in potentially life-threatening danger. We used the atrial fibrillation prediction database, open data from PhysioNet, and assembled our process based on convolutional neural networks. Conventional heart rate variability features are calculated from time-domain measures, frequency-domain measures using power spectral density estimations, time-frequency-domain measures using wavelet transform, and nonlinear Poincaré plot measures. In addition, we also applied an alternative heart rate normalization, which gave promising results only in a few studies, before calculating these heart rate variability features. We used these features directly and their normalized versions using min-max normalization and z-score normalization methods. Thus, heart rate variability features extracted from six different combinations of these normalizations, in addition to no normalization cases, were applied to the convolutional neural network classifier. We tuned the classifiers' hyperparameters using 90% of feature sets and tested the classifiers' performances using 10% of feature sets. The proposed approach resulted in 87.76% accuracy, 91.30% precision, 80.04% recall, and 87.50% f1-score in heart rate variability with z-score feature normalization. When the heart rate normalization was also utilized, the suggested method gave 100% accuracy, 100% precision, 100% recall, and 100% f1-score in heart rate variability with z-score feature normalization. The proposed method with heart rate normalization and z-score normalization methods resulted in better classification performance than similar studies in the literature. By comparing the existing studies, we conclude that our approach provides a much better tool to determine a near-future paroxysmal atrial fibrillation episode. However, although the achieved benchmarks are impressive, we note that the approach needs to be supported by other studies and on other datasets before clinical trials. © 2021 Author(s).en_US
dc.description.sponsorshipJavna Agencija za Raziskovalno Dejavnost RS, ARRS: J1-2457, P1-0403en_US
dc.description.sponsorshipM. Perc was supported by the Slovenian Research Agency (Grant Nos. P1-0403 and J1-2457).en_US
dc.identifier.doi10.1063/5.0069272
dc.identifier.issn10541500
dc.identifier.issue11en_US
dc.identifier.pmid34881615en_US
dc.identifier.scopus2-s2.0-85119059311en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1063/5.0069272
dc.identifier.urihttps://hdl.handle.net/20.500.12684/9883
dc.identifier.volume31en_US
dc.identifier.wosWOS:000721340400008en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherAmerican Institute of Physics Inc.en_US
dc.relation.ispartofChaosen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.titleConvolutional neural networks predict the onset of paroxysmal atrial fibrillation: Theory and applicationsen_US
dc.typeArticleen_US

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