Diagnosis of paroxysmal atrial fibrillation from thirty-minute heart rate variability data using convolutional neural networks

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dc.contributor.authorSurucu, Murat
dc.contributor.authorIsler, Yalcin
dc.contributor.authorKara, Resul
dc.date.accessioned2021-12-01T18:49:00Z
dc.date.available2021-12-01T18:49:00Z
dc.date.issued2021
dc.department[Belirlenecek]en_US
dc.description.abstractParoxysmal atrial fibrillation (PAF) is the initial stage of atrial fibrillation, one of the most common arrhythmia types. PAF worsens with time and affects the patient's life quality negatively. In this study, we aimed to diagnose PAF early, so patients can start taking precautions before this disease gets worse. We used the atrial fibrillation prediction database, an open data from Physionet and constructed our approach using convolutional neural networks. Heart rate variability (HRV) features are calculated from time-domain measures, frequency-domain measures using power spectral density estimations (fast Fourier transform, Lomb-Scargle, and Welch periodogram), time-frequency-domain measures using wavelet transform, and nonlinear Poincare plot measures. We also normalized these features using min-max normalization and z-score normalization methods. In addition, we also applied alternatively the heart rate normalization (HRN), which gave promising results in a few HRV-based research, before calculating these features. Thus, HRV data, HRN data, and HRV features extracted from six different combinations of these normalizations, in addition to no normalization cases, were applied to the convolutional neural networks classifier. We tuned the classifiers using 90% of samples and tested the classifiers' performances using 10% of data. The proposed approach resulted in 95.92% accuracy, 100% precision, 91.84% recall, and 95.74% f1-score in HRV with z-score feature normalization. When the heart rate normalization was also applied, the proposed approach reached 100% accuracy, 100% precision, 100% recall, and 100% f1-score in HRV 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. In addition, although deep learning models offer no use of separate feature extraction processes, this study reveals that using HRV-specific feature extraction techniques may improve the performance of deep learning algorithms in HRV-based studies. Comparing the existing studies, we concluded that our approach provides a much better tool to diagnose PAF patients.en_US
dc.identifier.doi10.3906/elk-2105-92
dc.identifier.endpage2900en_US
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.scopus2-s2.0-85117118593en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage2886en_US
dc.identifier.urihttps://doi.org/10.3906/elk-2105-92
dc.identifier.urihttps://hdl.handle.net/20.500.12684/10646
dc.identifier.volume29en_US
dc.identifier.wosWOS:000709712800008en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTubitak Scientific & Technical Research Council Turkeyen_US
dc.relation.ispartofTurkish Journal Of Electrical Engineering And Computer Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDeep learningen_US
dc.subjectconvolutional neural networksen_US
dc.subjectparaoxysmal atrial fibrillationen_US
dc.subjectheart rate variabilityen_US
dc.subjectclassificationen_US
dc.subjectPoincare Ploten_US
dc.subjectFrequency-Analysisen_US
dc.subjectSpectral-Analysisen_US
dc.subjectWavelet Entropyen_US
dc.subjectTime-Seriesen_US
dc.subjectHrven_US
dc.subjectPredictionen_US
dc.subjectEcgen_US
dc.subjectFeaturesen_US
dc.subjectPerformanceen_US
dc.titleDiagnosis of paroxysmal atrial fibrillation from thirty-minute heart rate variability data using convolutional neural networksen_US
dc.typeArticleen_US

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