Atrial Fibrillation Detection with Spectrogram and Convolutional Neural Networks

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dc.contributor.authorKandıralı, Çağrı
dc.contributor.author?zkurt, Nalan
dc.contributor.authorDedebağı, Nurbanu
dc.contributor.authorŞimşek, Evrim
dc.date.accessioned2025-09-28T09:32:39Z
dc.date.available2025-09-28T09:32:39Z
dc.date.issued2024
dc.descriptionIEEE SMC; IEEE Turkiye Section
dc.description2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024-- -- Ankara -- 204562
dc.description.abstractAtrial fibrillation (AF) is one of the most common heart arrhythmias and can lead to various complications such as heart failure, stroke, reduced exercise capacity, palpitations, anxiety, shortness of breath, and high blood pressure if not diagnosed promptly. In this study, we investigated the application of time-frequency domain techniques and artificial intelligence tools for the diagnosis of AF. We proposed two custom-designed Convolutional Neural Network (CNN) architecture. 24-hour Holter ECG records from patients with AF and control subjects from the Cardiology Department of Ege University were used as dataset. Ten seconds of ECG time series signals were employed to train a 1D CNN, while spectrogram images created from these signals were used to train a 2D CNN. We observed that the proposed spectrogram-2D CNN outperformed the 1D CNN, benefiting from the time-frequency information extracted by the spectrogram. © 2024 Elsevier B.V., All rights reserved.
dc.identifier.doi10.1109/ASYU62119.2024.10757051
dc.identifier.isbn9798350379433
dc.identifier.scopus2-s2.0-85213397789
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/11454/119994
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzScopus_20250927
dc.subjectAtrial Fibrillation; Convolutional Neural Network; Ecg; Spectrogram; Cardiology; Convolutional Neural Networks; Diseases; Electrocardiography; Frequency Domain Analysis; Heart; Spectrographs; Artificial Intelligence Tools; Atrial Fibrillation; Convolutional Neural Network; Heart Arrhythmias; Heart Failure; High Blood Pressures; Holter Ecg; Neural Network Architecture; Spectrograms; Time-frequency Domain Technique; Blood Pressure
dc.titleAtrial Fibrillation Detection with Spectrogram and Convolutional Neural Networks
dc.typeConference Object

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