DSD-R: Deep Learning Based Segmentation for the Detection of R peaks in ECG Signals

Basit Öğe Kaydı
dc.contributor.authorCelik, Eyupcan
dc.contributor.authorEren, Furkan
dc.contributor.authorSahin, Murat
dc.contributor.authorKilinc, Deniz
dc.contributor.authorErdemir, Fatih
dc.contributor.authorDen Engelsman, Robert
dc.contributor.authorGullu, Mehmet Kemal
dc.date.accessioned2023-03-22T19:47:17Z
dc.date.available2023-03-22T19:47:17Z
dc.date.issued2022
dc.departmentBelirleneceken_US
dc.descriptionMedical Technologies Congress (TIPTEKNO) -- OCT 31-NOV 02, 2022 -- Antalya, TURKEYen_US
dc.description.abstractElectrocardiography (ECG) is the recording of the electrical activity of the heart through electrodes. ECG signals are crucial in the early diagnosis of numerous cardiac diseases. Therefore, it is very important to read and analyze these signals using state-of-the-art technologies. The regular wave shapes in ECG data are frequently disturbed when certain heart diseases occur and these changes in signals help for detecting the disease. Signal processing and machine learning-based methods are widely used for this purpose. In recent years, deep learning-based methods have become widespread, and they offer promising results. This study aims to segmentation-based detection of R-peak locations in ECG signals. First, the ECG signal is transformed into a Continuous Wavelet Transform (CWT) based scalogram image, and then U-Net-based deep learning architectures are utilized for the segmentation. The comparisons are carried out on MIT-BIH Arrhythmia Database (MIT-DB). Whereas all methods provide promising results, U-Net 3+ model achieves 0.99 in Precision, 0.98 in Recall, 0.99 in F1 score, and 0.98 in Accuracy with the lowest parameter size.en_US
dc.description.sponsorshipBiyomedikal Klinik Muhendisligi Dernegi,Izmir Ekonomi Univen_US
dc.identifier.doi10.1109/TIPTEKNO56568.2022.9960181
dc.identifier.isbn978-1-6654-5432-2
dc.identifier.scopus2-s2.0-85144058639en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/TIPTEKNO56568.2022.9960181
dc.identifier.urihttps://hdl.handle.net/20.500.14034/590
dc.identifier.wosWOS:000903709700036en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.journal2022 Medical Technologies Congress (Tiptekno'22)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectECGen_US
dc.subjectScalogramen_US
dc.subjectSegmentationen_US
dc.titleDSD-R: Deep Learning Based Segmentation for the Detection of R peaks in ECG Signalsen_US
dc.typeConference Objecten_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu