Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7170
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dc.contributor.authorNasifoğlu, Hüseyin-
dc.contributor.authorEroğul, Osman-
dc.date.accessioned2021-09-11T15:55:51Z-
dc.date.available2021-09-11T15:55:51Z-
dc.date.issued2021en_US
dc.identifier.issn0967-3334-
dc.identifier.issn1361-6579-
dc.identifier.urihttps://doi.org/10.1088/1361-6579/ac0a9c-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/7170-
dc.description.abstractObjective. In this study, we conducted a comparative analysis of deep convolutional neural network (CNN) models in predicting obstructive sleep apnea (OSA) using electrocardiograms. Unlike other studies in the literature, this study automatically extracts time-frequency features by using CNNs instead of manual feature extraction from ECG recordings. Approach. The proposed model generates scalogram and spectrogram representations by transforming preprocessed 30 s ECG segments from time domain to the frequency domain using continuous wavelet transform and short time Fourier transform, respectively. We examined AlexNet, GoogleNet and ResNet18 models in predicting OSA events. The effect of transfer learning on success is also investigated. Based on the observed results, we proposed a new model that is found more effective in estimation. In total, 152 ECG recordings were included in the study for training and evaluation of the models. Main results. The prediction using scalograms immediately 30 s before potential OSA onsets gave the best performance with 82.30% accuracy, 83.22% sensitivity, 82.27% specificity and 82.95% positive predictive value. The prediction using spectrograms also achieved up to 80.13% accuracy and 81.99% sensitivity on prediction. Per-recording classification suggested considerable results with 91.93% accuracy for prediction of OSA events. Significance. Time-frequency deep features of scalograms and spectrograms of ECG segments prior to OSA events provided reliable information about the possible events in the future. The proposed CNN model can be used as a good indicator to accurately predict OSA events using ECG recordings.en_US
dc.language.isoenen_US
dc.publisherIOP Publishing Ltden_US
dc.relation.ispartofPhysiological Measurementen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectpredictionen_US
dc.subjectobstructive sleep apnea (OSA)en_US
dc.subjectelectrocardiogram (ECG)en_US
dc.subjectscalogramen_US
dc.subjectspectrogramen_US
dc.subjectconvolutional neural network (CNN)en_US
dc.titleObstructive Sleep Apnea Prediction From Electrocardiogram Scalograms and Spectrograms Using Convolutional Neural Networksen_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Engineering, Department of Biomedical Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Biyomedikal Mühendisliği Bölümütr_TR
dc.identifier.volume42en_US
dc.identifier.issue6en_US
dc.authorid0000-0002-4640-6570-
dc.authorid0000-0002-5939-8733-
dc.identifier.wosWOS:000668331800001en_US
dc.identifier.scopus2-s2.0-85109455880en_US
dc.institutionauthorEroğul, Osman-
dc.identifier.pmid34116519en_US
dc.identifier.doi10.1088/1361-6579/ac0a9c-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ1-
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.2. Department of Biomedical Engineering-
Appears in Collections:Biyomedikal Mühendisliği Bölümü / Department of Biomedical Engineering
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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