An expert system for automated recognition of patients with obstructive sleep apnea using electrocardiogram recordings

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dc.contributor.authorYildiz, Abdulnasir
dc.contributor.authorAkin, Mehmet
dc.contributor.authorPoyraz, Mustafa
dc.date.accessioned2024-04-24T16:11:25Z
dc.date.available2024-04-24T16:11:25Z
dc.date.issued2011
dc.departmentDicle Üniversitesien_US
dc.description.abstractObstructive sleep apnea (OSA) is a highly prevalent sleep disorder. The traditional diagnosis methods of the disorder are cumbersome and expensive. The ability to automatically identify OSA from electrocardiogram (ECG) recordings is important for clinical diagnosis and treatment. In this study, we proposed an expert system based on discrete wavelet transform (DWT), fast-Fourier transform (FFT) and least squares support vector machine (LS-SVM) for the automatic recognition of patients with OSA from nocturnal ECG recordings. Thirty ECG recordings collected from normal subjects and subjects with sleep apnea, each of approximately 8 h in duration, were used throughout the study. The proposed OSA recognition system comprises three stages. In the first stage, an algorithm based on DWT was used to analyze ECG recordings for the detection of heart rate variability (HRV) and ECG-derived respiration (EDR) changes. In the second stage, an FFT based power spectral density (PSD) method was used for feature extraction from HRV and EDR changes. Then, a hill-climbing feature selection algorithm was used to identify the best features that improve classification performance. In the third stage, the obtained features were used as input patterns of the LS-SVM classifier. Using the cross-validation method, the accuracy of the developed system was found to be 100% for using a subset of selected combination of HRV and EDR features. The results confirmed that the proposed expert system has potential for recognition of patients with suspected OSA by using ECG recordings. (C) 2011 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.eswa.2011.04.080
dc.identifier.endpage12890en_US
dc.identifier.issn0957-4174
dc.identifier.issue10en_US
dc.identifier.scopus2-s2.0-79957975941
dc.identifier.scopusqualityQ1
dc.identifier.startpage12880en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2011.04.080
dc.identifier.urihttps://hdl.handle.net/11468/15385
dc.identifier.volume38en_US
dc.identifier.wosWOS:000292169500094
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHeart Rate Variability (Hrv)en_US
dc.subjectEcg-Derived Respiration (Edr)en_US
dc.subjectDiscrete Wavelet Transform (Dwt)en_US
dc.subjectFast-Fourier Transform (Fft)en_US
dc.subjectLeast Square Support Vector Machine (Ls-Svm)en_US
dc.subjectObstructive Sleep Apnea (Osa)en_US
dc.titleAn expert system for automated recognition of patients with obstructive sleep apnea using electrocardiogram recordingsen_US
dc.titleAn expert system for automated recognition of patients with obstructive sleep apnea using electrocardiogram recordings
dc.typeArticleen_US

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