Pre-determination of OSA degree using morphological features of the ECG signal

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dc.contributor.authorYucelbas, Sule
dc.contributor.authorYucelbas, Cuneyt
dc.contributor.authorTezel, Gulay
dc.contributor.authorOzsen, Seral
dc.contributor.authorKuccukturk, Serkan
dc.contributor.authorYosunkaya, Sebnem
dc.date.accessioned2024-02-23T14:02:53Z
dc.date.available2024-02-23T14:02:53Z
dc.date.issued2017
dc.departmentNEÜen_US
dc.description.abstractObstructive sleep apnea (OSA) is a very common, but a difficult sleep disorder to diagnose. Recurrent obstructions form in the airway during sleep, such that OSA can threaten a breathing capacity of patients. Clinically, continuous positive airway pressure (CPAP) is the most specific and effective treatment for this. In addition, these patients must be separated according to its degree, with CPAP treatment applied as a result. In this study, 30 OSA patients from two different databases were automatically classified using electrocardiogram (ECG) data, identified as mild, moderate, and severe. One of the databases was original recordings which had 9 OSA patients with 8303 epochs and the other one was Physionet benchmark database which had 21 patients with 20,824 epochs. Fifteen morphological features could be identified when apnea was seen, both before and after it presented. Five data groups in total for first dataset and second dataset were prepared with these features and 10-fold cross validation was used to effectively determine the test data. Then, sequential backward feature selection (SBFS) algorithm was applied to understand the more effective features. The prepared data groups were evaluated with artificial neural networks (ANN) to obtain optimum classification performance. All processes were repeated for ten times and error deviation was calculated for the accuracy. Furthermore, different classifiers which are frequently used in the literature were tested with selected features. The degree of OSA was estimated from three epochs in pre-apnea data, yielding the success rates of 97.20 +/- 2.15% and 90.18 +/- 8.11% with the SBFS algorithm for the first and second datasets, respectively. Also, SVM classifier followed ANN system in the success rates of 96.23 +/- 3.48% and 88.75 +/- 8.52% for used datasets. (C) 2017 Elsevier Ltd. All rights reserved.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey [113E591]; Scientific Research Projects Coordination Unit of Selcuk Universityen_US
dc.description.sponsorshipThis study is supported by the Scientific and Technological Research Council of Turkey (Project no. 113E591) and the Scientific Research Projects Coordination Unit of Selcuk University.en_US
dc.identifier.doi10.1016/j.eswa.2017.03.049
dc.identifier.endpage87en_US
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85016394215en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage79en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2017.03.049
dc.identifier.urihttps://hdl.handle.net/20.500.12452/11872
dc.identifier.volume81en_US
dc.identifier.wosWOS:000401593300008en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectEcgen_US
dc.subjectMorphological Featuresen_US
dc.subjectOsa Degreeen_US
dc.titlePre-determination of OSA degree using morphological features of the ECG signalen_US
dc.typeArticleen_US

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