Automatic sleep staging based on SVD, VMD, HHT and morphological features of single-lead ECG signal

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dc.contributor.authorYucelbas, Sule
dc.contributor.authorYucelbas, Cuneyt
dc.contributor.authorTezel, Guley
dc.contributor.authorOzsen, Seral
dc.contributor.authorYosunkaya, Sebnem
dc.date.accessioned2024-02-23T14:02:53Z
dc.date.available2024-02-23T14:02:53Z
dc.date.issued2018
dc.departmentNEÜen_US
dc.description.abstractElectroencephalogram (EEG) signals, which are among the primary polysomnography (PSG) signals used for sleep staging, are difficult to obtain and interpret. However, it is much easier to obtain and interpret electrocardiogram (ECG) signals. The use of ECG signals for automatic sleep staging systems could bring practicality to these systems. In this study, ECG signals were used to identify the wake (W), non-rapid eye movement (NREM) and rapid eye movement (REM) stages of the sleep data from two different databases with 17,758 epochs of 28 subjects (21 healthy subjects and 7 obstructive sleep apnea (OSA) patients) in total. Four different methods were used to extract features from these signals: Singular Value Decomposition (SVD), Variational Mode Decomposition (VMD), Hilbert Huang Transform (HHT), and Morphological method. As a result of applying the methods separately, four different data sets were obtained. The four different datasets were given to the Wrapper Subset Evaluation system with the best-first search algorithm. After the feature selection procedure, the datasets were separately classified by using the Random Forest classifier. The results were interpreted by using the essential statistical criteria. Among the methods, morphological method was the most successful and it was followed by SVD in terms of success rate for both two databases. For the first database, the mean classification accuracy rate, Kappa coefficient and mean F-measure value of the Morphological method were found as 87.11%, 0.7369, 0.869 for the healthy and 78.08%, 0.5715, 0.782 for the patient, respectively. For the second database, the same statistical measures were determined as 77.02%, 0.4308, 0.755 for the healthy and 76.79%, 0.5227, 0.759 for the patient, respectively. The performance results of the study, which is consistent with real life applications, were compared with the previous studies in this field listed in the literature. (C) 2018 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.2018.02.034
dc.identifier.endpage206en_US
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85042762307en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage193en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2018.02.034
dc.identifier.urihttps://hdl.handle.net/20.500.12452/11873
dc.identifier.volume102en_US
dc.identifier.wosWOS:000430774900015en_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.subjectAutomatic Sleep Stagingen_US
dc.subjectEcgen_US
dc.subjectHhten_US
dc.subjectMorphological Featuresen_US
dc.subjectSvden_US
dc.subjectVmden_US
dc.titleAutomatic sleep staging based on SVD, VMD, HHT and morphological features of single-lead ECG signalen_US
dc.typeArticleen_US

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