Identification of full-night sleep parameters using morphological features of ECG signals: A practical alternative to EEG and EOG signals

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dc.contributor.authorYucelbas, Sule
dc.contributor.authorYucelbas, Cueneyt
dc.contributor.authorTezel, Guelay
dc.contributor.authorOzsen, Seral
dc.contributor.authorYosunkaya, Sebnem
dc.date.accessioned2024-02-23T14:02:22Z
dc.date.available2024-02-23T14:02:22Z
dc.date.issued2024
dc.departmentNEÜen_US
dc.description.abstractElectroencephalogram (EEG) signals, which are among the most important recordings used in Polysomnography for sleep staging, are more challenging and demanding than electrocardiography (ECG) signals, both in terms of acquisition and interpretation. When examining the studies of other researchers on sleep parameters in the literature, it is evident that EEG signals are predominantly used for determining arousal (AR), K-complex (Kc), and sleep spindle (Ss) parameters. Furthermore, it is understood that electrooculography (EOG) signals are employed for detecting slow eye movements (SEM) and rapid eye movements (REM) parameters.This study is a continuation of our previous research, where we used only EEG signals for Kc and Ss detection. In this study, an approach that includes ECG signals in the determination of sleep parameters to bring practicality to sleep staging studies was adopted. For this purpose, firstly, 16 morphological features were extracted from ECG recordings taken from a total of 24 subjects after various preprocessing steps. Subsequently, these data were used to work on the detection of five different sleep parameters: AR, Kc, Ss, SEM, and REM, using the Random Subspace (RaSE) ensemble learning algorithm. The results were calculated according to various statistical criteria and a classification accuracy of over 78 % was obtained in all parameters. As a result, the sleep parameters that could be determined most successfully using the ECG signal were SEM and arousal, respectively. In addition, feature elimination was performed for these datasets using Symmetric Uncertainty (SU) ranking. As a result of the reclassification process using 9 and 12 features, the effectiveness of which was determined for both datasets, respectively, significant increases were observed in the performance outputs. Experimental results have shown that ECG signals can be used as an alternative to EEG and EOG signals in the determination of full-night sleep parameters.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey [113E591]en_US
dc.description.sponsorshipThis study is supported by the Scientific and Technological Research Council of Turkey (Project no. 113E591) .en_US
dc.identifier.doi10.1016/j.bspc.2023.105633
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.scopus2-s2.0-85174333152en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2023.105633
dc.identifier.urihttps://hdl.handle.net/20.500.12452/11678
dc.identifier.volume88en_US
dc.identifier.wosWOS:001092891200001en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofBiomedical Signal Processing And Controlen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectRandom Subspace Algorithmen_US
dc.subjectSleep Parametersen_US
dc.subjectEcgen_US
dc.subjectMorphological Featuresen_US
dc.titleIdentification of full-night sleep parameters using morphological features of ECG signals: A practical alternative to EEG and EOG signalsen_US
dc.typeArticleen_US

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