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dc.contributor.authorEbrahimi, Farideh
dc.contributor.authorSetarehdan, Seyed Kamaledin
dc.contributor.authorMartinek, Radek
dc.contributor.authorNazeran, Homer
dc.date.accessioned2017-11-30T11:28:02Z
dc.date.available2017-11-30T11:28:02Z
dc.date.issued2017
dc.identifier.citationAdvances in electrical and electronic engineering. 2017, vol. 15, no. 3, p. 459-475cs
dc.identifier.issn1336-1376
dc.identifier.issn1804-3119
dc.identifier.urihttp://hdl.handle.net/10084/122109
dc.description.abstractHighly accurate classification of sleep stages is possible based on EEG signals alone. However, reliable and high quality acquisition of these signals in the home environment is difficult. Instead, electrocardiogram (ECG) and Respiratory (Res) signals are easier to record and may offer a practical alternative for home monitoring of sleep. Therefore, automatic sleep staging was performed using ECG, Res (thoracic excursion) and EEG signals from 31 nocturnal recordings of the Sleep Heart Health Study (SHHS) polysomnography Database. Feature vectors were extracted from 0.5 min (standard) epochs of sleep data by time-domain, frequency domain, time-frequency and nonlinear methods and optimized by using the Support Vector Machine-Recursive Feature Elimination (SVM-RFE) method. These features were then classified by using a SVM. Classification based upon EEG features produced a Correct Classification Ratio CCR=0.92. In comparison, features derived from ECG signals alone, that is the combination of Heart Rate Variability (HRV), and ECG-Derived Respiration (EDR) signals produced a CCR=0.54, while those features based on the combination of HRV and (thoracic) Res signals resulted in a CCR=0.57. Overall comparison of the results based on standard epochs of EEG signals with those obtained from 5-minute (long) epochs of cardiorespiratory signals, revealed that acceptable CCR=0.81 and discriminative capacity (Accuracy=89.32%, Specificity=92.88% and Sensitivity=78.64%) were also achievable when using optimal feature sets derived from long epochs of the latter signals in sleep staging. In addition, it was observed that the presence of some artifacts (like bigeminy) in the cardiorespiratory signals reduced the accuracy of automatic sleep staging more than the artifacts that contaminated the EEG signals.cs
dc.format.extent1017613 bytes
dc.format.mimetypeapplication/pdf
dc.languageNeuvedenocs
dc.language.isoencs
dc.publisherVysoká škola báňská - Technická univerzita Ostravacs
dc.relation.ispartofseriesAdvances in electrical and electronic engineeringcs
dc.relation.urihttp://dx.doi.org/10.15598/aeee.v15i3.2182
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectautomatic sleep stagingcs
dc.subjectECG-derived respiration signalscs
dc.subjectelectroencephalogram (EEG) signalcs
dc.subjectheart rate variability (HRV) signalcs
dc.subjectres (thoracic excursion) signalcs
dc.titleComparative analysis of the discriminative capacity of EEG, two ECG-derived and respiratory signals in automatic sleep stagingcs
dc.typearticlecs
dc.identifier.doi10.15598/aeee.v15i3.2182
dc.rights.accessopenAccess
dc.type.versionpublishedVersion
dc.type.statusPeer-reviewed
dc.identifier.wos000424330700011


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