Approach based on wavelet packet transform and 1D-RMLBP for drowsiness detection using EEG

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dc.authorid0000-0002-2917-3736en_US
dc.contributor.authorAlçin, Ömer Faruk
dc.date.accessioned2021-06-08T11:24:28Z
dc.date.available2021-06-08T11:24:28Z
dc.date.issued2020en_US
dc.departmentMTÖ Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractEarly drowsiness detection may be crucial for the vehicle alertness system. Towards this, wearable technology, camera-based biophysical signals like electroencephalogram (EEG) approaches are utilised. In this Letter, the EEG-based approach is proposed to detect drowsiness. The proposed method consists of random sampling-based artificial signal augmentation, wavelet packet transform decomposition, logarithmic energy entropy, and one-dimensional region mean local binary pattern (1d-RMLBP) based feature extraction and classifier. k-Nearest neighbour and support vector machine classifiers are employed to detect the drowsiness. The MIT/BIH polysomnographic dataset has been used to test the proposed model. The proposed method has superior performance than the other methods using the same data set. The experimental results demonstrate that the proposed model could efficiently detect drowsiness from polysomnographic EEG signals.en_US
dc.identifier.citationAlçin, O. F. (December 01, 2020). Approach based on wavelet packet transform and 1D‐RMLBP for drowsiness detection using EEG. Electronics Letters, 56, 25, 1378-1381.en_US
dc.identifier.doi10.1049/el.2020.2668
dc.identifier.endpage1381en_US
dc.identifier.issue25en_US
dc.identifier.startpage1378en_US
dc.identifier.urihttps://doi.org/10.1049/el.2020.2668
dc.identifier.urihttps://hdl.handle.net/20.500.12899/196
dc.identifier.volume56en_US
dc.identifier.wosWOS:000604957700009en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.institutionauthorAlçin, Ömer Faruk
dc.language.isoenen_US
dc.publisherWiley-Blackwellen_US
dc.relation.ispartofElectronics Lettersen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectWavelet transformsen_US
dc.subjectElectroencephalographyen_US
dc.subjectFeature extractionen_US
dc.subjectSupport vector machinesen_US
dc.subjectEntropyen_US
dc.subjectMedical signal processingen_US
dc.subjectSignal classificationen_US
dc.subjectMedical signal detectionen_US
dc.subjectNearest neighbour methodsen_US
dc.subjectDrowsiness detectionen_US
dc.subjectVehicle alertness systemen_US
dc.subjectWearable technologyen_US
dc.subjectCamera-based biophysical signalsen_US
dc.subjectRandom sampling-based artificial signal augmentationen_US
dc.subjectLogarithmic energy entropyen_US
dc.subjectSupport vector machine classifiersen_US
dc.subjectPolysomnographic EEG signalsen_US
dc.subject1D-RMLBPen_US
dc.subjectWavelet packet transform decompositionen_US
dc.subjectOne-dimensional region mean local binary pattern based feature extractionen_US
dc.subjectMIT-BIH polysomnographic dataseten_US
dc.subjectk-nearest neighbour classifieren_US
dc.subjectExtreme learning machineen_US
dc.subjectWPDen_US
dc.titleApproach based on wavelet packet transform and 1D-RMLBP for drowsiness detection using EEGen_US
dc.typeArticleen_US

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