Automatic recognition of alertness level by using wavelet transform and artificial neural network

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dc.contributor.authorKiymik, MK
dc.contributor.authorAkin, M
dc.contributor.authorSubasi, A
dc.date.accessioned2024-04-24T16:15:10Z
dc.date.available2024-04-24T16:15:10Z
dc.date.issued2004
dc.departmentDicle Üniversitesien_US
dc.description.abstractWe propose a novel method for automatic recognition of alertness level from full spectrum electroencephalogram (EEG) recordings. This procedure uses power spectral density (PSD) of discrete wavelet transform (DWT) of full spectrum EEG as an input to an artificial neural network (ANN) with three discrete outputs: alert, drowsy and sleep. The error back propagation neural network is selected as a classifier to discriminate the alertness level of a subject. EEG signals were obtained from 30 healthy subjects. The group consisted of 14 females and 16 males with ages ranging from 18 to 65 years and a mean age of 33.5 years, and a body mass index (BMI) of 32.4 +/- 7.3 kg/m(2). Alertness level and classification properties of ANN were tested using the data recorded in 12 healthy subjects, whereby the EEG recordings were not used been used to train the ANN. The statistics were used as a measure of potential applicability of the ANN. The accuracy of the ANN was 96 3% alert, 95 +/- 4% drowsy and 94 +/- 5% sleep. The results suggest that the automatic recognition algorithm is applicable for distinguishing between alert, drowsy and sleep state in recordings that have not been used for the training. (C) 2004 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.jneumeth.2004.04.027
dc.identifier.endpage240en_US
dc.identifier.issn0165-0270
dc.identifier.issn1872-678X
dc.identifier.issue2en_US
dc.identifier.pmid15488236
dc.identifier.scopus2-s2.0-5644255894
dc.identifier.scopusqualityQ2
dc.identifier.startpage231en_US
dc.identifier.urihttps://doi.org/10.1016/j.jneumeth.2004.04.027
dc.identifier.urihttps://hdl.handle.net/11468/15683
dc.identifier.volume139en_US
dc.identifier.wosWOS:000224970500012
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofJournal of Neuroscience Methods
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAlerten_US
dc.subjectDrowsyen_US
dc.subjectSleepen_US
dc.subjectElectroencephalogram (Eeg)en_US
dc.subjectDiscrete Wavelet Transform (Dwt)en_US
dc.subjectArtificial Neural Network (Ann)en_US
dc.subjectPower Spectral Density (Psd)en_US
dc.titleAutomatic recognition of alertness level by using wavelet transform and artificial neural networken_US
dc.titleAutomatic recognition of alertness level by using wavelet transform and artificial neural network
dc.typeArticleen_US

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