Estimation of neurological status from non-electroencephalography bio-signals by motif patterns

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dc.contributor.authorKaya, Yilmaz
dc.contributor.authorErtugrul, Omer Faruk
dc.date.accessioned2024-12-24T19:25:20Z
dc.date.available2024-12-24T19:25:20Z
dc.date.issued2019
dc.departmentSiirt Üniversitesi
dc.description.abstractIn this paper, a novel feature extraction approach, which was called motif patterns, was proposed and it was employed to estimate the neurological status from non-electroencephalography (non-EEG) bio-signals. It was found from the literature that successful results were obtained by using the feature extraction methods that are sensitive to local changes such as one-dimensional local binary patterns (1D-LBP). In 1D-LBP, the local changes in a signal were determined based on the comparisons between each central value'' with its neighbors. In order to increase the sensitivity of extracted features from the local changes in a signal, each value'' in the signal was compared with its neighbor, and by this way, a motif was obtained in the result of the comparisons in a specified window. To evaluate and validate the proposed approach, the non-EEG bio-signals, which were recorded by electrodermal activity, temperature, accelerometer, heart rate, and arterial oxygen level sensors, were employed. The features that were extracted from these signals by the proposed motif patterns were classified by machine learning methods. The neurological status of each of the samples was classified accurately by the proposed approach. Furthermore, the optimal sensor types were investigated and it was found that heart rate signals are enough to estimate the neurological status. (C) 2019 Elsevier B.V. All rights reserved.
dc.description.sponsorshipScientific Research Projects Coordination Unit of Batman University, Turkey [BTUBAP-2018-MMF-4]
dc.description.sponsorshipThis research is supported by BTUBAP-2018-MMF-4 code project of Scientific Research Projects Coordination Unit of Batman University, Turkey.
dc.identifier.doi10.1016/j.asoc.2019.105609
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.scopus2-s2.0-85068224220
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2019.105609
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6367
dc.identifier.volume83
dc.identifier.wosWOS:000488100900002
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofApplied Soft Computing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectMotif patterns
dc.subjectOne-dimensional local binary patterns
dc.subjectLocal changes
dc.subjectNeurological status
dc.subjectBio-signals
dc.subjectFeature extraction
dc.titleEstimation of neurological status from non-electroencephalography bio-signals by motif patterns
dc.typeArticle

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