Real time sleep onset detection from single channel EEG signal using block sample entropy

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dc.contributor.authorZobaed, Talha
dc.contributor.authorAhmed, Saadaldeen Rashid Ahmed
dc.contributor.authorMiah, Abu Saleh Musa
dc.contributor.authorBinta, Salma Masuda
dc.contributor.authorAhmed, Mohammed Rashid Ahmed
dc.contributor.authorRashid, Mamunur
dc.date.accessioned2021-05-15T12:49:31Z
dc.date.available2021-05-15T12:49:31Z
dc.date.issued2020
dc.departmentMühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description2nd International Scientific Conference of Al-Ayen University, ISCAU 2020 -- 15 July 2020 through 17 July 2020 -- -- 165187
dc.description.abstractIn recent years, driver's temporary state has been one in each of the foremost causes of road accidents and would possibly lead to severe physical damaging, mortality and necessary and noticeable economic losses. Maximum road accidents possible to avoided, if possible, to properly monitored driver's drowsiness and a system are given warnings. In this work, a simple and inexpensive method has been offered to detect driver's drowsiness or sleep onset detection with single channel EEG signal analysis. The key novelty of this work is to identify the sleep onset detection from a publicly available graph signal dataset by exploitation only one feature, simply implementable filter in any microcontroller device or smartphone and a threshold based mostly classification. Since, threshold-based classification techniques don't need to train the classifier, hence, new subject adaptation is comparatively easier and real time implementation is more feasible. This novel approach can be easily implemented in smartphone to design and expand a drowsiness detection and alarming system for vehicle's driver. On a variety of subjects, the experimental results show 95.68% accuracy. © 2020 Published under licence by IOP Publishing Ltd.en_US
dc.description.sponsorshipThis work has been done under the research grants provided by the ICT Division, Ministry of Posts, Telecommunication, and Information Technology, the People's Republic of Bangladesh.en_US
dc.identifier.doi10.1088/1757-899X/928/3/032021
dc.identifier.issn1757-8981
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85097186775
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1088/1757-899X/928/3/032021
dc.identifier.urihttps://hdl.handle.net/20.500.12939/1039
dc.identifier.volume928en_US
dc.indekslendigikaynakScopus
dc.institutionauthorAhmed, Mohammed Rashid Ahmed
dc.language.isoen
dc.publisherIOP Publishing Ltden_US
dc.relation.ispartofIOP Conference Series: Materials Science and Engineering
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBlock Sample Entropyen_US
dc.subjectDrowsinessen_US
dc.subjectElectroencephalographyen_US
dc.subjectSleep Onseten_US
dc.titleReal time sleep onset detection from single channel EEG signal using block sample entropy
dc.typeConference Object

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