Automated Sleep Scoring Using Multichannel EEG Biomarkers

Bari, Md. Abdul; Jany, Rafsan; Uddin, Musfik

dc.contributor.author	Bari, Md. Abdul
dc.contributor.author	Jany, Rafsan
dc.contributor.author	Uddin, Musfik
dc.date.accessioned	2022-12-12T05:43:21Z
dc.date.available	2022-12-12T05:43:21Z
dc.date.issued	2022-05-30
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dc.identifier.uri	http://hdl.handle.net/123456789/1608
dc.description	Supervised by Dr. Md. Azam Hossain, Assistant Professor, Department of CSE, A thesis submitted to the Department of CSE, in partial fulfillment of the requirements for the degree of B.Sc. Engineering in SWE Islamic University of Technology(IUT), Board Bazar, Gazipur, Bangladesh	en_US
dc.description.abstract	Sleep staging is one of the most essential approaches for diagnosing many sorts of sleep- related illnesses. Electroencephalography (EEG) is considered a computing tool for eval- uating the relationship between neurological effects and sleep stages because it detects sleep-related neurological changes quickly and accurately. So In comparison to the tradi- tional polysomnographic signal based approach, EEG is considered to be a more efficient tool to predict sleep stages outside of a fully equipped medical environment. The goal of this study is to use sleep EEG data to identify effective neurological EEG biomark- ers and predict five stages of sleep. We analyzed three EEG channels (F4, C4 and O2) from the dataset collected by Haaglanden Medisch Centrum (HMC, The Netherlands) and published by PhysioNet that contains 154 sleep recordings. In this study we have applied different classification models that are Decision Forest, Support Vector Machine, K-Nearest Neighbors, Extreme Gradient Boost and Neural Network to classify 5-class sleep stages. Among those we found that the Neural Network outperformed other mod- els. We have also identified delta wave power ratios (DAR, DTR, and DTABR) as EEG biomarkers that improved the overall accuracy from 84% to 92% using the Neural Net- work model.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.subject	Sleep scoring, electroencephalography, biomarker, machine learn- ing, neural network	en_US
dc.title	Automated Sleep Scoring Using Multichannel EEG Biomarkers	en_US
dc.type	Thesis	en_US