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.
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
