Predicting Sleep Disorder using Raw Multi-Channel EEG signal

Jarif, Afnan; Rahman, Asfi; Prima, Tasfia Tabassum

dc.contributor.author	Jarif, Afnan
dc.contributor.author	Rahman, Asfi
dc.contributor.author	Prima, Tasfia Tabassum
dc.date.accessioned	2024-01-18T08:32:32Z
dc.date.available	2024-01-18T08:32:32Z
dc.date.issued	2023-05-30
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dc.identifier.uri	http://hdl.handle.net/123456789/2065
dc.description	Supervised Ms. Lutfun Nahar Lota, Assistant Professor, Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.description.abstract	Accurate sleep stage scoring and finding relevant feature form multi-channel EEG signal form different subject (healthy and unhealthy) is complex task. In recent years, deep learning, a type of machine learning that involves training ar tificial neural networks on large data sets, has shown promise for improving the accuracy and reliability of sleep stage scoring. This approach involves analyzing the pre-processed raw data and extract important feature and try to find informa tion and based on that we predict if a person has sleep disorder or not. By using deep learning to train our model the extracted data set we reprocessed and find promising result, it is possible to develop more accurate algorithms or models for automatic prediction of sleep disorder and other abnormal activity in brain	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.title	Predicting Sleep Disorder using Raw Multi-Channel EEG signal	en_US
dc.type	Thesis	en_US