Deep learning approaches to EEG and fMRI data: a comparative study for sleep stage classification

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.

Détails bibliographiques
Auteurs principaux:	Tanvir, Farhan, Rahman, Tanjilur, Kamal, S M Arfa, Hassan, Mahmudul, Nazia, Nowshin
Autres auteurs:	Nahim, Nabuat Zaman
Format:	Thèse
Langue:	English
Publié:	Brac University 2024
Sujets:	fMRI data Deep learning Sleep stage detection ConvLSTM model LSTM EEG data Signal processing > Digital techniques. Machine learning. Electroencephalography.
Accès en ligne:	http://hdl.handle.net/10361/24267

id	10361-24267
record_format	dspace
spelling	10361-242672024-10-01T21:01:03Z Deep learning approaches to EEG and fMRI data: a comparative study for sleep stage classification Tanvir, Farhan Rahman, Tanjilur Kamal, S M Arfa Hassan, Mahmudul Nazia, Nowshin Nahim, Nabuat Zaman Reza, Tanzim Department of Computer Science and Engineering, Brac University fMRI data Deep learning Sleep stage detection ConvLSTM model LSTM EEG data Signal processing--Digital techniques. Machine learning. Electroencephalography. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024. Cataloged from PDF version of thesis. Includes bibliographical references (pages 46-48). In this thesis, for classification of sleep stages, we use deep learning techniques with the help of data from fMRI and EEG. The ConvLSTM models are applied for the fMRI data. The data for the EEG is worked on with the LSTM and Bidirectional LSTM. This can hence be seen as a work of optimizing the accuracy, the precision, and the generalizability of all these models with one another. The baselines for all these different types of data are built up using initial models. The LSTM baseline model has given an accuracy of 78.69% on testing for sleep staging with W (Wake), NREM-1, NREM-2, and NREM-3 using EEG data, which is highly effective with such data resolution in time. Meanwhile, the Bidirectional LSTM model performs better preprocessing for the temporal aspect and hence yields, on average, 80.60% accuracy for general classification on the same stages. This would make it a model that can capture the dynamic nature of the EEG data across these particular stages. In contrast, processed fMRI data starts with a 76.82% testing accuracy, while performance is readjusted based on the feature extraction spatial-temporal settings adopted in their ConvLSTM configurations to classify sleep stages W, NREM-1, NREM-2, and NREM-3, with special attention to the role of model configuration. These results show that functionalities of tailored deep learning play the most basic role in the high complexity domain of sleep stage classification. These findings are prerequisite for the future development of this area. Farhan Tanvir Tanjilur Rahman S M Arfa Kamal Mahmudul Hassan Nowshin Nazia B.Sc. in Computer Science 2024-10-01T08:47:24Z 2024-10-01T08:47:24Z ©2024 2024-05 Thesis ID 22241051 ID 19101033 ID 22341062 ID 23341137 ID 19201093 http://hdl.handle.net/10361/24267 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 57 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	fMRI data Deep learning Sleep stage detection ConvLSTM model LSTM EEG data Signal processing--Digital techniques. Machine learning. Electroencephalography.
spellingShingle	fMRI data Deep learning Sleep stage detection ConvLSTM model LSTM EEG data Signal processing--Digital techniques. Machine learning. Electroencephalography. Tanvir, Farhan Rahman, Tanjilur Kamal, S M Arfa Hassan, Mahmudul Nazia, Nowshin Deep learning approaches to EEG and fMRI data: a comparative study for sleep stage classification
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.
author2	Nahim, Nabuat Zaman
author_facet	Nahim, Nabuat Zaman Tanvir, Farhan Rahman, Tanjilur Kamal, S M Arfa Hassan, Mahmudul Nazia, Nowshin
format	Thesis
author	Tanvir, Farhan Rahman, Tanjilur Kamal, S M Arfa Hassan, Mahmudul Nazia, Nowshin
author_sort	Tanvir, Farhan
title	Deep learning approaches to EEG and fMRI data: a comparative study for sleep stage classification
title_short	Deep learning approaches to EEG and fMRI data: a comparative study for sleep stage classification
title_full	Deep learning approaches to EEG and fMRI data: a comparative study for sleep stage classification
title_fullStr	Deep learning approaches to EEG and fMRI data: a comparative study for sleep stage classification
title_full_unstemmed	Deep learning approaches to EEG and fMRI data: a comparative study for sleep stage classification
title_sort	deep learning approaches to eeg and fmri data: a comparative study for sleep stage classification
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/24267
work_keys_str_mv	AT tanvirfarhan deeplearningapproachestoeegandfmridataacomparativestudyforsleepstageclassification AT rahmantanjilur deeplearningapproachestoeegandfmridataacomparativestudyforsleepstageclassification AT kamalsmarfa deeplearningapproachestoeegandfmridataacomparativestudyforsleepstageclassification AT hassanmahmudul deeplearningapproachestoeegandfmridataacomparativestudyforsleepstageclassification AT nazianowshin deeplearningapproachestoeegandfmridataacomparativestudyforsleepstageclassification
_version_	1814307302876905472

Deep learning approaches to EEG and fMRI data: a comparative study for sleep stage classification

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