Detection of early stages of Parkinson's disease by analyzing fMRI data and machine learning approaches

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.

Manylion Llyfryddiaeth
Prif Awduron:	Neehal, Ahmed Hasin, Azam, Md. Nura, Islam, Md. Sazzadul, Hossain, Md. Ishrak
Awduron Eraill:	Parvez, Mohammad Zavid
Fformat:	Traethawd Ymchwil
Iaith:	English
Cyhoeddwyd:	Brac University 2020
Pynciau:	Functional imaging Parkinson's disease fMRI Voxel intensity Machine learning SVM classifier STFT
Mynediad Ar-lein:	http://hdl.handle.net/10361/13884

id	10361-13884
record_format	dspace
spelling	10361-138842022-01-26T10:21:46Z Detection of early stages of Parkinson's disease by analyzing fMRI data and machine learning approaches Neehal, Ahmed Hasin Azam, Md. Nura Islam, Md. Sazzadul Hossain, Md. Ishrak Parvez, Mohammad Zavid Department of Computer Science and Engineering, Brac University Functional imaging Parkinson's disease fMRI Voxel intensity Machine learning SVM classifier STFT This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. Cataloged from PDF version of thesis. Includes bibliographical references (pages 23-26). Parkinson's Disease is a progressive nervous system brain disorder which affects motor neuron loss control and movement coordination. Parkinson's symptoms are shown gradually and get worse over time. Its signs and symptoms can be different for everyone. There may be minor early signs and they may go unnoticed. Therefore, early detection of Parkinson's disease might significantly improve life style by giving proper treatment. Moreover, doctors may suggest regulating certain regions of your brain and improve the symptoms. In recent years, the use of Functional Imaging in neurodegenerative diseases has increased, with applications in basic pathophysiology research, support in determination, or evaluation of new medications. In our research we used fMRI data of eight early PD patients. Resting-state fMRI images were collected for analyzing the data and feature extraction. Time series data were generated for each subject based on voxel intensity. In addition, STFT was used to measure the time frequency function. Furthermore, SVM classifier was used for the classification and prediction of the early stage of PD. Using our proposed method, we have achieved 100% sensitivity, specificity, and accuracy considering seven subjects, however, one subject was exceptional whereas we have achieved 99.76% accuracy, 100% specificity and 99.53% sensitivity. Finally, this process is a well-structured model for predicting the early stages of PD. It may help to the doctors for diagnosis of the disease at its early stages and the patients should receive better treatment. Ahmed Hasin Neehal Md. Nura Azam Md. Sazzadul Islam Md. Ishrak Hossain B. Computer Science 2020-07-13T13:42:15Z 2020-07-13T13:42:15Z 2019 2019-12 Thesis ID 16101142 ID 16101169 ID 16101161 ID 16101166 http://hdl.handle.net/10361/13884 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. 26 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Functional imaging Parkinson's disease fMRI Voxel intensity Machine learning SVM classifier STFT
spellingShingle	Functional imaging Parkinson's disease fMRI Voxel intensity Machine learning SVM classifier STFT Neehal, Ahmed Hasin Azam, Md. Nura Islam, Md. Sazzadul Hossain, Md. Ishrak Detection of early stages of Parkinson's disease by analyzing fMRI data and machine learning approaches
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.
author2	Parvez, Mohammad Zavid
author_facet	Parvez, Mohammad Zavid Neehal, Ahmed Hasin Azam, Md. Nura Islam, Md. Sazzadul Hossain, Md. Ishrak
format	Thesis
author	Neehal, Ahmed Hasin Azam, Md. Nura Islam, Md. Sazzadul Hossain, Md. Ishrak
author_sort	Neehal, Ahmed Hasin
title	Detection of early stages of Parkinson's disease by analyzing fMRI data and machine learning approaches
title_short	Detection of early stages of Parkinson's disease by analyzing fMRI data and machine learning approaches
title_full	Detection of early stages of Parkinson's disease by analyzing fMRI data and machine learning approaches
title_fullStr	Detection of early stages of Parkinson's disease by analyzing fMRI data and machine learning approaches
title_full_unstemmed	Detection of early stages of Parkinson's disease by analyzing fMRI data and machine learning approaches
title_sort	detection of early stages of parkinson's disease by analyzing fmri data and machine learning approaches
publisher	Brac University
publishDate	2020
url	http://hdl.handle.net/10361/13884
work_keys_str_mv	AT neehalahmedhasin detectionofearlystagesofparkinsonsdiseasebyanalyzingfmridataandmachinelearningapproaches AT azammdnura detectionofearlystagesofparkinsonsdiseasebyanalyzingfmridataandmachinelearningapproaches AT islammdsazzadul detectionofearlystagesofparkinsonsdiseasebyanalyzingfmridataandmachinelearningapproaches AT hossainmdishrak detectionofearlystagesofparkinsonsdiseasebyanalyzingfmridataandmachinelearningapproaches
_version_	1814309423181463552

Detection of early stages of Parkinson's disease by analyzing fMRI data and machine learning approaches

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