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Classification of attention deficit hyperactivity disorder using variational autoencoder

Attention Deficit Hyperactivity Disorder (ADHD) categorize as one of the typical neurodevelopmental and mental disorders. Over the years, researchers have identified ADHD as a complicated disorder since it is not directly tested with a standard medical test such as a blood or urine test on the early...

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Main Authors: A. Samah, Azurah, Ahmad, Siti Nurul Aqilah, Abdul Majid, Hairudin, Ali Shah, Zuraini, Hashim, Haslina, Azman, Nuraina Syaza, Azmi, Nur Sabrina, Dewi Nasien, Dewi Nasien
Format: Article
Language:English
Published: Penerbit UTM Press 2021
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/97795/1/SitiNurulAqilah2021_ClassificationofAttentionDeficitHyperactivity.pdf
http://eprints.utm.my/id/eprint/97795/
http://dx.doi.org/10.11113/ijic.v11n2.352
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spelling my.utm.977952022-10-31T08:55:14Z http://eprints.utm.my/id/eprint/97795/ Classification of attention deficit hyperactivity disorder using variational autoencoder A. Samah, Azurah Ahmad, Siti Nurul Aqilah Abdul Majid, Hairudin Ali Shah, Zuraini Hashim, Haslina Azman, Nuraina Syaza Azmi, Nur Sabrina Dewi Nasien, Dewi Nasien QA75 Electronic computers. Computer science Attention Deficit Hyperactivity Disorder (ADHD) categorize as one of the typical neurodevelopmental and mental disorders. Over the years, researchers have identified ADHD as a complicated disorder since it is not directly tested with a standard medical test such as a blood or urine test on the early-stage diagnosis. Apart from the physical symptoms of ADHD, clinical data of ADHD patients show that most of them have learning problems. Therefore, functional Magnetic Resonance Imaging (fMRI) is considered the most suitable method to determine functional activity in the brain region to understand brain disorders of ADHD. One of the ways to diagnose ADHD is by using deep learning techniques, which can increase the accuracy of predicting ADHD using the fMRI dataset. Past attempts of classifying ADHD based on functional connectivity coefficient using the Deep Neural Network (DNN) result in 95% accuracy. As Variational Autoencoder (VAE) is the most popular in extracting high-level data, this model is applied in this study. This study aims to enhance the performance of VAE to increase the accuracy in classifying ADHD using fMRI data based on functional connectivity analysis. The preprocessed fMRI dataset is used for decomposition to find the region of interest (ROI), followed by Independent Component Analysis (ICA) that calculates the correlation between brain regions and creates functional connectivity matrices for each subject. As a result, the VAE model achieved an accuracy of 75% on classifying ADHD. Penerbit UTM Press 2021-12 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/97795/1/SitiNurulAqilah2021_ClassificationofAttentionDeficitHyperactivity.pdf A. Samah, Azurah and Ahmad, Siti Nurul Aqilah and Abdul Majid, Hairudin and Ali Shah, Zuraini and Hashim, Haslina and Azman, Nuraina Syaza and Azmi, Nur Sabrina and Dewi Nasien, Dewi Nasien (2021) Classification of attention deficit hyperactivity disorder using variational autoencoder. International Journal of Innovative Computing, 11 (2). pp. 81-87. ISSN 2180-4370 http://dx.doi.org/10.11113/ijic.v11n2.352 DOI:10.11113/ijic.v11n2.352
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
A. Samah, Azurah
Ahmad, Siti Nurul Aqilah
Abdul Majid, Hairudin
Ali Shah, Zuraini
Hashim, Haslina
Azman, Nuraina Syaza
Azmi, Nur Sabrina
Dewi Nasien, Dewi Nasien
Classification of attention deficit hyperactivity disorder using variational autoencoder
description Attention Deficit Hyperactivity Disorder (ADHD) categorize as one of the typical neurodevelopmental and mental disorders. Over the years, researchers have identified ADHD as a complicated disorder since it is not directly tested with a standard medical test such as a blood or urine test on the early-stage diagnosis. Apart from the physical symptoms of ADHD, clinical data of ADHD patients show that most of them have learning problems. Therefore, functional Magnetic Resonance Imaging (fMRI) is considered the most suitable method to determine functional activity in the brain region to understand brain disorders of ADHD. One of the ways to diagnose ADHD is by using deep learning techniques, which can increase the accuracy of predicting ADHD using the fMRI dataset. Past attempts of classifying ADHD based on functional connectivity coefficient using the Deep Neural Network (DNN) result in 95% accuracy. As Variational Autoencoder (VAE) is the most popular in extracting high-level data, this model is applied in this study. This study aims to enhance the performance of VAE to increase the accuracy in classifying ADHD using fMRI data based on functional connectivity analysis. The preprocessed fMRI dataset is used for decomposition to find the region of interest (ROI), followed by Independent Component Analysis (ICA) that calculates the correlation between brain regions and creates functional connectivity matrices for each subject. As a result, the VAE model achieved an accuracy of 75% on classifying ADHD.
format Article
author A. Samah, Azurah
Ahmad, Siti Nurul Aqilah
Abdul Majid, Hairudin
Ali Shah, Zuraini
Hashim, Haslina
Azman, Nuraina Syaza
Azmi, Nur Sabrina
Dewi Nasien, Dewi Nasien
author_facet A. Samah, Azurah
Ahmad, Siti Nurul Aqilah
Abdul Majid, Hairudin
Ali Shah, Zuraini
Hashim, Haslina
Azman, Nuraina Syaza
Azmi, Nur Sabrina
Dewi Nasien, Dewi Nasien
author_sort A. Samah, Azurah
title Classification of attention deficit hyperactivity disorder using variational autoencoder
title_short Classification of attention deficit hyperactivity disorder using variational autoencoder
title_full Classification of attention deficit hyperactivity disorder using variational autoencoder
title_fullStr Classification of attention deficit hyperactivity disorder using variational autoencoder
title_full_unstemmed Classification of attention deficit hyperactivity disorder using variational autoencoder
title_sort classification of attention deficit hyperactivity disorder using variational autoencoder
publisher Penerbit UTM Press
publishDate 2021
url http://eprints.utm.my/id/eprint/97795/1/SitiNurulAqilah2021_ClassificationofAttentionDeficitHyperactivity.pdf
http://eprints.utm.my/id/eprint/97795/
http://dx.doi.org/10.11113/ijic.v11n2.352
_version_ 1748703145894084608
score 13.211869

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