A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD)

Major depressive disorder (MDD), a debilitating mental illness, could cause functional disabilities and could become a social problem. An accurate and early diagnosis for depression could become challenging. This paper proposed a machine learning framework involving EEG-derived synchronization likel...

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Main Authors: Mumtaz, W., Ali, S.S.A., Yasin, M.A.M., Malik, A.S.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.21811 /
Published: Springer Verlag 2018
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85023175534&doi=10.1007%2fs11517-017-1685-z&partnerID=40&md5=08a959277e51f50b7ced0939c6ab3bd4
http://eprints.utp.edu.my/21811/
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spelling utp-eprints.218112018-11-16T08:28:09Z A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD) Mumtaz, W. Ali, S.S.A. Yasin, M.A.M. Malik, A.S. Major depressive disorder (MDD), a debilitating mental illness, could cause functional disabilities and could become a social problem. An accurate and early diagnosis for depression could become challenging. This paper proposed a machine learning framework involving EEG-derived synchronization likelihood (SL) features as input data for automatic diagnosis of MDD. It was hypothesized that EEG-based SL features could discriminate MDD patients and healthy controls with an acceptable accuracy better than measures such as interhemispheric coherence and mutual information. In this work, classification models such as support vector machine (SVM), logistic regression (LR) and Naïve Bayesian (NB) were employed to model relationship between the EEG features and the study groups (MDD patient and healthy controls) and ultimately achieved discrimination of study participants. The results indicated that the classification rates were better than chance. More specifically, the study resulted into SVM classification accuracy = 98, sensitivity = 99.9, specificity = 95 and f-measure = 0.97; LR classification accuracy = 91.7, sensitivity = 86.66, specificity = 96.6 and f-measure = 0.90; NB classification accuracy = 93.6, sensitivity = 100, specificity = 87.9 and f-measure = 0.95. In conclusion, SL could be a promising method for diagnosing depression. The findings could be generalized to develop a robust CAD-based tool that may help for clinical purposes. © 2017, International Federation for Medical and Biological Engineering. Springer Verlag 2018 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85023175534&doi=10.1007%2fs11517-017-1685-z&partnerID=40&md5=08a959277e51f50b7ced0939c6ab3bd4 Mumtaz, W. and Ali, S.S.A. and Yasin, M.A.M. and Malik, A.S. (2018) A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD). Medical and Biological Engineering and Computing, 56 (2). pp. 233-246. http://eprints.utp.edu.my/21811/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Major depressive disorder (MDD), a debilitating mental illness, could cause functional disabilities and could become a social problem. An accurate and early diagnosis for depression could become challenging. This paper proposed a machine learning framework involving EEG-derived synchronization likelihood (SL) features as input data for automatic diagnosis of MDD. It was hypothesized that EEG-based SL features could discriminate MDD patients and healthy controls with an acceptable accuracy better than measures such as interhemispheric coherence and mutual information. In this work, classification models such as support vector machine (SVM), logistic regression (LR) and Naïve Bayesian (NB) were employed to model relationship between the EEG features and the study groups (MDD patient and healthy controls) and ultimately achieved discrimination of study participants. The results indicated that the classification rates were better than chance. More specifically, the study resulted into SVM classification accuracy = 98, sensitivity = 99.9, specificity = 95 and f-measure = 0.97; LR classification accuracy = 91.7, sensitivity = 86.66, specificity = 96.6 and f-measure = 0.90; NB classification accuracy = 93.6, sensitivity = 100, specificity = 87.9 and f-measure = 0.95. In conclusion, SL could be a promising method for diagnosing depression. The findings could be generalized to develop a robust CAD-based tool that may help for clinical purposes. © 2017, International Federation for Medical and Biological Engineering.
format Article
author Mumtaz, W.
Ali, S.S.A.
Yasin, M.A.M.
Malik, A.S.
spellingShingle Mumtaz, W.
Ali, S.S.A.
Yasin, M.A.M.
Malik, A.S.
A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD)
author_sort Mumtaz, W.
title A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD)
title_short A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD)
title_full A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD)
title_fullStr A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD)
title_full_unstemmed A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD)
title_sort machine learning framework involving eeg-based functional connectivity to diagnose major depressive disorder (mdd)
publisher Springer Verlag
publishDate 2018
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85023175534&doi=10.1007%2fs11517-017-1685-z&partnerID=40&md5=08a959277e51f50b7ced0939c6ab3bd4
http://eprints.utp.edu.my/21811/
_version_ 1741196524675661824
score 11.62408

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