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


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-02

AUTHORS

Wajid Mumtaz, Syed Saad Azhar Ali, Mohd Azhar Mohd Yasin, Aamir Saeed Malik

ABSTRACT

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. More... »

PAGES

233-246

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11517-017-1685-z

DOI

http://dx.doi.org/10.1007/s11517-017-1685-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1090667817

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/28702811


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Download the RDF metadata as:  json-ld nt turtle xml License info

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RDF/XML is a standard XML format for linked data.

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