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...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 56; no. 2; pp. 233 - 247 |
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| Autores principales: | , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=127735945&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 127735945 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Feb2018 vid: 56 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 127735945 127735945 143923111 NLM28702811 127735945 10.1007/s11517-017-1685-z NLM28702811 127735945 ppf: 233 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD). aug: au: Mumtaz, Wajid Ali, Syed Saad Azhar Yasin, Mohd Azhar Mohd Malik, Aamir Saeed affil: Center for Intelligent Signal and Imaging Research, Electrical and Electronic Engineering Department, Universiti Teknologi PETRONAS, 32610, Seri Iskandar, Malaysia sug: subj: Depression Diagnosis Electroencephalography Adult Logistic Regression Female Models, Theoretical Sensitivity and Specificity Reproducibility of Results Probability Middle Age Young Adult Male Funding Source Human Adult: 19-44 years Middle Aged: 45-64 years Female Male ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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