Depression Detection Using Relative EEG Power Induced by Emotionally Positive Images and a Conformal Kernel Support Vector Machine

Applied Sciences

Elyssa Barrick¹  DanielG. Dillon¹  Shiuan Huang²  Hao-Chun Hsu²  Yi-Hung Liu²  Chien-Te Wu³ 

[1] Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA 02474, USA;Graduate Institute of Mechatronics Engineering, National Taipei University of Technology, Taipei 10608, Taiwan;School of Occupational Therapy, College of Medicine, National Taiwan University, Taipei 10617, Taiwan;

关键词: major depressive disorder;    electroencephalography;    brain–computer interface;    emotion induction;    International Affective Picture System;    support vector machine;    machine learning;   

DOI  :  10.3390/app8081244

来源: DOAJ

【 摘 要 】

Electroencephalography (EEG) can assist with the detection of major depressive disorder (MDD). However, the ability to distinguish adults with MDD from healthy individuals using resting-state EEG features has reached a bottleneck. To address this limitation, we collected EEG data as participants engaged with positive pictures from the International Affective Picture System. Because MDD is associated with blunted positive emotions, we reasoned that this approach would yield highly dissimilar EEG features in healthy versus depressed adults. We extracted three types of relative EEG power features from different frequency bands (delta, theta, alpha, beta, and gamma) during the emotion task and resting state. We also applied a novel classifier, called a conformal kernel support vector machine (CK-SVM), to try to improve the generalization performance of conventional SVMs. We then compared CK-SVM performance with three machine learning classifiers: linear discriminant analysis (LDA), conventional SVM, and quadratic discriminant analysis. The results from the initial analyses using the LDA classifier on 55 participants (24 MDD, 31 healthy controls) showed that the participant-independent classification accuracy obtained by leave-one-participant-out cross-validation (LOPO-CV) was higher for the EEG recorded during the positive emotion induction versus the resting state for all types of relative EEG power. Furthermore, the CK-SVM classifier achieved higher LOPO-CV accuracy than the other classifiers. The best accuracy (83.64%; sensitivity = 87.50%, specificity = 80.65%) was achieved by the CK-SVM, using seven relative power features extracted from seven electrodes. Overall, combining positive emotion induction with the CK-SVM classifier proved useful for detecting MDD on the basis of EEG signals. In the future, this approach might be used to develop a brain–computer interface system to assist with the detection of MDD in the clinic. Importantly, such a system could be implemented with a low-density electrode montage (seven electrodes), highlighting its practical utility.

【 授权许可】

Unknown