MDD-TSVM: A novel semisupervised-based method for major depressive disorder detection using electroencephalogram signals. (January 2022) - Lin, Hongtuo; Jian, Chufan; Cao, Yang; Ma, Xiaoguang; Wang, Hailiang; Miao, Fen; Fan, Xiaomao; Yang, Jinzhu; Zhao, Gansen; Zhou, Hui
Abstract: Major depressive disorder (MDD) is a common mental illness characterized by persistent feeling of depressed mood and loss of interest. It would cause, in a severe case, suicide behaviors. In clinical settings, automatic MDD detection is mainly based on electroencephalogram (EEG) signals with supervised learning techniques. However, supervised-based MDD detection methods encounter two ineviTable bottlenecks: firstly, such methods rely heavily on an EEG training dataset with MDD labels annotated by a physical therapist, leading to subjectivity and high cost; secondly, most of EEG signals are unlabeled in a real scenario. In this paper, a novel semisupervised-based MDD detection method named MDD-TSVM is presented. Specifically, the MDD-TSVM utilizes the semisupervised method of transductive support vector machine (TSVM) as its backbone, further dividing the unlabeled penalty item of the TSVM objective function into two pseudo-labeled penalty items with or without MDD. By such improvement, the MDD-SVM can make full use of labeled and unlabeled datasets as well as alleviate the class imbalance problem. Experiment results showed that our proposed MDD-TSVM achieved F 1 score of 0.85 ± 0.05 and accuracy of 0.89 ± 0.03 on identifying MDD patients, which is superior to the state-of-the-art methods. Highlights: A semisupervised-based major depressive disorder (MDD) detection method named MDD-TSVM is proposed. MDD-TSVM can alleviate the class imbalance problem in the real MDD-EEG signal dataset by two pseudo labeled penalty items. MDD-TSVM achieves promising results on MDD detection task, which outperforms the cutting-edge methods with a margin.