TY  - JOUR
PY  - 2022//
TI  - MDD-TSVM: A novel semisupervised-based method for major depressive disorder detection using electroencephalogram signals
JO  - Computers in biology and medicine
A1  - Lin, Hongtuo
A1  - Jian, Chufan
A1  - Cao, Yang
A1  - Ma, Xiaoguang
A1  - Wang, Hailiang
A1  - Miao, Fen
A1  - Fan, Xiaomao
A1  - Yang, Jinzhu
A1  - Zhao, Gansen
A1  - Zhou, Hui
SP  - e105039
EP  - e105039
VL  - 140
IS  - 
N2  - 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 F1 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.<p /> <p>Language: en</p>
LA  - en
SN  - 0010-4825
UR  - http://dx.doi.org/10.1016/j.compbiomed.2021.105039
ID  - ref1
ER  -