@article{ref1,
title="MDD-TSVM: A novel semisupervised-based method for major depressive disorder detection using electroencephalogram signals",
journal="Computers in biology and medicine",
year="2022",
author="Lin, Hongtuo and Jian, Chufan and Cao, Yang and Ma, Xiaoguang and Wang, Hailiang and Miao, Fen and Fan, Xiaomao and Yang, Jinzhu and Zhao, Gansen and Zhou, Hui",
volume="140",
number="",
pages="e105039-e105039",
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 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>",
language="en",
issn="0010-4825",
doi="10.1016/j.compbiomed.2021.105039",
url="http://dx.doi.org/10.1016/j.compbiomed.2021.105039"
}