A major depressive disorder classification framework based on EEG signals using statistical, spectral, wavelet, functional connectivity, and nonlinear analysis

支持向量机 人工智能 模式识别(心理学) 特征选择 脑电图 计算机科学 重性抑郁障碍 分类器(UML) 小波 机器学习 特征提取 心理学 精神科 认知
作者
Reza Akbari Movahed,Gila Pirzad Jahromi,Shima Shahyad,Gholam Hossein Meftahi
出处
期刊:Journal of Neuroscience Methods [Elsevier]
卷期号:358: 109209-109209 被引量:64
标识
DOI:10.1016/j.jneumeth.2021.109209
摘要

Major depressive disorder (MDD) is a prevalent mental illness that is diagnosed through questionnaire-based approaches; however, these methods may not lead to an accurate diagnosis. In this regard, many studies have focused on using electroencephalogram (EEG) signals and machine learning techniques to diagnose MDD. This paper proposes a machine learning framework for MDD diagnosis, which uses different types of EEG-derived features. The features are extracted using statistical, spectral, wavelet, functional connectivity, and nonlinear analysis methods. The sequential backward feature selection (SBFS) algorithm is also employed to perform feature selection. Various classifier models are utilized to select the best one for the proposed framework. The proposed method is validated with a public EEG dataset, including the EEG data of 34 MDD patients and 30 healthy subjects. The evaluation of the proposed framework is conducted using 10-fold cross-validation, providing the metrics such as accuracy (AC), sensitivity (SE), specificity (SP), F1-score (F1), and false discovery rate (FDR). The best performance of the proposed method has provided an average AC of 99%, SE of 98.4%, SP of 99.6%, F1 of 98.9%, and FDR of 0.4% using the support vector machine with RBF kernel (RBFSVM) classifier. The obtained results demonstrate that the proposed method outperforms other approaches for MDD classification based on EEG signals. According to the obtained results, a highly accurate MDD diagnosis would be provided using the proposed method, while it can be utilized to develop a computer-aided diagnosis (CAD) tool for clinical purposes.
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