可解释性
人工智能
支持向量机
模式识别(心理学)
重性抑郁障碍
脑电图
特征选择
计算机科学
特征(语言学)
特征向量
特征提取
机器学习
心理学
精神科
语言学
哲学
认知
作者
Vojtěch Mrázek,Soyiba Jawed,Muhammad Arif,Aamir Saeed Malik
标识
DOI:10.1145/3583131.3590398
摘要
In this paper, we propose an interpretable electroencephalogram (EEG)-based solution for the diagnostics of major depressive disorder (MDD). The acquisition of EEG experimental data involved 32 MDD patients and 29 healthy controls. A feature matrix is constructed involving frequency decomposition of EEG data based on power spectrum density (PSD) using the Welch method. Those PSD features were selected, which were statistically significant. To improve interpretability, the best features are first selected from feature space via the non-dominated sorting genetic (NSGA-II) evolutionary algorithm. The best features are utilized for support vector machine (SVM), and k-nearest neighbors (k-NN) classifiers, and the results are then correlated with features to improve the interpretability. The results show that the features (gamma bands) extracted from the left temporal brain regions can distinguish MDD patients from control significantly. The proposed best solution by NSGA-II gives an average sensitivity of 93.3%, specificity of 93.4% and accuracy of 93.5%. The complete framework is published as open-source at https://github.com/ehw-fit/eeg-mdd.
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