EEG-based Major Depressive Disorder Detection Using Data Mining Techniques

Danqi Hong, Xingxian Huang, Yingshan Shen, Haibo Yu, Xiaomao Fan, Gansen Zhao, Wenbin Lei, Haoyu Luo

Abstract: Major depressive disorder (MDD) is a common mental illness characterized by a persistent feeling of low mood, sadness, fatigue, despair, etc.. In a serious case, patients with MDD may have suicidal thoughts or even suicidal behaviors. In clinical practice, a widely used method of MDD detection is based on a professional rating scale. However, the scale-based diagnostic method is highly subjective, and requires a professional assessment from a trained staff. In this work, 92 participants were recruited to collect EEG signals in the Shenzhen Traditional Chinese Medicine Hospital, assessing MDD severity with the HAMD-17 rating scale by a trained physician. Two data mining methods of logistic regression (LR) and support vector machine (SVM) with derived EEG-based beta-alpha-ratio features, namely LR-DF and SVM-DF, are employed to screen out patients with MDD. Experimental results show that the presented the LR-DF and SVM-DF achieved F 1 scores of 0:76 0:30 and 0:92 0:18, respectively, which have obvious superiority to the LR and SVM without derived EEG-based beta-alpha-ratio features.