Identification of Depression Subtypes Based on EEG and Machine Learning.

Depressive disorders are heterogeneous in symptoms and course of disease, while it is difficult to reveal the neurophysiological subtypes. In this study, we combined EEG data and machine learning methods to identify subtypes of depression, and further evaluated the rationality and reliability of subtypes. The results showed that the left-right asymmetry of prefrontal lobe in alpha band clustered 32 patients with depression into three different subtypes, each type showed unique clinical characteristics. High classification effects could be obtained through decision tree and logistic regression model. The study further investigated the new way to identify neurophysiological subtypes of depression with machine learning methods. This study also shows the important value of the classic EEG index of depression, namely the left-right asymmetry of prefrontal lobe in alpha band, in the identification of depression subtypes.