Machine-learning-based classification between post-traumatic stress disorder and major depressive disorder using P300 features.

ABSTRACT:

Background

The development of optimal classification criteria for specific mental disorders which share similar symptoms is an important issue for precise diagnosis. We investigated whether P300 features in both sensor-level and source-level could be effectively used to classify post-traumatic stress disorder (PTSD) and major depressive disorder (MDD).

Method

EEG signals were recorded from fifty-one PTSD patients, 67 MDD patients, and 39 healthy controls (HCs) while performing an auditory oddball task. Amplitude and latency of P300 were evaluated, and the current source analysis of P300 components was conducted using sLORETA. Finally, we classified two groups using machine-learning methods with both sensor- and source-level features. Moreover, we checked the comorbidity effe

SUBMITTER: Shim M 

PROVIDER: S-EPMC6812119 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature