Machine Learning-Based Electroencephalographic Phenotypes of Schizophrenia and Major Depressive Disorder.

ABSTRACT: Background: Psychiatric diagnosis is formulated by symptomatic classification; disease-specific neurophysiological phenotyping could help with its fundamental treatment. Here, we investigated brain phenotyping in patients with schizophrenia (SZ) and major depressive disorder (MDD) by using electroencephalography (EEG) and conducted machine-learning-based classification of the two diseases by using EEG components. Materials and Methods: We enrolled healthy controls (HCs) (n = 30) and patients with SZ (n = 34) and MDD (n = 33). An auditory P300 (AP300) task was performed, and the N1 and P3 components were extracted. Two-group classification was conducted using linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Positive and nega

SUBMITTER: Jang KI 

PROVIDER: S-EPMC8549692 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature