Artificial intelligence-based classification of schizophrenia: A high density electroencephalographic and support vector machine study.

ABSTRACT:

Background

Interview-based schizophrenia (SCZ) diagnostic methods are not completely valid. Moreover, SCZ-the disease entity is very heterogeneous. Supervised-Machine-Learning (sML) application of Artificial-Intelligence holds a tremendous promise in solving these issues.

Aims

To sML-based discriminating validity of resting-state electroencephalographic (EEG) quantitative features in classifying SCZ from healthy and, positive (PS) and negative symptom (NS) subgroups, using a high-density recording.

Settings and design

Data collected at a tertiary care mental-health institute using a cross-sectional study design and analyzed at a premier Engineering Institute.

Materials and methods

Data of 38-SCZ patients and 20-healthy controls were retrieved. The positive-nega

SUBMITTER: Tikka SK 

PROVIDER: S-EPMC7368447 | biostudies-literature | 2020 May-Jun

REPOSITORIES: biostudies-literature