Sparse control-inspired features generation for Schizophrenia diagnosis⋆

Abstract

In this paper, a recent control-oriented features generation method is used to diagnose schizophrenia using electroencephalogram (EEG) signals. The methodology has already been used for the diagnosis of Parkinson's disease with competitive results. The method is directly inspired by the functioning of the brain and is mainly based on optimal control theory and sparse optimisation. An appealing feature in the proposed solution is that it allows to combine both frequency and temporal-related aspects of the signal which are known to be detrimental in this context. The proposed solution is evaluated on a publicly available dataset that includes 81 subjects, of which 49 suffer from schizophrenia and 32 are healthy. Results show that by mean of only one extracted feature, fed to a linear discriminant analysis (LDA) classifier, high accuracy separation is obtained. Several validity tests were carried out to assess the statistical relevance of the findings.