Sparse control-inspired features generation for Schizophrenia diagnosis - Pôle Automatique et Diagnostic Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

Sparse control-inspired features generation for Schizophrenia diagnosis

Résumé

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.
Fichier principal
Vignette du fichier
root.pdf (465.59 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04051822 , version 1 (30-03-2023)

Identifiants

  • HAL Id : hal-04051822 , version 1

Citer

Houssem Meghnoudj, Bogdan Robu, Mazen Alamir. Sparse control-inspired features generation for Schizophrenia diagnosis. IFAC WC 2023 - 22nd IFAC World Congress, IFAC, Jul 2023, Yokohama, Japan. ⟨hal-04051822⟩
71 Consultations
23 Téléchargements

Partager

Gmail Facebook X LinkedIn More