Quantum-inspired feature extraction model from EEG frequency waves for enhanced schizophrenia detection

Ateke Goshvarpour

Chaos, Solitons & Fractals, 2025, vol. 196, issue C

Abstract: Schizophrenia diagnosis remains challenging due to the reliance on subjective clinical assessments and the lack of robust, objective biomarkers. Current neuroimaging methods are often expensive, time-consuming, and may lack specificity, highlighting the need for the development of scalable and accurate diagnostic tools. This study investigates the feasibility of using electroencephalogram (EEG) frequency waves as biomarkers for the detection of schizophrenia, employing a quantum-based feature extraction methodology. The primary objective of this research is to develop an advanced detection methodology that integrates quantum-based feature extraction with sophisticated channel and feature selection techniques. This approach aims to enhance the accuracy and reliability of schizophrenia diagnosis by identifying the most informative EEG channels and features for classification purposes.

Keywords: Directed quantum pattern; Schizophrenia; Electroencephalogram frequency waves; Channel selection; Feature selection; Classification (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:196:y:2025:i:c:s096007792500414x

DOI: 10.1016/j.chaos.2025.116401

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