Schizophrenia detection via lobe-wise and overall EEG features using VMD and bayesian-optimized machine learning models

TL;DR

This study uses EEG signals and machine learning to detect schizophrenia with high accuracy, focusing on frontal and temporal lobe activity.

Contribution

A novel VMD-based feature extraction and Bayesian-optimized ML framework for schizophrenia detection using lobe-wise EEG analysis.

Findings

The proposed method achieved 96.7% accuracy on the MHRC dataset and 99.0% on the RepOD dataset.

Frontal lobe analysis showed 91.2% accuracy on MHRC and 99.4% on RepOD, highlighting its discriminative power.

Temporal lobe also demonstrated strong performance, supporting frontal-temporal dysconnectivity in schizophrenia.

Abstract

Schizophrenia (SCH) is a chronic and severe mental disorder that leads to significant cognitive and neurophysiological impairments, affecting daily life. Early diagnosis remains challenging as it relies on the manifestation of symptoms that develop over time. Electroencephalography (EEG), which measures brain activity, provides a promising avenue for early detection. In this study, two EEG datasets—the Mental Health Research Center (MHRC) and the Repository for Open Data (RepOD)—were employed to detect SCH. EEG signals were segmented into 8-second durations and decomposed using Variational Mode Decomposition (VMD) into 10 Intrinsic Mode Functions (IMFs). Multi-domain features extracted from IMFs were classified using nine machine learning (ML) and seven optimized ML (OML) classifiers. The proposed method achieved an accuracy (Ac) of 96.7% for the MHRC dataset using the Optimizable KNN classifier and 99.0% for the RepOD dataset using the Optimizable Ensemble classifier. To prevent data leakage, a strict subject-wise Leave-One-Out Cross-Validation (LOOCV) strategy was employed. Lobe-wise analysis showed that the frontal lobe achieved accuracies of 91.2% for MHRC using the Optimizable Ensemble and 99.4% for RepOD using the Optimizable Neural Network, with the temporal lobe also showing strong discriminative power. These findings align with established evidence of frontal–temporal dysconnectivity in SCH. Overall, the proposed VMD + OML framework offers a computationally efficient and clinically interpretable solution for early SCH detection using EEG signals.

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