DCM-ML: An Electroencephalography-based Classifier for Early Diagnosis of Schizophrenia Based on Dynamic Connectivity...

Early diagnosis of schizophrenia (SZ) remains challenging due to the subjective nature of clinical assessments and the heterogeneity of symptoms. There is a pressing need for objective, scalable, and non-invasive diagnostic tools to complement traditional methods. This study aimed to propose a machine learning (ML) framework that utilizes dynamic connectivity matrices (DCMs) derived from event-related potentials (ERPs) for SZ classification.

ERP data from 81 participants, including 49 patients with SZ and 32 healthy controls, were sourced from a publicly accessible and anonymized dataset. Granger causality was employed to compute 64×64 directional connectivity matrices, capturing inter-electrode information flow. Feature selection through t-tests identified 2,777 significant connectivity differences (P<0.05), which were subsequently used to train a random forest (RF) classifier. To address class imbalance, balanced training subsets were created. Additionally, the model's robustness was evaluated across varying levels of white Gaussian noise (0% to 45%).

The RF classifier demonstrated high diagnostic accuracy (99.24%), sensitivity (98.34%), specificity (99.73%), and an F1-score of 98.91% across 100 iterations, effectively minimizing the risks of overfitting. Its performance remained robust across various train-test splits and substantial noise levels, with an F1-score of 92% even with 45% white Gaussian noise. Feature selection significantly enhanced noise resilience and classification stability. Connectivity analysis revealed that central (Cz, FCz), occipito-parietal (PO3, Oz), and inferior (Iz) regions were key discriminators, indicating disrupted fronto-temporal and sensory integration networks in individuals with SZ.

This study highlights the feasibility of ML-driven ERP connectivity analysis as a non-invasive tool for early SZ detection. Achieving near-perfect accuracy, the model demonstrates strong generalizability, interpretability, and clinical scalability, outperforming deep learning counterparts while relying on a minimal, targeted feature set. These findings underscore the diagnostic relevance of fronto-central and occipito-parietal connectivity patterns. While promising as a non-invasive diagnostic adjunct, future validation on larger, demographically diverse cohorts is essential.