dcm-ml: an electroencephalography-based classifier for early diagnosis of schizophrenia based on dynamic connectivity matrices and machine learning algorithms

Introduction: 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.methods: 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%).results: 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.conclusion: 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.