Advanced Predictive Framework for Early Detection and Classification of Psychiatric Conditions Using EEG Data

Abstract

Psychiatric illnesses, such as depression, generalized anxiety disorder, and schizophrenia, tend to be characterized by mild neurophysiological markers that make early diagnosis difficult. The greatest limitation of present diagnostic approaches is the failure to detect such mild brainwave anomalies with good accuracy, especially during the early stages of the disorders. This research presents a new predictive model for the early classification and diagnosis of psychiatric diseases from Electroencephalogram (EEG) signals. The framework employs the use of the Archerfish Hunting Optimizer Tuned Spiking Neural Network (AHO-SNN). This hybrid approach combines the computational effectiveness of an evolution-inspired optimizer with spiking neural networks' (SNNs) temporal processing ability. The AHO algorithm is used to fine-tune the SNN's synaptic weights in order to make the SNN more sensitive to neural oscillations and cortical pathologies related to psychiatric disorders. The projected AHO-SNN results are precision 94%, f1-score 94%, accuracy 96%, and recall 92%. The outcomes reveal that the AHO-SNN approach obtains high diagnostic precision, separating psychiatric patients from healthy controls based on the patterns of neural activity, for instance, theta and alpha band anomalies. The technique has enormous potential to support improved early psychiatric diagnosis, facilitating timely interventions and customized treatment strategies. Future research will center on integrating multimodal biomarkers and real-time monitoring to further enhance diagnostic accuracy and increase clinical utility.