Optimization-Driven Intelligent Diagnosis of Neuropsychiatric Conditions Using EEG Analytics

Abstract

Neuropsychiatric disorders such as depression, schizophrenia, anxiety disorders, bipolar disorder, and attention-deficit/hyperactivity disorder represent a major global healthcare burden due to their increasing prevalence and complex neurological manifestations. Early and accurate diagnosis remains challenging because traditional clinical assessments often rely on subjective evaluations and behavioral observations. Electroencephalography (EEG) has emerged as an effective non-invasive neuroimaging technique capable of capturing neural activity associated with cognitive and psychiatric abnormalities. However, EEG signals are characterized by high dimensionality, nonlinearity, noise, and temporal variability, making automated diagnosis a difficult task. To address these challenges, this study proposes an Optimization-Driven Intelligent Diagnosis Framework for Neuropsychiatric Conditions Using EEG Analytics (OIDF-NC) that integrates EEG signal processing, optimization-based feature selection, deep representation learning, and intelligent classification mechanisms.