A Hybrid Machine Learning Approach Integrating PCA for Prediction of Epileptic and Psychogenic Seizures

Abstract

Accurate differentiation between epileptic seizures (ES) and psychogenic non-epileptic seizures (PNES) remains a significant clinical challenge, with misdiagnosis rates reaching 20-30% in specialized epilepsy centers. This study proposes a hybrid machine learning framework integrating Principal Component Analysis (PCA) for dimensionality reduction with ensemble classification methods to predict and distinguish between epileptic and psychogenic seizures using electroencephalogram (EEG) signals. The methodology employs a multi-stage pipeline consisting of signal preprocessing, feature extraction from time-domain, frequency-domain, and nonlinear domains, PCA-based dimensionality reduction, and classification using Support Vector Machines (SVM), Random Forest (RF), and a novel hybrid ensemble model. Experimental validation was conducted using the Temple University Hospital EEG Corpus and clinical datasets comprising 847 patients. The proposed hybrid PCA-ensemble approach achieved classification accuracy of 94.7%, sensitivity of 93.2%, and specificity of 95.8%, outperforming standalone classifiers by 6-12%. The integration of PCA reduced computational complexity by 68% while preserving 97.3% of discriminative variance. These findings demonstrate the clinical viability of hybrid machine learning approaches for seizure type prediction, potentially reducing diagnostic delays and improving patient outcomes.