A DF-SSA analytical framework for revealing variations in multidimensional EEG features of epileptic seizures

Abstract

Epilepsy, recognized as the most prevalent chronic neurological disorder globally, markedly impacts affected individuals’ lives. Epileptic states are effectively detected by electroencephalography (EEG), yet the potential of machine learning to epilepsy’s neural mechanisms remains underutilized. In this study, an analytical framework employing the Deep Forest (DF) classifier and Sparrow Search Algorithm (SSA) was introduced to extract an optimal subset of multidimensional EEG features from the CHB-MIT and JHMCHH datasets, aimed at revealing significant variations in epilepsy. Three pivotal epileptic states-interictal, preictal, and ictal-were identified, with Relative Power (RP), Sample Entropy (SE), and Mutual Information (MI) computed for each. The importance ranking and selection of features were facilitated by the DF-SSA framework, leading to the identification of an optimal subset that achieved notable classification accuracies of 98.38 ± 0.42 % and 99.09 ± 0.91 %, which represent increases of 0.56 % and 2.04 % over the baseline, respectively. Additionally, significant changes within the beta and gamma bands across the three states were revealed by analyzing variations in cerebral cortex activity, with SE showcasing consistent patterns and a marked elevation from the interictal to preictal, and finally to ictal periods. Surprisingly, SE more readily distinguished the three epileptic states than RP, due to its sensitivity to signal complexity changes. Additionally, a reorganization of functional connectivity across all brain regions was uncovered to be triggered by seizures. Through this innovative analytical framework’s employment, three key epileptic seizure states were identified, revealing significant variations in brain electrical features and offering insights into epilepsy’s complexity.