"Brain state network dynamics in pediatric epilepsy: Chaotic attractor transition ensemble network".

Traditional scalp EEG signal analysis in pediatric epilepsy is limited by poor spatial resolution, susceptibility to noise and artifacts, and difficulty in accurately localizing epileptic activity, especially from deep or interconnected brain regions. Additionally, such methods often overlook the dynamic nature of brain states and seizure propagation, while reliance on visual inspection introduces variability in interpretation. These limitations hinder precise seizure detection and the mechanistic understanding of brain network dynamics. Here, we offer an alternative approach that addresses these challenges, and eventually enables effective clinical interventions to improve patient outcomes. By incorporating chaos and dynamical systems theory, we present and validate a novel ensemble framework, Chaotic Attractor Transition Ensemble Network for Epilepsy (CATE-NET), which identifies neuro-dynamical signatures underlying pediatric epilepsy, facilitating the discrimination between physiological brain activity and seizure-induced signal irregularities. CATE-NET is modularly designed to leverage nonlinear dynamics of EEG signals and chaotic attractors, particularly the Rössler chaotic attractor to model scalp EEG data. This is followed by a long short-term memory network module for the automatic analysis of brain states. The final module utilizes probabilistic graphing to map the output of the LSTM to state transition graphs, between pre-ictal, inter-ictal, ictal, and ictal-free brain states. Model metrics include a classification accuracy of 0.98, sensitivity of 0.76, specificity of 0.84, and an AUC value of 0.91 when distinguishing among ictal, inter-ictal, and ictal-free brain states. Additionally, the system integrates flexible horizon windows of 10, 20, and 30 min to determine brain state transitions. We demonstrate that nonlinear dynamics present in epileptic brain states derived from the Rössler chaotic attractor are effective features to compute brain state analysis and visualize pediatric epileptic brain state topology. CATE-NET introduces a novel platform for brain state analysis, feature extraction, and topological mapping in pediatric epilepsy by combining chaotic attractors, deep learning, and probabilistic graphing. By integrating explainable AI (XAI), the framework clarifies how chaotic attractor patterns and probabilistic transitions contribute to brain state classifications, seizure state dynamic transitions. This approach reveals the spatial organization and EEG signal dynamics of pediatric epileptic brain states, allowing integration with clinical EEG equipment to potentially improve seizure management and real time decision making.