Title : Simulating epileptic seizure dynamics: A perturbed lorenz attractor approach with bessel filtering
Abstract:
In this study, we present a novel approach to model epileptic seizures using a perturbed Lorenz attractor combined with Bessel filtering. The Lorenz attractor, known for its chaotic behavior, is modified by introducing stochastic perturbations to simulate the irregular and complex patterns observed in epileptic EEG signals. By applying a Bessel filter, we enhance the signal's temporal characteristics, ensuring the preservation of critical information. Our results demonstrate that this methodology effectively captures the chaotic dynamics inherent in epileptic episodes, providing a clearer and more accurate representation of the signal. This approach holds significant potential for improving the diagnosis and understanding of epilepsy, paving the way for advanced treatment strategies.
Keywords: EEG, Bessel Filtering, Epileptic, Lorenz, Attractor, Chaos
