An Accurate Epilepsy Diagnosis Approach Based on Fractal Dimension of Brain Rhythms and Chaos-Embedding

Abstract

Epilepsy is a neurological disorder characterized by unpredictable seizures that can severely affect patients’ quality of life. Diagnosis primarily relies on the analysis of the electroencephalogram (EEG), a complex, noisy, and nonstationary signal representing the summation of various brain rhythms (delta, theta, alpha, beta, and gamma). Since manual EEG analysis is difficult and time-consuming, automatic approaches are necessary to efficiently detect seizures and diagnose the disease. In this work, we propose a novel framework that integrates wavelets for brain rhythms estimation with Chaos-Embedding and the Higuchi fractal dimension to extract highly discriminative features. These features are then used in a robust representation space and classified using K-Nearest Neighbors is employed to differentiate between healthy patients, epileptic patients, and epileptic patients during seizures. Experimental results show that the proposed approach achieves near-perfect performance, outperforming existing methods and providing a promising tool for the automatic diagnosis of epilepsy based on brain rhythms.