Diagnosing Epilepsy using Entropy Measures and Embedding Parameters of EEG Signals

This study evaluated various entropy measures and embedding parameters applied to EEG signals to develop an automated classification system for distinguishing epileptic patients from healthy controls. Results showed that sample entropy, norm entropy, and several other measures achieved 97-100% classification accuracy using linear discriminant analysis, with robustness tested against noise.