Epileptic Seizure Classification in EEG Signals Using KNN and SVM

Epilepsy is a neurological disorder that affects millions of people worldwide, and accurate classification of epilepsy based on seizure type and epilepsy syndrome is crucial for effective treatment. However, distinguishing between different types of epilepsy can be challenging due to the complexity of EEG signals. This study investigated the effectiveness of using eight key features extracted from EEG signals in accurately classifying epilepsy using KNN and SVM algorithms, achieving an accuracy of 100% for both algorithms. The study's findings provide a promising approach to accurately classify epilepsy, which can potentially improve the accuracy of epilepsy classification and develop more effective treatment strategies for epilepsy patients.