Epileptic Seizure Prediction From EEG Signal Recording Using Energy and Dispersion Entropy with SVM Classifier

Epilepsy is one of the most common chronic diseases in the world. Nearly 50 million individuals worldwide are afflicted by this condition. The brain seizures are caused by excessive synchronization of neurons electrical excitation which spreads to the whole brain area. This disease can affect anyone, regardless of their gender, age, or racial group. Hence, individuals with epilepsy must adapt their lives around their illness carefully. A seizure may bring some injury or even make life risky to the patient or others, mainly dealing with the heavy machinery industry or driving vehicles. The purpose of this research is to design an epileptic seizure prediction system to warn patients before an attack occurs. The Seizure Prediction Horizon (SPH) used in this research is 10 minutes. The features chosen are energy and Dispersion Entropy (DispEN) acquired from Discrete Wavelet Transform (DWT) signal decomposition. Finally, the classification and seizure prediction technique we deemed best is the Support Vector Machine (SVM). The highest accuracy achieved by this model is 86.2% on a binary classification scenario Ictal and Non-Ictal, which is the Non-Ictal state is a combination of Normal and Pre-ictal state. While the highest sensitivity performance of our prediction system is $\mathbf{9 1 \%}$, with the lowest FPR average of 0.45.