Ensemble Classification for Epileptic Seizure Prediction

Epilepsy is a central nervous system disease that affects loss of consciousness and convulsions. The testing of electrical activity in the brain is identified as EEG. Ability to predict the onset of epileptic seizures before their commence seems to be very useful for medication-assisted seizure protection. Electroencephalogram (EEG) signals are now used to predict epileptic seizures using machine learning techniques and statistical analysis. Pre- processing of EEG signals for dynamic range and extra functionality, but in the other hand, extraction are two real issues that have a negative effect on the system’s run time and correctness. Therefore, this paper proposes a model that provides a reliable method for both pre-processing and classification. It is implemented using FIR filter for preprocessing and machine learning classification methods. Among the variety of machine learning algorithms, this paper uses RF algorithm and BPNN for classification. After the classification, the results were compared, and it is revealed that Random Forest classifier gives 95% accuracy which is more efficient than Neural network via back propagation.