Detection of Epileptic Seizures from EEG Data Using CEEMD Algorithm

Epilepsy is the second most common neurological disorder characterized by sudden electrical discharges of the brain after stroke according to the World Health Organization. Absence epilepsy is one of the most common types of epilepsy and is majorly based on the detection of generalised spike and wave discharges in Electroencephalogram (EEG) signals. Electroencephalography is a common measurement technique used to assess brain activity, containing information about different physiological states of the brain. In this study, various statistical features are extracted from EEG signal segments decomposed using the Complete Ensemble Mode Decomposition technique and seizure classification is performed using machine learning techniques. The effectiveness of the proposed algorithm is validated with performance metrics and shows promising success compared to traditional studies.