Continuous ambulatory epilepsy detection system incorporating feature engineering

Abstract

Epilepsy is a prevailing disease that affects people from different age brackets and demographic backgrounds. It leads to uncontrollable onset of seizures and can result in severe neurological injuries. In this paper, we devised a novel seizure prediction system as a real-time early warning system for patients. By using real-time transmissible, portable, and wireless devices, we can acquire raw data from scalp electroencephalogram (EEG) without any pre-processing for the input. After pre-processing, the data is fed into selected prediction algorithms based on literature review and a combination of methodologies. After times of iteration, our result shows a promising performance, with an accuracy rate of 100% Bonn dataset. We further designed a hardware data acquisition apparatus (with our program built-in) to smooth and ameliorate the data acquisition process when eliminating overmuch electrodes, which may serve as a promising seizure onset detecting device in the new era.