Early Prediction of Epilepsy Seizures VLSI BCI System

TL;DR

This paper presents a wireless wearable BCI system for early epilepsy seizure prediction, achieving over 71% accuracy and a prediction lead time of approximately 14.56 seconds, aiming to improve patient safety and medical response.

Contribution

It introduces a novel portable, minimally invasive wireless BCI system for early seizure prediction, enhancing accuracy and usability over existing methods.

Findings

Achieved 71% prediction accuracy

Provided an average prediction lead time of 14.56 seconds

Enabled remote monitoring via external devices

Abstract

Controlling the surrounding world and predicting future events has always seemed like a dream, but that could become a reality using a Brain-Computer/Machine Interface (BCI/BMI). Epilepsy is a group of neurological diseases characterized by epileptic seizures. It affects millions of people worldwide, with 80 percent of cases occurring in developing countries. This can result in accidents and sudden, unexpected death. Seizures can happen undetectably in newborns, comatose, or motor-impaired patients, especially due to the fact that many medical personnel is not qualified for EEG signal analysis. Therefore, a portable automated detection and monitoring solution is in high demand. Thus, in this study, a system of a wireless wearable adaptive for early prediction of epilepsy seizures is proposed, works via minimally invasive wireless technology paired with an external control device (e.g., a doctors smartphone), with a higher than standard accuracy 71 percent and prediction time (14.56 sec). This novel architecture has not only opened new opportunities for daily usable BCI implementations, but they can also save a life by helping to prevent a seizure fatal consequences