Real-Time Detection of Epileptiform Activity and Seizure Onset Using EEG Signals and Machine Learning
Biomedical Engineering
Dhyeaya Dhiren Parmar
Abstract
Epilepsy affects 50 million people worldwide, and most seizures happen without warning, leaving patients at risk of serious injury. While researchers have tried to predict seizures using brain wave patterns, existing methods struggle to work across different patients.
I developed a machine learning system that analyzes EEG signals and achieved 87.5% accuracy in detecting pre-seizure brain activity across multiple patients, with a 2-minute warning window.