This demo presents SeizNet, an innovative system for predicting epileptic seizures benefiting from a multi-modal sensor network and utilizing Deep Learning (DL) techniques. Epilepsy affects approximately 65 million people worldwide, many of whom experience drug-resistant seizures. SeizNet aims at providing highly accurate alerts, allowing individuals to take preventive measures without being disturbed by false alarms. SeizNet uses a combination of data collected through either invasive (intracranial electroencephalogram (iEEG)) or non-invasive (electroencephalogram (EEG) and electrocardiogram (ECG)) sensors, and processed by advanced DL algorithms that are optimized for real-time inference at the edge, ensuring privacy and minimizing data transmission. SeizNet achieves > 97% accuracy in seizure prediction while keeping the size and energy restrictions of an implantable device.
@article{arxiv.2411.05817,
title = {Demo: Multi-Modal Seizure Prediction System},
author = {Ali Saeizadeh and Pietro Brach del Prever and Douglas Schonholtz and Raffaele Guida and Emrecan Demirors and Jorge M. Jimenez and Pedram Johari and Tommaso Melodia},
journal= {arXiv preprint arXiv:2411.05817},
year = {2024}
}
Comments
1 page, 1 figure, Proceedings of the IEEE 20th International Conference on Body Sensor Networks (BSN), October 2024