Demo: Multi-Modal Seizure Prediction System

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Abstract

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.

Citation

Saeizadeh, Arman, Pietro Brach del Prever, Daniel Schonholtz, Raffaele Guida, Emrecan Demirors, Jose M. Jimenez, Pedram Johari, and Tommaso Melodia. 2024. “Demo: Multi-Modal Seizure Prediction System.” In IEEE 20th International Conference on Body Sensor Networks (BSN).

@inproceedings{saeizadeh2024bsn_demo,
  author    = {Saeizadeh, Arman and Brach del Prever, Pietro and Schonholtz, Daniel and Guida, Raffaele and Demirors, Emrecan and Jimenez, Jose M. and Johari, Pedram and Melodia, Tommaso},
  title     = {{Demo: Multi-Modal Seizure Prediction System}},
  booktitle = {IEEE 20th International Conference on Body Sensor Networks (BSN)},
  year      = {2024},
  month     = oct,
  url       = {https://arxiv.org/pdf/2411.05817}
}