Demo: Multi-Modal Seizure Prediction System

Ali Saeizadeh, Pietro Brach del Prever, Douglas Schonholtz, Raffaele Guida, Emrecan Demirors +3 more
11/1/2024
eess.SPcs.LG

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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.

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Cite this paper

@article{saeizadeh2024demo,
  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},
  year   = {2024},
  eprint = {2411.05817},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2411.05817v2}
}

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