# Demo: Multi-Modal Seizure Prediction System

arXiv: https://arxiv.org/abs/2411.05817 · HTML: https://mlsanity.com/paper/2411.05817/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 3 references that are in the map.
- First posted on arXiv 1 Nov 2024.

## Builds on (most cited first, 1 of 1)

- [SeizNet: An AI-enabled Implantable Sensor Network System for Seizure Prediction](https://mlsanity.com/paper/2401.06644/) (2024)

## Abstract (arXiv)

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.