SeizNet: An AI-enabled Implantable Sensor Network System for Seizure
  Prediction
Ali Saeizadeh Douglas Schonholtz Daniel Uvaydov Raffaele Guida Emrecan Demirors Pedram Johari Jorge M. Jimenez Joseph S. Neimat Tommaso Melodia
In this paper, we introduce SeizNet, a closed-loop system for predicting
epileptic seizures through the use of Deep Learning (DL) method and implantable
sensor networks. While pharmacological treatment is effective for some epilepsy
patients (with ~65M people affected worldwide), one out of three suffer from
drug-resistant epilepsy. To alleviate the impact of seizure, predictive systems
have been developed that can notify such patients of an impending seizure,
allowing them to take precautionary measures. SeizNet leverages DL techniques
and combines data from multiple recordings, specifically intracranial
electroencephalogram (iEEG) and electrocardiogram (ECG) sensors, that can
significantly improve the specificity of seizure prediction while preserving
very high levels of sensitivity. SeizNet DL algorithms are designed for
efficient real-time execution at the edge, minimizing data privacy concerns,
data transmission overhead, and power inefficiencies associated with
cloud-based solutions. Our results indicate that SeizNet outperforms
traditional single-modality and non-personalized prediction systems in all
metrics, achieving up to 99% accuracy in predicting seizure, offering a
promising new avenue in refractory epilepsy treatment.