SeizNet: An AI-enabled Implantable Sensor Network System for Seizure Prediction

TL;DR

SeizNet is an AI-powered implantable sensor network system that predicts epileptic seizures with high accuracy using deep learning on multimodal data, enabling real-time, privacy-preserving seizure prediction for drug-resistant epilepsy patients.

Contribution

This paper introduces SeizNet, a novel deep learning-based system integrating intracranial EEG and ECG sensors for accurate, real-time seizure prediction at the edge, improving over existing methods.

Findings

Achieves up to 99% accuracy in seizure prediction.

Outperforms traditional single-modality and non-personalized systems.

Operates efficiently in real-time at the edge, ensuring privacy.

Abstract

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.