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.

翻译：本文提出SeizNet，一种利用深度学习方法和可植入传感器网络预测癫痫发作的闭环系统。尽管药物治疗对部分癫痫患者有效（全球约6500万人受影响），但三分之一的患者属于药物难治性癫痫。为减轻癫痫发作的影响，研究人员开发了预测系统，可在发作前通知患者，使其采取预防措施。SeizNet采用深度学习技术，融合多模态记录数据（特别是颅内脑电图和心电图传感器数据），在保持极高灵敏度的同时，显著提高了癫痫发作预测的特异性。SeizNet的深度学习算法针对边缘端高效实时执行而设计，最大程度降低了与云端解决方案相关的数据隐私风险、传输开销和能耗问题。实验结果表明，SeizNet在所有指标上均优于传统单模态及非个性化预测系统，癫痫发作预测准确率高达99%，为难治性癫痫治疗开辟了全新途径。