用多模态传感器和深度学习，提前一小时精准预测癫痫发作。
A Multi-Modal Non-Invasive Deep Learning Framework for Progressive Prediction of Seizures
- 融合脑电与心电数据，用深度学习做渐进式预警。
- 在29名患者上实现95%灵敏度、98%特异性和97%准确率。
- 可部署在边缘设备，保护隐私并节省电量，适合癫痫患者日常使用。
本文提出一种基于非侵入式多模态传感网络的深度学习框架，用于对癫痫发作进行渐进式（时间粒度细化至发作前）预测。全球约有6500万癫痫患者，其中部分对药物治疗无效。为应对这一挑战，我们倡导建立能及时预警的系统，使高风险人群可采取预防措施。本框架利用个性化数据，通过非侵入式脑电图（EEG）与心电图（ECG）传感器网络，结合先进深度学习技术提升预测精度。算法优化于边缘设备实时运行，降低隐私泄露风险和云端传输开销，有效延长电池寿命。系统可预测发作前15分钟至一小时内的倒计时时间，为干预争取关键时间窗口。多模态模型在29名患者的平均测试中达到95%敏感度、98%特异度和97%准确率。
原文摘要 · Abstract (English)
This paper introduces an innovative framework designed for progressive (granular in time to onset) prediction of seizures through the utilization of a Deep Learning (DL) methodology based on non-invasive multi-modal sensor networks. Epilepsy, a debilitating neurological condition, affects an estimated 65 million individuals globally, with a substantial proportion facing drug-resistant epilepsy despite pharmacological interventions. To address this challenge, we advocate for predictive systems that provide timely alerts to individuals at risk, enabling them to take precautionary actions. Our framework employs advanced DL techniques and uses personalized data from a network of non-invasive electroencephalogram (EEG) and electrocardiogram (ECG) sensors, thereby enhancing prediction accuracy. The algorithms are optimized for real-time processing on edge devices, mitigating privacy concerns and minimizing data transmission overhead inherent in cloud-based solutions, ultimately preserving battery energy. Additionally, our system predicts the countdown time to seizures (with 15-minute intervals up to an hour prior to the onset), offering critical lead time for preventive actions. Our multi-modal model achieves 95% sensitivity, 98% specificity, and 97% accuracy, averaged among 29 patients.
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