用多模态数据和深度学习预测癫痫发作，准确率超97%。
Demo: Multi-Modal Seizure Prediction System
- 融合脑电、心电等多源传感器数据，通过优化的深度学习模型实时分析。
- 在植入式设备限制下实现97%以上预测准确率，误报率低。
- 适合癫痫患者长期监测，尤其适用于药物难治性患者。
本演示介绍SeizNet系统，一种利用多模态传感器网络与深度学习技术预测癫痫发作的创新方案。全球约有6500万癫痫患者，其中许多存在药物难治性发作。SeizNet旨在提供高精度预警，使患者可采取预防措施，同时避免误报干扰。系统采用侵入式（颅内脑电，iEEG）或非侵入式（脑电，EEG；心电，ECG）传感器采集数据，通过优化用于边缘实时推理的深度学习算法处理，保障隐私并减少数据传输。SeizNet在满足植入设备尺寸与功耗限制的前提下，实现超过97%的癫痫发作预测准确率。
原文摘要 · Abstract (English)
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.
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