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.

翻译：本演示介绍了SeizNet，一种创新的癫痫发作预测系统，该系统得益于多模态传感器网络并利用深度学习（DL）技术。癫痫影响着全球约6500万人，其中许多人患有耐药性癫痫发作。SeizNet旨在提供高精度警报，使个体能够采取预防措施，而不会受到误报的干扰。SeizNet结合使用通过侵入式（颅内脑电图（iEEG））或非侵入式（脑电图（EEG）和心电图（ECG））传感器收集的数据，并由先进的DL算法进行处理，这些算法针对边缘实时推理进行了优化，确保了隐私并最大限度地减少了数据传输。SeizNet在癫痫发作预测中实现了> 97%的准确率，同时满足了植入式设备的尺寸和能量限制。