Demo：多模态癫痫预测系统
Demo: Multi-Modal Seizure Prediction System
AI总结：
本研究提出SeizNet多模态癫痫预测系统，结合iEEG、EEG和ECG传感器数据，利用边缘优化的深度学习算法实现实时推理，在保持植入设备尺寸与能耗限制下达到>97%预测准确率。
摘要：
本演示提出了SeizNet，一种创新的癫痫发作预测系统，受益于多模态传感器网络并利用深度学习（DL）技术。癫痫影响全球约6500万人，其中许多人经历耐药性发作。SeizNet旨在提供高精度警报，使个人能够采取预防措施而不受误报干扰。SeizNet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））传感器收集的数据组合，并由针对边缘实时推理进行优化的高级DL算法处理，从而确保隐私并最小化数据传输。SeizNet在癫痫预测中实现了> 97%的准确率，同时保持了植入式设备的尺寸和能耗限制。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
