The unpredictability of seizures continues to distress many people with drug-resistant epilepsy. On account of recent technological advances, considerable efforts have been made using different hardware technologies to realize smart devices for the real-time detection and prediction of seizures. In this paper, we investigate the feasibility of using Memristive Deep Learning Systems (MDLSs) to perform real-time epileptic seizure prediction on the edge. Using the MemTorch simulation framework and the Children's Hospital Boston (CHB)-Massachusetts Institute of Technology (MIT) dataset we determine the performance of various simulated MDLS configurations. An average sensitivity of 77.4% and a Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.85 are reported for the optimal configuration that can process Electroencephalogram (EEG) spectrograms with 7,680 samples in 1.408ms while consuming 0.0133W and occupying an area of 0.1269mm$^2$ in a 65nm Complementary Metal-Oxide-Semiconductor (CMOS) process.

中文翻译：

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面向忆阻式深度学习系统的实时移动癫痫发作预测

癫痫病的不可预测性继续困扰着许多患有耐药性癫痫的人。由于最近的技术进步，已经使用不同的硬件技术做出了相当大的努力以实现用于实时检测和预测癫痫发作的智能设备。在本文中，我们研究了使用忆阻深度学习系统（MDLSs）在边缘执行实时癫痫发作预测的可行性。使用MemTorch模拟框架和波士顿儿童医院（CHB）-麻省理工学院（MIT）数据集，我们可以确定各种模拟MDLS配置的性能。平均灵敏度为77.4％，接收器工作特性曲线（AUROC）下的面积为0。