面向实时的移动癫痫发作预测的忆阻深度学习系统
Towards Memristive Deep Learning Systems for Real-time Mobile Epileptic Seizure Prediction
摘要：
癫痫发作的不可预测性继续困扰着许多耐药性癫痫患者。由于最近的技术进步，已经做出了相当大的努力，使用不同的硬件技术来实现用于实时的【形】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（MDLS）在边缘上执行实时的【形】癫痫发作预测的可行性。使用MemTorch仿真框架和波士顿儿童医院（CHB）-麻省理工学院（MIT）数据集，我们确定各种模拟MDLS配置的性能。在65 nm CMOS工艺中，最佳结构的平均灵敏度为77.4%，接收器工作特性曲线下的面积为0.85，可在1.408 ms内处理7,680个样本的脑电图（EEG）谱图，同时消耗0.0133 W，占用0.1269 mm $^2$的面积。
英文摘要：
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.
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