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.
翻译:缉获的不可预测性继续使许多抗药性癫痫患者感到不安,由于最近的技术进步,已作出相当大的努力,利用各种硬件技术实现实时检测和预测缉获的智能装置,在本文件中,我们调查使用记忆深学习系统(MDLS)在边缘进行实时癫痫收缴预测的可行性,利用MemTorrch模拟框架和儿童医院波士顿-Massachusetts(CHB)-Massachusetts Institute(MIT)数据集,我们确定各种模拟MDLS配置的性能,报告平均灵敏度为77.4%,接收器操作特征曲线下区域为0.85,以最佳配置处理电子脑图光谱(EEG),1 408米的7 680个样本,同时消耗0.0133瓦,在65毫米的辅助金属-氧化物-半导体(CMOS)进程中占据0.1269毫米2美元的区域。