Lammie, Corey, Xiang, Wei, and Rahimi Aghadi, Mostafa (2021) Towards memristive deep learning systems for real-time mobile epileptic seizure prediction. In: Proceedings of the IEEE International Symposium on Circuits and Systems. From: ISCAS 2021: IEEE International Symposium on Circuits and Systems, 22-28 May 2021, Daegu, Korea.

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Abstract

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.

Item ID:	68689
Item Type:	Conference Item (Research - E1)
ISBN:	978-1-7281-9201-7
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Date Deposited:	14 Jul 2021 23:09
FoR Codes:	42 HEALTH SCIENCES > 4203 Health services and systems > 420310 Health surveillance @ 25% 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning @ 25% 40 ENGINEERING > 4018 Nanotechnology > 401804 Nanoelectronics @ 50%
SEO Codes:	20 HEALTH > 2003 Provision of health and support services > 200303 Health surveillance @ 100%
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