Hardware modeling evaluation demonstrates 85% accuracy for absent seizure detection in human models, highlighting the potential of memristive fabrics for energy-constrained medical devices.
Echo state networks (ESN) or reservoirs, are random, recurrent neural network topologies that integrate temporal data over short time windows by operating on the edge of chaos. Recently, there is a significant effort on the mathematical modeling and software topologies of the reservoirs. However, hardware reservoir fabrics are essential to deploy in energy constrained environments. In this paper, we investigate a hardware reservoir with bi-stable memristive synapses. In particular, we demonstrate a scalable hardware model for detecting real-time epileptic seizures in human models. The performance of the proposed reservoir hardware is evaluated for absent seizure signals with 85% accuracy.
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Merkel et al. (2014) studied this question.
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