A 96.2nJ/class Neural Signal Processor with Adaptable Intelligence for Seizure Prediction

Abstract

Epilepsy is a common neurodegenerative disease that affects more than 50 million people worldwide. Closed-loop neuromodulation is a promising solution to epileptic seizure control through an implantable device that delivers stimulation when seizures are sensed. Figure 33.2.1 shows an overview of a closed-loop neuromodulation system that includes a neural-signal acquisition unit for extracting EEGs, a neural signal processor for sensing seizures, and a stimulation unit for electrical stimulation. For epileptic states, a seizure onset indicates where a seizure begins, followed by intense brain activity. Several seizure detectors [1] [2] having reasonable performance have been proposed to sense seizures after onset. However, patients may still suffer from epileptic syndromes, depending on the severity of the seizures. The syndromes can be eliminated if the seizures can be predicted before onset. This also reduces the amount of required stimulation current, thereby extending the battery life of the implantable device. However, the computational complexity of an accurate seizure prediction algorithm is very high, considering a machine learning kernel is usually embedded to tackle the time-varying characteristics of EEGs adaptively. Up to tens of minutes is needed for seizure prediction on a high-end CPU and a real-time, energy-efficient seizure predictor has never been demonstrated in the literature. This work presents a neural signal processor with adaptable intelligence for real-time seizure prediction with low energy. © 2022 IEEE.