Real-time Sub-milliwatt Epilepsy Detection Implemented on a Spiking Neural Network Edge Inference Processor

TL;DR

This paper presents a real-time, low-power epileptic seizure detection system using a spiking neural network on a neuromorphic processor, achieving high accuracy and efficiency suitable for portable devices.

Contribution

It introduces a novel low-power SNN-based seizure detection method deployed on a neuromorphic processor, enabling real-time analysis on edge devices.

Findings

Achieved 93.3% and 92.9% accuracy in classifying seizure states.

Power consumption was only 87.4 μW (IO) + 287.9 μW (computational).

Demonstrated low-latency performance across multiple datasets.

Abstract

Analyzing electroencephalogram (EEG) signals to detect the epileptic seizure status of a subject presents a challenge to existing technologies aimed at providing timely and efficient diagnosis. In this study, we aimed to detect interictal and ictal periods of epileptic seizures using a spiking neural network (SNN). Our proposed approach provides an online and real-time preliminary diagnosis of epileptic seizures and helps to detect possible pathological conditions.To validate our approach, we conducted experiments using multiple datasets. We utilized a trained SNN to identify the presence of epileptic seizures and compared our results with those of related studies. The SNN model was deployed on Xylo, a digital SNN neuromorphic processor designed to process temporal signals. Xylo efficiently simulates spiking leaky integrate-and-fire neurons with exponential input synapses. Xylo has much lower energy requirments than traditional approaches to signal processing, making it an ideal platform for developing low-power seizure detection systems.Our proposed method has a high test accuracy of 93.3% and 92.9% when classifying ictal and interictal periods. At the same time, the application has an average power consumption of 87.4 uW(IO power) + 287.9 uW(computational power) when deployed to Xylo. Our method demonstrates excellent low-latency performance when tested on multiple datasets. Our work provides a new solution for seizure detection, and it is expected to be widely used in portable and wearable devices in the future.