Real-time sub-milliwatt epilepsy detection implemented on a spiking neural network edge inference processor

Ruixin Li, Guoxu Zhao, Dylan Richard Muir, Yuya Ling, Karla Burelo, Mina Khoe, Dong Wang, Yannan Xing, Ning Qiao

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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 requirements 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 μW (IO power) + 287.9 μW (compute 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.

Original languageEnglish
Article number109225
Number of pages10
JournalComputers in Biology and Medicine
Volume183
Early online date16 Oct 2024
DOIs
Publication statusPublished - Dec 2024
Externally publishedYes

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