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

• URL: http://arxiv.org/abs/2410.16613v1
• Date: Tue, 22 Oct 2024 01:55:02 GMT
• Title: Real-time Sub-milliwatt Epilepsy Detection Implemented on a Spiking Neural Network Edge Inference Processor
• Authors: Ruixin Lia, Guoxu Zhaoa, Dylan Richard Muir, Yuya Ling, Karla Burelo, Mina Khoei, Dong Wang, Yannan Xing, Ning Qiao,
• Abstract summary: This study aims to detect interictal and ictal periods of epileptic seizures using a spiking neural network (SNN) Our proposed method has a high test accuracy of 93.3% and 92.9% when classifying ictal and interictal periods. 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.
• Score: 5.021433741823472
• License:
• 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.

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