Efficient and generalizable cross-patient epileptic seizure detection through a spiking neural network

Abstract

IntroductionEpilepsy is a global chronic disease that brings pain and inconvenience to patients, and an electroencephalogram (EEG) is the main analytical tool. For clinical aid that can be applied to any patient, an automatic cross-patient epilepsy seizure detection algorithm is of great significance. Spiking neural networks (SNNs) are modeled on biological neurons and are energy-efficient on neuromorphic hardware, which can be expected to better handle brain signals and benefit real-world, low-power applications. However, automatic epilepsy seizure detection rarely considers SNNs.MethodsIn this article, we have explored SNNs for cross-patient seizure detection and discovered that SNNs can achieve comparable state-of-the-art performance or a performance that is even better than artificial neural networks (ANNs). We propose an EEG-based spiking neural network (EESNN) with a recurrent spiking convolution structure, which may better take advantage of temporal and biological characteristics in EEG signals.ResultsWe extensively evaluate the performance of different SNN structures, training methods, and time settings, which builds a solid basis for understanding and evaluation of SNNs in seizure detection. Moreover, we show that our EESNN model can achieve energy reduction by several orders of magnitude compared with ANNs according to the theoretical estimation.DiscussionThese results show the potential for building high-performance, low-power neuromorphic systems for seizure detection and also broaden real-world application scenarios of SNNs.

LMA-EEGNet: A Lightweight Multi-Attention Network for Neonatal Seizure Detection Using EEG signals

“…Although our model demonstrated outstanding performance in several aspects, there are some limitations. Good generalization capability is crucial for a neonatal seizure detection model [34], and our model's training and validation were performed on a specific dataset. Future research needs to validate the model's generalization ability on a broader range of datasets.…”

Epilepsy detection based on multi-head self-attention mechanism

“…It’s noteworthy that the PANN model proposed by the Z. Zhang team [ 32 ] exhibited outstanding performance in cross-patient epilepsy detection tasks, with an accuracy only 0.46% lower than our model and a specificity only 0.40% lower. Subsequently, the Z. Zhang team [ 33 ] introduced another cross-patient epilepsy detection model based on Pulse Neural Networks (EESNN). While this model showed improvement in specificity, it experienced a decline in sensitivity.…”