Epilepsy is a common disease of the nervous system. Timely prediction of
seizures and intervention treatment can significantly reduce the accidental
injury of patients and protect the life and health of patients. This paper
presents a neuromorphic Spiking Convolutional Transformer, named Spiking
Conformer, to detect and predict epileptic seizure segments from scalped
long-term electroencephalogram (EEG) recordings. We report evaluation results
from the Spiking Conformer model using the Boston Children's Hospital-MIT
(CHB-MIT) EEG dataset. By leveraging spike-based addition operations, the
Spiking Conformer significantly reduces the classification computational cost
compared to the non-spiking model. Additionally, we introduce an approximate
spiking neuron layer to further reduce spike-triggered neuron updates by nearly
38% without sacrificing accuracy. Using raw EEG data as input, the proposed
Spiking Conformer achieved an average sensitivity rate of 94.9% and a
specificity rate of 99.3% for the seizure detection task, and 96.8%, 89.5% for
the seizure prediction task, and needs >10x fewer operations compared to the
non-spiking equivalent model.

本文提出了一种名为 Spiking Conformer 的神经形态学脉冲卷积变压器，用于从头皮长期脑电图（EEG）记录中检测和预测癫痫发作片段，并通过融合基于脉冲的加法运算显著降低了分类计算成本，同时在使用原始 EEG 数据作为输入时，在癫痫检测任务中实现了 94.9% 的平均灵敏度率和 99.3% 的特异性率，在癫痫预测任务中实现了 96.8% 和 89.5% 的结果，并且与非脉冲等效模型相比，所需运算量减少了超过 10 倍。