  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.
