  Several high specificity and sensitivity seizure prediction methods with
convolutional neural networks (CNNs) are reported. However, CNNs are
computationally expensive and power hungry. These inconveniences make CNN-based
methods hard to be implemented on wearable devices. Motivated by the
energy-efficient spiking neural networks (SNNs), a neuromorphic computing
approach for seizure prediction is proposed in this work. This approach uses a
designed gaussian random discrete encoder to generate spike sequences from the
EEG samples and make predictions in a spiking convolutional neural network
(Spiking-CNN) which combines the advantages of CNNs and SNNs. The experimental
results show that the sensitivity, specificity and AUC can remain 95.1%, 99.2%
and 0.912 respectively while the computation complexity is reduced by 98.58%
compared to CNN, indicating that the proposed Spiking-CNN is hardware friendly
and of high precision.
