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.
@article{arxiv.2102.12773,
title = {A New Neuromorphic Computing Approach for Epileptic Seizure Prediction},
author = {Fengshi Tian and Jie Yang and Shiqi Zhao and Mohamad Sawan},
journal= {arXiv preprint arXiv:2102.12773},
year = {2022}
}
Comments
Accepted to 2021 IEEE International Symposium on Circuits and Systems (ISCAS)