A New Neuromorphic Computing Approach for Epileptic Seizure Prediction

• URL: http://arxiv.org/abs/2102.12773v1
• Date: Thu, 25 Feb 2021 10:39:18 GMT
• Title: A New Neuromorphic Computing Approach for Epileptic Seizure Prediction
• Authors: Fengshi Tian, Jie Yang, Shiqi Zhao, Mohamad Sawan
• Abstract summary: CNNs are computationally expensive and power hungry. Motivated by the energy-efficient spiking neural networks (SNNs), a neuromorphic computing approach for seizure prediction is proposed in this work.
• Score: 4.798958633851825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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