A New Neuromorphic Computing Approach for Epileptic Seizure Prediction

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-ef...

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Bibliographic Details
Published in2021 IEEE International Symposium on Circuits and Systems (ISCAS) pp. 1 - 5
Main Authors Tian, Fengshi, Yang, Jie, Zhao, Shiqi, Sawan, Mohamad
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2021
Subjects
Computational complexity
Convolutional neural networks
EEG
Electroencephalography
Energy efficiency
Epileptic seizure prediction
Neuromorphic computing
Neuromorphic engineering
Sensitivity
Spiking convolutional neural network
Wearable computers
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Summary: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.
ISBN:9781728192017
1728192013
ISSN:2158-1525
2158-1525
DOI:10.1109/ISCAS51556.2021.9401560