Epilepsy detection with artificial neural network based on as-fabricated neuromorphic chip platform

• Main Authors: Liu, Yihe, Chen, L., Li, X. W., Wu, Yuancong, Liu, S., Wang, Junjie, Hu, Shaogang, Yu, Qi, Chen, Tupei, Liu, Yang
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Electrical and electronic engineering; Epilepsy Detection; Neural Network Model
• Online Access: https://hdl.handle.net/10356/165034
• Institution: Nanyang Technological University

Epilepsy is a serious neurological condition caused by a sudden abnormality of brain neurons. An accurate epilepsy detection based on electroencephalogram (EEG) signals can provide vital information for diagnosis and treatment. In this study, we propose a lightweight automatic epilepsy detection system with artificial neural network based on our as-fabricated neuromorphic chip. The proposed system utilizes a neural network model to achieve high-accuracy detection without the need for epilepsy-related prior knowledge. The model uses a filter module and a convolutional neural network to preprocess the raw EEG signal and uses a long short-term memory recurrent neural network and a fully connected network as the classifier. In the examination, the classification accuracy of the normal cases and seizures approaches 99.10%, and the accuracy of the normal cases, and interictal and seizure cases can reach 94.46%. This design provides possible epilepsy detection in wearable or portable devices.