Epilepsy detection with artificial neural network based on as-fabricated neuromorphic chip platform
Y. H. Liu, Y. H. Liu, L. Chen, X. W. Li, Yuancong Wu, Shuang Liu, Junjie Wang, S. G. Hu, Qi Yu, T. P. Chen, Junjie Wang, Junjie Wang
Abstract
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.