Epilepsy detection with artificial neural network based on as-fabricated neuromorphic chip platform
Source abstract
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.
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Research context
OpenAlex topics: EEG and Brain-Computer Interfaces · Advanced Memory and Neural Computing · Neuroscience and Neural Engineering
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China 1 , Chengdu 610054, People’s Republic of China
Beijing Microelectronics Technology Institute (BMTI) 2 , Beijing 100076, People’s Republic of China
School of Electrical and Electronic Engineering, Nanyang Technological University 3 , Singapore 639798
Funding
National Natural Science Foundation of China
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