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

Authors :
Y. H. Liu
L. Chen
X. W. Li
Y. C. Wu
S. Liu
J. J. Wang
S. G. Hu
Q. Yu
T. P. Chen
Y. Liu
Source :
AIP Advances, Vol 12, Iss 3, Pp 035106-035106-6 (2022)
Publication Year :
2022
Publisher :
AIP Publishing LLC, 2022.

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.

Subjects

Subjects :
Physics
QC1-999

Details

Language :
English
ISSN :
21583226
Volume :
12
Issue :
3
Database :
Directory of Open Access Journals
Journal :
AIP Advances
Publication Type :
Academic Journal
Accession number :
edsdoj.2d81a54e6a4f418280e48430beeb8825
Document Type :
article
Full Text :
https://doi.org/10.1063/5.0075761