Less Parameterization Inception-Based End to End CNN Model for EEG Seizure Detection

Many deep-learning-based seizure detection algorithms have achieved good classification, which usually outperformed traditional machine-learning-based algorithms. However, the hand-engineered features increase the computational complexity and potentially have an ineffectiveness problem for the categ...

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Bibliographic Details
Published in:IEEE access Vol. 11; p. 1
Main Authors: Shyu, Kuo-Kai, Huang, Szu-Chi, Lee, Lung-Hao, Lee, Po-Lei
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Biomedical monitoring
Brain modeling
Classification
Complexity
Convolutional neural networks
Datasets
Deep learning
Electroencephalography
end-to-end model
Epilepsy
Feature extraction
Machine learning
Modules
Parameterization
seizure detection
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:Many deep-learning-based seizure detection algorithms have achieved good classification, which usually outperformed traditional machine-learning-based algorithms. However, the hand-engineered features increase the computational complexity and potentially have an ineffectiveness problem for the category. Therefore, this paper proposes a novel end-to-end deep-learning model comprising an inception module and a residual module to analyze the multi-scales of original EEG signals and realize seizure detection without feature extraction. Experiments were conducted and evaluated on the Bonn dataset and the CHB-MIT dataset. In the subject-dependent experiments, our model achieved an average F1-score of 69.34% on the CHB-MIT dataset. In subject-independent experiments, our method achieved an average accuracy of 99.04% on the Bonn dataset and an average F1-score of 37.31% on the CHB-MIT dataset. A series of analyses confirmed that our proposed model has better classification performance and lower computational complexity than existing end-to-end seizure detection models.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3277634