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

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Abstract

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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Shyu, K. K., Huang, S. C., Lee, L. H., & Lee, P. L. (2023). Less Parameterization Inception-Based End to End CNN Model for EEG Seizure Detection. IEEE Access, 11, 49172–49182. https://doi.org/10.1109/ACCESS.2023.3277634

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