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Journal of Vibration Testing and System Dynamics 8(4) (2024) 429--441 | DOI:10.5890/JVTSD.2024.12.005

Zhiyi Jing, Ying Wu, Yeyin Xu

School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an, 710049, PR China

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Abstract

Epilepsy is a highly prevalent neurological disorder worldwide, and the automatic detection of epileptic activity in clinical practice is a significant focus of researchers. In this study, we construct a high-performance model for normal and epileptic electroencephalogram signals using a convolutional neural network architecture called Shallow ConvNet based on the CHB-MIT dataset. The model achieves a maximum classification accuracy of 98\%. We also investigate the effects of different data slice and sample sizes on the model performance. Results show that larger data slice sizes can improve accuracy and generalization ability to a certain extent, while increasing the sample size of the training set only improves accuracy but not generalization ability. The best overall performance is achieved by the classifier trained with a slice size of 5 seconds and a training set sample size of 500. These findings provide theoretical guidance for the clinical application of automatic epilepsy detection.

Acknowledgments

This study is funded by the National Nature Science Foundation of China (Grant No. 1213012 and 11972275).

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