Research on Automatic Detection of Epilepsy based on Sliding Time Windows
DOI:
https://doi.org/10.5890/JVTSD.2024.12.005Abstract
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.References
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