A mixed CNN based on attention mechanism to predict seizures

Nan Qi; Yan Piao; Baolin Tan

doi:10.1117/12.2678864

25 May 2023 A mixed CNN based on attention mechanism to predict seizures

Nan Qi, Yan Piao, Baolin Tan

Proceedings Volume 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023); 127120P (2023) https://doi.org/10.1117/12.2678864
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 2023, Huzhou, China

Abstract

Epilepsy is a chronic disease caused by sudden abnormal discharges of neurons in the brain, which leads to transient brain dysfunction. Tens of millions of people in the world suffer from epilepsy. Predicting the coming seizures will help to save the lives of patients. In this paper, a mixed convolutional neural network (CNN) seizure prediction algorithm based on attention mechanism is proposed. We first processed the original EEG signals as 3D features, and then designed a mixed CNN to automatically extract spatial-temporal features and completed the classification task. In addition, we applied the attention mechanism to the proposed mixed CNN model, which can not only obtain the importance between different channels, but also further learn more abstract features. We used the CHB-MIT scalp EEG dataset to evaluate our seizure prediction algorithm. We also compared our seizure prediction algorithm with the previous deep learning algorithm, and the experimental results have showed that our proposed seizure prediction algorithm is effective.

Citation Download Citation

Nan Qi, Yan Piao, and Baolin Tan "A mixed CNN based on attention mechanism to predict seizures", Proc. SPIE 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 127120P (25 May 2023); https://doi.org/10.1117/12.2678864

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