Epilepsy prediction model based on 2D-CNN and Cox-Stuart early stopping mechanism
10.3969/j.issn.1005-202X.2025.01.012
- VernacularTitle:基于2D-CNN和Cox-Stuart早停机制的癫痫预测模型
- Author:
Xizhen ZHANG
1
;
Xiaoli ZHANG
;
Yang LÜ
;
Fuming CHEN
Author Information
1. 甘肃中医药大学医学信息工程学院,甘肃兰州730050;中国人民解放军联勤保障部队第940医院医疗保障中心,甘肃兰州730050
- Publication Type:Journal Article
- Keywords:
epilepsy;
prediction;
Cox-Stuart test;
two-dimensional convolutional neural network;
deep learning
- From:
Chinese Journal of Medical Physics
2025;42(1):82-94
- CountryChina
- Language:Chinese
-
Abstract:
An epilepsy prediction model based on two-dimensional convolutional neural network and Cox-Stuart test for non-independent patients is proposed to address the problem of how to effectively predict whether epilepsy patients are going to have an attack or not. After EEG data normalization and EEG signal noise removal using a notch filter and a high-pass filter,the filtered signals are inputted into the two-dimensional convolutional neural network model for feature extraction and classification,and Cox-Stuart test is used to determine whether an early stopping is needed or not,thereby reducing the computational and time complexities of the model. The model is tested under the conditions with pre-seizure periods of 10,30 and 60 min,respectively,and the results show that the model performs best when the pre-seizure period is 10 min. The model has an average accuracy,sensitivity and specificity of 97.70％,97.36％and 98.04％on the test set,demonstrating its superior performance.
