文献类型：期刊文献

英文题名：A novel epileptic seizure prediction model based on Cox-Stuart and Optuna

作者：Zhang, Xizhen[1,2];Zhang, Xiaoli[1,2];Chen, Fuming[1]

第一作者：Zhang, Xizhen

通信作者：Chen, FM[1]

机构：[1]940th Hosp Joint Logist Support Force Chinese Peop, Med Secur Ctr, Lanzhou, Peoples R China;[2]Gansu Univ Tradit Chinese Med, Lanzhou, Peoples R China

第一机构：940th Hosp Joint Logist Support Force Chinese Peop, Med Secur Ctr, Lanzhou, Peoples R China

通信机构：[1]corresponding author), 940th Hosp Joint Logist Support Force Chinese Peop, Med Secur Ctr, Lanzhou, Peoples R China.

年份：2025

卷号：16

外文期刊名：FRONTIERS IN NEUROLOGY

收录：;Scopus(收录号：2-s2.0-105022142537);WOS:【SCI-EXPANDED(收录号:WOS:001615496600001)】；

基金：The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by the National Natural Science Foundation of China (61901515 and 82000926) and the Natural Science Foundation of Gansu Province (22JR5RA002).

语种：英文

外文关键词：epilepsy prediction; Cox-Stuart; Optuna; CNN; CNN-BiLSTM

摘要：Objectives In order to more accurately predict whether patients with intractable epilepsy are about to develop seizures, this paper proposes an epilepsy prediction model.Methods When the amount of targeted patient data is small, A Cox-Stuart and Convolutional Neural Network and Bi-directional Long Short-Term Memory (Cox-Stuart-CNN-BiLSTM) model based on multi-patient epilepsy prediction is proposed, which aims to capture common features of epileptic seizures by integrating EEG signal data from multiple patients to train the model. When there is enough data for targeted patient, an Optuna and Convolutional Neural Network and Bi-directional Long Short-Term Memory (Optuna-CNN-BiLSTM) model based on independent patient epilepsy prediction is proposed, which can train the model for EEG data of individual patients, aiming to better match physiological characteristics and seizure patterns of targeted patient.Results The accuracy of the test set for multi-patient is 0.9992, the sensitivity is 0.9996, and the specificity is 0.9988; the average accuracy of the test set for independent patient is 0.9996, the sensitivity is 0.9995, and the specificity is 1.0000.Conclusions It can be proved that the method proposed in this paper has good experimental results.