[1]张喜珍,张晓莉,吕洋,等.基于2D-CNN和Cox-Stuart早停机制的癫痫预测模型[J].中国医学物理学杂志,2025,42(1):82-94.[doi:DOI:10.3969/j.issn.1005-202X.2025.01.012]
　ZHANG Xizhen,ZHANG Xiaoli,et al.Epilepsy prediction model based on 2D-CNN and Cox-Stuart early stopping mechanism[J].Chinese Journal of Medical Physics,2025,42(1):82-94.[doi:DOI:10.3969/j.issn.1005-202X.2025.01.012]
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基于2D-CNN和Cox-Stuart早停机制的癫痫预测模型()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
42
期数:
2025年第1期
页码:
82-94
栏目:
医学信号处理与医学仪器
出版日期:
2025-01-19

文章信息/Info

Title:
Epilepsy prediction model based on 2D-CNN and Cox-Stuart early stopping mechanism
文章编号:
1005-202X（2025）01-0082-13
作者:
张喜珍1; 2; 张晓莉1; 2; 吕洋3; 陈扶明2
1.甘肃中医药大学医学信息工程学院， 甘肃 兰州 730050； 2.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； 3.中国人民解放军联勤保障部队第940医院眼科， 甘肃 兰州 730050
Author(s):
ZHANG Xizhen1;  2;  ZHANG Xiaoli1;  2;  L?Yang3;  CHEN Fuming2
1. School of Medical Information Engineering, Gansu University of Chinese Medicine, Lanzhou 730050, China 2. Medical Security Center, The 940th Hospital of Joint Logistics Support Force of Chinese Peoples Liberation Army, Lanzhou 730050, China 3. Department of Ophthalmology, The 940th Hospital of Joint Logistics Support Force of Chinese Peoples Liberation Army, Lanzhou 730050, China
关键词:
癫痫; 预测; Cox-Stuart检验法; 2D-CNN; 深度学习
Keywords:
Keywords: epilepsy prediction Cox-Stuart test two-dimensional convolutional neural network deep learning
分类号:
R318；TP912.35
DOI:
DOI:10.3969/j.issn.1005-202X.2025.01.012
文献标志码:
A
摘要:
针对如何有效预测癫痫患者是否将要发病这一问题，提出一种基于非独立患者的2维卷积神经网络（2D-CNN）和Cox-Stuart检验法的癫痫预测模型方法。首先对脑电数据做归一化处理，使用陷波滤波器和高通滤波器滤除脑电信号的噪声；将滤波后的信号输入到2D-CNN模型中进行特征提取和分类，使用Cox-Stuart方法检测是否需要早停，从而降低模型的计算复杂度和时间复杂度。此外，分别在发作前期为10、30、60 min的情况下对模型进行测试，结果显示，发作前期为10 min时，模型的效果最优。在测试集上的准确率为97.70%，灵敏度为97.36%，特异性为98.04%，具有良好的性能。
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.

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备注/Memo

备注/Memo:
【收稿日期】2024-09-11 【基金项目】国家自然科学基金（61901515，82000926）；甘肃省自然科学基金（22JR5RA002） 【作者简介】张喜珍，研究方向：生物医学信号检测与处理，E-mail: 2252403239@qq.com 【通信作者】陈扶明，高级工程师，研究方向：生物医学信号检测与处理，E-mail: cfm5762@126.com
更新日期/Last Update: 2025-01-19