生命科学仪器2024，Vol.22Issue(1)：10-13,4.DOI:10.11967/2024220203
脑电图信号多维度特性分析在癫痫病发作预测中的应用
Application ofMultidimensional Characteristic Analysis of Electroencephalogram Signal in Epileptic Seizure Prediction
摘要
Abstract
The nonlinear EEG signals of epilepsy patients face challenges such as difficulty in classifying and recog-nizing patterns.In view of this,this study constructs a joint EEG signal classification model based on convolutional neural networks combined with various intelligent optimization algorithms,and verifies its convergence and classifi-cation performance through experiments.The model can accurately test the corresponding changes in EEG signals under different frequencies of brain stimulation.And the convergence efficiency of the joint algorithm was tested by selecting a dataset.The convergence speed of the joint algorithm from the 10th iteration was significantly better than the other algorithms,and it still had a significant advantage in the 200th generation.The classification efficien-cy of the joint algorithm is about 10％higher than that of traditional extreme learning machines.Overall,this mod-el has played a role in collecting,analyzing,and classifying the EEG signals of epilepsy patients in practical diagnos-tic scenarios,and has certain practicality and reference value for the diagnosis and prediction of seizures.关键词
癫痫/脑电信号/卷积神经网络/智能寻优算法/分类模型Key words
Epilepsy/Eeg signal/Convolutional neural network/Intelligent optimization algorithm/Classification model分类
信息技术与安全科学引用本文复制引用
努尔比亚·阿不拉江,阿地力江·阿布力米提,祖木来提·司马义,阿不都米吉提·阿吉,阿依夏·米吉提,古丽乃则尔·麦麦提..脑电图信号多维度特性分析在癫痫病发作预测中的应用[J].生命科学仪器,2024,22(1):10-13,4.基金项目
Q2SD课题:新疆喀什地区癫痫病流行学调查及危险因素分析,编号KS2021067 （）
