北京生物医学工程2025，Vol.44Issue(1)：55-60,67,7.DOI:10.3969/j.issn.1002-3208.2025.01.008
基于k最近邻法的癫痫脑电信号研究
Study on epileptic EEG signals based onk-nearest neighbor algorithm
卢灿爱 1姚文坡 2乙万义 2白登选 1王琼 1戴加飞 3王俊2
作者信息
- 1. 南京邮电大学通信与信息工程学院(南京 210003)
- 2. 南京邮电大学地理与生物信息学院(南京 210023)
- 3. 南京大学医学院金陵医院神经内科(南京 210008)
- 折叠
摘要
Abstract
Objective The k-nearest neighbor algorithm is used to calculate the partial mutual information of symbol sequence to analyze the coupling relationship between epileptic EEG signals,to explore the difference in the coupling degree between epileptic EEG signals and healthy people's EEG signals,and to provide a reference method for the study of epileptic EEG signals.Methods The traditional method is to obtain partial mutual information by calculating the probability distribution density between variables.In this paper,the k-nearest neighbor method is used to calculate part of mutual information.The algorithm does not require much data,and the accuracy and efficiency of the algorithm are relatively high.First,the original epileptic EEG signal sequence is symbolized.The purpose of symbolization is to transform the sequence into a symbol sequence,which can effectively reduce the impact of noise.Then,the symbol sequence is coded.Finally,the k-nearest neighbor algorithm is used to calculate partial mutual information to obtain the coupling relationship of EEG signals.Results For traditional methods of obtaining partial mutual information,when the data length is greater than 4 000,the P-values obtained in the pillow regions O1 and O2 are less than 0.05.For the k-nearest neighbor method to obtain partial mutual information,when the data length is greater than 2 000,the P-values obtained in the pillow regions O1 and O2 are less than 0.05.Compared to traditional methods,the k-nearest neighbor method can distinguish between epileptic EEG signals and healthy EEG signals in experimental data using a shorter data length.At the same time,it is found that the coupling degree of EEG signals in healthy individuals is significantly higher than that in epilepsy patients.Conclusions The k-nearest neighbor method can effectively analyze epileptic EEG signals.The accuracy and efficiency of the algorithm are relatively high.关键词
癫痫脑电信号/序列符号化/互信息/k最近邻算法/耦合关系Key words
epileptic EEG/sequence symbolization/mutual information/k-nearest neighbor/coupling relationship分类
医药卫生引用本文复制引用
卢灿爱,姚文坡,乙万义,白登选,王琼,戴加飞,王俊..基于k最近邻法的癫痫脑电信号研究[J].北京生物医学工程,2025,44(1):55-60,67,7.
