Epilepsy electroencephalogram signal analysis based on improved k-nearest neighbor network

被引：0
|
作者：
Yu X. [1 ]
Liu C. [1 ]
Dai J. [2 ]
Li J. [3 ]
Wang J. [1 ]
Hou F. [4 ]
机构：
[1] Image Processing and Image Communications Key Lab, Nanjing University of Posts and Telecommunications, Nanjing
[2] Nanjing General Hospital of Nanjing Military Command, Nanjing
[3] College of Physics and Information Technology, Shaanxi Normal University, Xi'an
[4] School of Science, China Pharmaceutical University, Nanjing
来源：
Shengwu Yixue Gongchengxue Zazhi/Journal of Biomedical Engineering | 2016年 / 33卷 / 06期
关键词：
Analysis of electroencephalogram signals; Epilepsy; Improved k-nearest neighbor network; Time series;
D O I：
10.7507/1001-5515.20160167
中图分类号：
学科分类号：
摘要：
The study of complex networks has become a hot research area of electroencephalogram signal. Electroencephalogram time series generated by the network keeps node information of network, so studying the time series from the network can also achieve the purpose of study epileptic electroencephalogram. In this paper, we propose a method to analyze epileptic electroencephalogram based on time series which is based on improved k-nearest neighbor network. The results of the experiment showed that studying power spectrum of time series from network was easier than power spectrum of time series directly generated from the original brain data to distinguish between normal controls and epileptic patients. In addition, studying the clustering coefficient of improved k-nearest neighbor network was able to distinguish between normal persons and patients with epilepsy. This study can provide important reference for the study of epilepsy and clinical diagnosis. � 2016, Editorial Office of Journal of Biomedical Engineering. All right reserved.
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页码：1039 / 1045
页数：6
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