生物医学工程学杂志

生物医学工程学杂志

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基于通用赤池信息量准则改进维纳-格兰杰因果索引算法的颅内脑电效应连通性研究

杨淳沨 1,2,4 , , 向文涛 3,4 , 伍家松 1,2,4 , 孔佑勇 1,4 , 姜龙玉 1,4 , Le BouquinJèannes Régine 3,4 , 舒华忠 1,2,4

1. 东南大学 计算机科学与工程学院 计算机网络和信息集成教育部重点实验室（南京  210096） ;
2. 东南大学 教育部信息显示与可视化国际合作联合实验室（南京 210096） ;
3. 法国国家医学与健康研究院U1099，雷恩第一大学信号与图像处理实验室（法国雷恩 35000） ;
4. 中法生物医学信息研究中心（南京 210096，法国雷恩  35000）

收稿日期: 2017-09-15;  修回日期:2018-04-16; 

基金项目: 国家自然科学基金资助项目（31400842，61271312，61201344，61401085，31640028）；江苏省自然科学基金资助项目（BK20150650）；教育部留学回国人员科研启动基金资助项目；声场声信息国家重点实验室（中科院声学所）开放课题（SKLA201604）

通讯作者: 杨淳沨, Email: chunfeng.yang@seu.edu.cn

Research on effective connectivity of intracerebral electroencephalogram based on Wiener-Granger Causality Index modified by generalized Akaike’s Information Criterion

YANG Chunfeng 1,2,4 , , XIANG Wentao 3,4 , WU Jiasong 1,2,4 , KONG Youyong 1,4 , JIANG Longyu 1,4 , LE BOUQUIN JÈANNES Régine 3,4 , SHU Huazhong 1,2,4

1. Key Laboratory of Computer Network and Information Integration of Ministry of Education, School of Computer Science and Engineering, Southeast University, Nanjing 210096, P.R.China ;
2. International Joint Research Laboratory of Information Display and Visualization, Southeast University, Nanjing 210096, P.R.China ;
3. INSERM U1099, LTSI, Université de Rennes 1, Rennes 35000, France ;
4. Centre de Recherche en Information Biomédicale Sino-français, Nanjing 210096 & Rennes 35000, P.R.China & France

Corresponding Author: YANG Chunfeng, Email: chunfeng.yang@seu.edu.cn

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本文的目标是处理并分析使用深度电极在难治性癫痫患者癫痫发作期间其大脑皮层中记录到的癫痫脑电信号间的大脑效应连通性。维纳-格兰杰因果索引算法是一种众所周知的检测脑电信号间大脑效应连通性的有效方法。它是一种基于线性自回归模型的方法，而模型参数估计问题在其用于脑电因果效应连通性研究中的计算准确性与鲁棒性方面起着至关重要的作用。本文针对这一问题，使用了我们提出的改进的赤池信息量准则来估计算法中自回归模型的模型阶数，以提高维纳-格兰杰因果索引算法检测大脑效应连通性的性能。实验仿真结果表明：不管是在线性随机系统中还是在能生成模拟癫痫信号的生理模型中，该改进的维纳-格兰杰因果索引算法在检测脑效应连通性上都表现出良好的鲁棒性。

The objective is to deal with brain effective connectivity among epilepsy electroencephalogram (EEG) signals recorded by use of depth electrodes in the cerebral cortex of patients suffering from refractory epilepsy during their epileptic seizures. The Wiener-Granger Causality Index (WGCI) is a well-known effective measure that can be useful to detect causal relations of interdependence in these kinds of EEG signals. It is based on the linear autoregressive model, and the issue of the estimation of the model parameters plays an important role in the calculation accuracy and robustness of WGCI to do research on brain effective connectivity. Focusing on this issue, a modified Akaike’s information criterion algorithm is introduced in the computation of the WGCI to estimate the orders involved in the underlying models and in order to advance the performance of WGCI to detect brain effective connectivity. Experimental results support the interesting performance of the proposed algorithm to characterize the information flow both in a linear stochastic system and a physiology-based model.

关键词: 因果索引; 赤池信息量准则; 基于生理学的模型; 癫痫; 大脑连通性

Key words: causality index; Akaike's information criterion; physiology-based model; epilepsy; brain connectivity

引用本文： 杨淳沨, 向文涛, 伍家松, 孔佑勇, 姜龙玉, Le BouquinJèannes Régine, 舒华忠. 基于通用赤池信息量准则改进维纳-格兰杰因果索引算法的颅内脑电效应连通性研究. 生物医学工程学杂志, 2018, 35(5): 665-671. doi: 10.7507/1001-5515.201709032 复制

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