Dynamic multivariate multiscale entropy based analysis on brain death diagnosis

作     者：NI Li CAO JianTing WANG RuBin 

作者机构：Institute for Cognitive Neurodynamics East China University of Science and Technology Department Electronic Engineering Saitama Institute of Technology Brain Science Institute RIKEN 2-1 HirosawaSaitama 351-0198 Japan 

出 版 物：《Science China(Technological Sciences)》 (中国科学（技术科学英文版）)

年 卷 期：2015年第58卷第3期

页      面：425-433页

核心收录：

学科分类：1002[医学-临床医学] 100204[医学-神经病学] 10[医学] 

基　　金：supported by KAKENHI(Grant Nos.21360179,22560425)(JAPAN) supported by the Key Project of National Science Foundation of China(Grant Nos.11232005) The Ministry of Education Doctoral Foundation(Grant Nos.20120074110020) 

主　　题：EEG signals approximate entropy sample entropy brain death diagnosis 

摘      要：The recently introduced multivariate multiscale sample entropy(MMSE)well evaluates the long correlations in multiple channels,so that it can reveal the complexity of multivariate biological *** existing MMSE algorithm deals with short time series statically whereas long time series are common for real-time computation in practical *** a solution,we novelly proposed our dynamic MMSE(DMMSE)as an extension of *** helps us gain greater insight into the complexity of each section of time series,producing multifaceted and more robust estimates than the standard *** simulation results illustrated the feasibility and well performance in the brain death diagnosis.