文献类型：期刊文献

中文题名：Dynamic multivariate multiscale entropy based analysis on brain death diagnosis

英文题名：Dynamic multivariate multiscale entropy based analysis on brain death diagnosis

作者：Ni Li[1];Cao JianTing[2,3];Wang RuBin[1]

机构：[1]E China Univ Sci & Technol, Inst Cognit Neurodynam, Shanghai 200237, Peoples R China;[2]Saitama Inst Technol, Dept Elect Engn, Saitama 3690293, Japan;[3]RIKEN, Brain Sci Inst, Wako, Saitama 3510198, Japan

年份：2015

卷号：58

期号：3

起止页码：425

中文期刊名：Science China(Technological Sciences)

外文期刊名：SCIENCE CHINA-TECHNOLOGICAL SCIENCES

收录：;EI(收录号：20150900566434);Scopus;WOS:【SCI-EXPANDED(收录号:WOS:000350336800004)】；CSCD:【CSCD2015_2016】；

基金：This work was supported by KAKENHI (Grant Nos. 21360179, 22560425) (JAPAN), also supported by the Key Project of National Science Foundation of China (Grant Nos. 11232005) and The Ministry of Education Doctoral Foundation (Grant Nos. 20120074110020).

语种：英文

中文关键词：EEG signals; approximate entropy; sample entropy; brain death diagnosis

外文关键词：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 signals.The existing MMSE algorithm deals with short time series statically whereas long time series are common for real-time computation in practical use.As a solution,we novelly proposed our dynamic MMSE(DMMSE)as an extension of MMSE.This helps us gain greater insight into the complexity of each section of time series,producing multifaceted and more robust estimates than the standard MMSE.The simulation results illustrated the feasibility and well performance in the brain death diagnosis.
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 signals. The existing MMSE algorithm deals with short time series statically whereas long time series are common for real-time computation in practical use. As a solution, we novelly proposed our dynamic MMSE (DMMSE) as an extension of MMSE. This helps us gain greater insight into the complexity of each section of time series, producing multifaceted and more robust estimates than the standard MMSE. The simulation results illustrated the feasibility and well performance in the brain death diagnosis.