Paper Info
Title
Time-Dependent multivariate multiscale entropy based analysis on brain consciousness diagnosis
Abstract
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 time-dependent MMSE as an extension of MMSE. This helps us gain greater insight into the complexity of each section of time series, respectively, producing multifaceted and more robust estimates than the standard MMSE. The simulation results illustrated the effectiveness and well performance in the brain death diagnosis.
Year
DOI
Venue
2013
10.1007/978-3-642-38786-9_9
BICS
Keywords
Field
DocType
brain consciousness diagnosis,brain death diagnosis,multivariate multiscale sample entropy,time series,long time series,short time series statically,multivariate biological signal,time-dependent mmse,standard mmse,long correlation,existing mmse algorithm deal,time-dependent multivariate multiscale entropy
Multiscale entropy,Approximate entropy,Sample entropy,Multivariate statistics,Computer science,Algorithm,Consciousness,Artificial intelligence,Death diagnosis,Computation
Conference
Citations 
PageRank 
References 
0
0.34
3
Authors
3
Name
Order
Citations
PageRank
Li Ni100.34
Jianting Cao219434.47
Rubin Wang314125.54