title: Multivariate multiscale entropy for brain consciousness analysis.
abstract: The recently introduced multiscale entropy (MSE) method accounts for long range correlations over multiple time scales and can therefore reveal the complexity of biological signals. The existing MSE algorithm deals with scalar time series whereas multivariate time series are common in experimental and biological systems. To that cause, in this paper the MSE method is extended to the multivariate case. This allows us to gain a greater insight into the complexity of the underlying signal generating system, producing multifaceted and more robust estimates than standard single channel MSE. Simulations on both synthetic data and brain consciousness analysis support the approach.
authors.affiliation: Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK. mosabber.ahmed@imperial.ac.uk
authors.forename: Mosabber Uddin
authors.initials: MU
authors.lastname: Ahmed
authors.forename: Ling
authors.initials: L
authors.lastname: Li
authors.forename: Jianting
authors.initials: J
authors.lastname: Cao
authors.forename: Danilo P
authors.initials: DP
authors.lastname: Mandic
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D003243:Consciousness / Q000502:physiology*; D056805:Consciousness Monitors; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D015999:Multivariate Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity