title: On the application of the auto mutual information rate of decrease to biomedical signals.
abstract: The auto mutual information function (AMIF) evaluates the signal predictability by assessing linear and non-linear dependencies between two measurements taken from a single time series. Furthermore, the AMIF rate of decrease (AMIFRD) is correlated with signal entropy. This metric has been used to analyze biomedical data, including cardiac and brain activity recordings. Hence, the AMIFRD can be a relevant parameter in the context of biomedical signal analysis. Thus, in this pilot study, we have analyzed a synthetic sequence (a Lorenz system) and real biosignals (electroencephalograms recorded with eyes open and closed) with the AMIFRD. We aimed at illustrating the application of this parameter to biomedical time series. Our results show that the AMIFRD can detect changes in the non-linear dynamics of a sequence and that it can distinguish different physiological conditions.
authors.affiliation: University of Valladolid, Camino del Cementerio s/n, 47011, Spain. javier.escudero@ieee.org
authors.forename: Javier
authors.initials: J
authors.lastname: Escudero
authors.forename: Roberto
authors.initials: R
authors.lastname: Hornero
authors.forename: Daniel
authors.initials: D
authors.lastname: Abasolo
authors.forename: Miguel
authors.initials: M
authors.lastname: Lopez
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods; D019277:Entropy; D006801:Humans; D017711:Nonlinear Dynamics; D016011:Normal Distribution; D010865:Pilot Projects; D012815:Signal Processing, Computer-Assisted