title: Mixed predictability and cross-validation to assess non-linear Granger causality in short cardiovascular variability series.
abstract: A method to evaluate the direction and strength of causal interactions in bivariate cardiovascular and cardiorespiratory series is presented. The method is based on quantifying self and mixed predictability of the two series using nearest-neighbour local linear approximation. It returns two causal coupling indexes measuring the relative improvement in predictability along direct and reverse directions, and a directionality index indicating the preferential direction of interaction. The method was implemented through a cross-validation approach that allowed quantification of directionality without constraining the embedding of the series, and fully exploited the available data to maximise the prediction accuracy. Validation on short simulated bivariate time series demonstrated the ability of the method to capture different degrees of unidirectional and bidirectional interaction. Moreover, application to representative examples of heart rate, systolic arterial pressure and respiration series allowed the inference of causal relationships related to known physiological mechanisms and experimental conditions.
authors.affiliation: Department of Physics, University of Trento, Trento, Italy. faes@science.unitn.it
authors.forename: Luca
authors.initials: L
authors.lastname: Faes
authors.forename: Roberta
authors.initials: R
authors.lastname: Cucino
authors.forename: Giandomenico
authors.initials: G
authors.lastname: Nollo
mesh_terms: D000328:Adult; D000465:Algorithms; D001794:Blood Pressure / Q000502:physiology*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005260:Female; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D008955:Models, Cardiovascular; D017711:Nonlinear Dynamics; D012044:Regression Analysis; D015656:Respiratory Mechanics / Q000502:physiology*; D013223:Statistics as Topic