On the use of permutation entropy in the classification of heart rate variability time series

TRAVERSARO, FRANCISCO; NICOLÁS QUIRÓS; MARCELO RISK; FRANCISCO REDELICO

Córdoba

Congreso; XXI Congreso Argentino de Bioingeniería; 2017

Sociedad Argentina de Bioingeniería

Bandt and Pompe introduced Permutation Entropy as a complexity measure and has been widelyused in this and in many nonlinear dynamics fields. In theory these time series come from a process that generates continuous values, and if equal values exists in a neighborhood, xt = xt; t 6= t, they can be neglected with no consequences because their probability of occurrence is insignificant. Since then, this measure has been modified and extended, in particular in cases when the amount of equal values in the series is large due to the observational method, and can not be neglected (i.e heart rate variability (HRV) time series). We present a new Bayesian Missing Data Imputation that cope with type of time series without modifying the essence of the Permutation Entropy and compare it with the Modified Permutation Entropy, a complexity measure that assumes that equal values are not from artifacts of observations but they are typical of the generator process. The Bayesian Method is proves to outperform the Modified Entropy.