Synchronization provides an insight into mechanisms underlying the interaction among bivariate physiological signals where their coupling is not known a priori. Cross-sample entropy (C-SampEn) has been used to quantify their synchronization. However, traditional C-SampEn has a poor statistical stability because a rigid decision rule is applied to define the similarity between two vectors. In this study, a fuzzy membership function was implemented to redefine the decision rule in C-SampEn with its performance evaluated using simulated and real cardiovascular coupling signals (RR interval and pulse transit time sequences from 10 normal subjects and 10 heart failure patients). Simulation results verified the decrease of both C-SampEn with increasing coupling degree. The analysis of cardiovascular coupling signals demonstrated a significant difference between normal and heart failure patients (normal 1.17 ± 0.09 vs. heart failure 1.02 ± 0.10, P<;0.01) with the improved C-SampEn, but not the traditional C-SampEn. Our improved C-SampEn provides a better understanding of the different cardiovascular coupling between normal subjects and heart failure patients.
|Title of host publication||Computing in Cardiology|
|Number of pages||4|
|Publication status||Published - 2013|
|Event||Computing in Cardiology 2013 Conference - Zaragoza, Spain|
Duration: 22 Sep 2013 → 25 Sep 2013
|Conference||Computing in Cardiology 2013 Conference|
|Period||22/09/13 → 25/09/13|