Cross Entropy Profiling to Test Pattern Synchrony in Short-Term Signals.

Abstract

Examining nonlinear bi-variate time series for pattern synchrony has been largely carried out by the cross sample entropy measure, X-SampEn, which is highly bound by parametric restrictions. Threshold parameter r is the one that limits X-SampEn estimations most adversely. An inappropriate r choice leads to erroneous synchrony detection, even for the case of X-SampEn analysis on simple synthetically generated signals like the MIX(P) process. This gives us an intimation of how difficult it would be for such synchrony measures to handle the more complex physiologic data. The recently introduced concept of entropy profiling has been proved to release such measures from the clutches of r dependence. In this study, we demonstrate how entropy profiling with respect to r can be implemented on cross entropy analysis, particularly X-SampEn. We have used different sets of simple MIX(P) processes for the purpose and validated the impact of X-SampEn profiling over X-SampEn estimation, with a special focus on short-term data. From results, we see that X-SampEn profiling alone can accurately classify MIX(P) signals based on pattern synchrony. Here, X-SampEn estimation fails undoubtedly, even at the higher data lengths where traditional SampEn estimation is known to perform with good accuracy.