Publications

Abstract : Sample entropy (SampEn), a popularly used “regularity analysis” tool, has restrictions in handling shortterm segments (largely N ≤ 200) of heart rate variability (HRV) data. For such short signals, the SampEn estimate either remains undefined or fails to retrieve “accurate” regularity information. These limitations arise due to the extreme dependence of SampEn on its functional parameters, in particular the tolerance r. Evaluating SampEn at a single random choice of parameter r is a major cause of concern in being able to extract reliable and complete regularity information from a given signal. Here, we hypothesize that, finding a complete profile of SampEn (in contrast to a single estimate) corresponding to a data specific set of r values may facilitate enhanced information retrieval from short-term signals. We introduce a novel and computationally efficient concept of SampEn profiling in order to eliminate existing inaccuracies seen in the case of SampEn estimation. Using three different HRV datasets from the PhysioNet database-first, real and simulated, second, elderly and young, and third, healthy and arrhythmic; we demonstrate better definiteness and classification performance of SampEn profile based estimates (TotalSampEn and AvgSampEn) when compared to conventional SampEn and FuzzyEn estimates. Our novelty is to identify the importance of reliability in short-term signal regularity analysis, and our proposed approach aims to enhance both quality and quantity of information from any short-term signal.