Entropy-based data mining on the example of cardiac arrhythmia suppression

Abstract

Heart rate variability (HRV) is the variation of the time interval between consecutive heartbeats and depends on the extrinsic regulation of the heart rate. It can be quantified using nonlinear methods such as entropy measures, which determine the irregularity of the time intervals. In this work, approximate entropy (ApEn), sample entropy (SampEn), fuzzy entropy (FuzzyEn) and fuzzy measure entropy (FuzzyMEn) were used to assess the effects of three different cardiac arrhythmia suppressing drugs on the HRV after a myocardial infarction. The results show that the ability of all four entropy measures to distinguish between pre- and post-treatment HRV data is highly significant (p < 0.01). Furthermore, approximate entropy and sample entropy are able to differentiate significantly (p < 0.05) between the tested arrhythmia suppressing agents. © 2014 Springer International Publishing.