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Distribution entropy ( DistEn ) is a recently developed measure of complexity that is used to analyse heart rate variability (HRV) data. Its calculation requires two input parameters—the embedding dimension  m , and the number of bins  M  which replaces the tolerance parameter  r  that is used by the existing approximation entropy ( ApEn ) and sample entropy ( SampEn ) measures. The performance of  DistEn  can also be affected by the data length  N . In our previous studies, we have analyzed stability and performance of  DistEn  with respect to one parameter ( m  or  M ) or combination of two parameters ( N  and  M ). However, impact of varying all the three input parameters on  DistEn  is not yet studied. Since DistEn is predominantly aimed at analysing short length heart rate variability (HRV) signal, it is important to comprehensively study the stability, consistency and performance of the measure using multiple case studies. In this study, we examined the impact of changing input parameters on  DistEn  for synthetic and physiological signals. We also compared the variations of  DistEn  and performance in distinguishing physiological (Elderly from Young) and pathological (Healthy from Arrhythmia) conditions with  ApEn  and  SampEn . The results showed that  DistEn  values are minimally affected by the variations of input parameters compared to  ApEn  and  SampEn. DistEn  also showed the most consistent and the best performance in differentiating physiological and pathological conditions with various of input parameters among reported complexity measures. In conclusion,  DistEn  is found to be the best measure for analysing short length HRV time series.

Stability, Consistency and Performance of Distribution Entropy in Analysing Short Length Heart Rate Variability (HRV) Signal