Influence of embedding dimension on distribution entropy in analyzing heart rate variability

Distribution entropy (DistEn) is a recent measure of complexity that is used to analyze Heart Rate Variability (HRV) data. DistEn which is a function of data length N, number of bins M and embedding dimension m is known to be stable and consistent with respect to parameters N and M respectively. Also, (N, M) are known to have a combined effect in deciding performance of DistEn as a classification feature. But, all such analysis have mostly ignored the influence of the third parameter m on DistEn properties. Though a random fixed choice of m value has so far succeeded in portraying the effect of other parameters on DistEn, it is considered equally important to reveal the influence of a varying m on DistEn and its characteristics. This study examines the impact of m on the stability, consistency and performance of DistEn when the latter is used to analyze HRV data belonging to (i) healthy subjects discerned by age and (ii) subjects discerned by their heart's physiologic condition. Here, data length N of each signal is varied from 50 to 1000, while the number of bins M used varies from 100 to 2000. Information pertaining to m variations is obtained by carrying out experiments at four different values of embedding dimension; m = 2, 3,4 and 5. The study shows that the stability, consistency and classification performance of DistEn is not much influenced by changes in m.