Open AccessUnreliability of Approximate Entropy to Locate Optimal Complexity in Diabetes Mellitus via Heart Rate Variability
Author(s) -
David M. Garner,
AUTHOR_ID
Publication year - 2020
Publication title -
series of endocrinology, diabetes and metabolism
Language(s) - English
Resource type - Journals
ISSN - 2768-413X
DOI - 10.54178/jsedmv2i2003
Subject(s) - approximate entropy , supine position , mathematics , heart rate variability , statistics , heart rate , chaotic , metric (unit) , cardiology , pattern recognition (psychology) , medicine , computer science , artificial intelligence , time series , blood pressure , operations management , economics
Approximate Entropy (ApEn) is a widely enforced metric to evaluate the chaotic response and irregularities of RR intervals from an electrocardiogram. We applied the metric to estimate these responses in subjects with type 1 diabetes mellitus (DM1). So far, as a technique it has one key problem – the accurate choices of the tolerance (r) and embedding dimension (M). So, we attempted to overcome this drawback by applying different groupings to detect the optimum. Methods: We studied 46 subjects split into two equal groups: DM1 and control. To evaluate autonomic modulation the heart rate was measured for 30 min in a supine position without any physical, sensory, or pharmacological stimuli. For the time-series, the ApEn was applied with set values for r (0.1→0.5 in intervals of 0.1) and M (1→5 in intervals of 1) and the differences between the two groups and their effect size by two measures (Cohen’s ds and Hedges’s gs) were computed. Results: The highest value of statistical significance accomplished for the effect sizes (ES) for any of the combinations performed was -0.7137 for Cohen’s ds and -0.7015 for Hedges’s gs with M = 2 and r = 0.08. Conclusion: ApEn was able to identify the reduction in chaotic response in DM1 subjects. Still, ApEn is relatively unreliable as a mathematical marker to determine this.