2 research outputs found
HRV analysis: Unpredictability of approximate entropy in chronic obstructive pulmonary disease
Introduction: Approximate entropy (ApEn) is a widely imposed metric to evaluate a chaotic
response and irregularities of RR-intervals from an electrocardiogram. Yet, the technique is
problematic due to the accurate choice of the tolerance (r) and embedding dimension (M). We
prescribed the metric to evaluate these responses in subjects exhibiting symptoms of chronic
obstructive pulmonary disease (COPD) and we strived to overcome this disadvantage by
applying different groupings to detect the optimal.
Methods: We examined 38 subjects split equally: COPD and control. To evaluate autonomic
modulation the heart rate was measured beat-by-beat for 30 min in a supine position without
any physical, sensory, or pharmacological stimuli. In the time-series obtained the ApEn was
then applied with set values for tolerance, r and embedding dimension, M. Then, 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 any effect size statistical
combinations undertaken was -1.13 for Cohen’s ds, and -1.10 for Hedges’s gs with embedding
dimension, M = 2 and tolerance, r = 0.1.
Conclusion: ApEn was capable of optimally identifying the decrease in chaotic response in
COPD. The optimal combination of r and M for this were 0.1 and 2, respectively. Despite this,
ApEn is a relatively unpredictable mathematical marker and the use of other techniques to
evaluate a healthy or pathological condition is encouraged