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. 2017 Sep 20:8:720.
doi: 10.3389/fphys.2017.00720. eCollection 2017.

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

Chandan Karmakar  1 , Radhagayathri K Udhayakumar  2 , Peng Li  3 , Svetha Venkatesh  4 , Marimuthu Palaniswami  2
Affiliations

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

Chandan Karmakar et al. Front Physiol. .

Abstract

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.

Keywords: aging; approximate entropy; arrhythmia; distribution entropy; heart rate variability; sample entropy; short-term analysis.

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Figures

Figure 1
Figure 1
Variation of Approximate entropy (ApEn) for Synthetic signal (“Chaotic” and “Periodic”) varying parameters N and r for (A) m = 2, (B) m = 3, (C) m = 4, and (D) m = 5.
Figure 2
Figure 2
Variation of Sample entropy (SampEn) for Synthetic signal (“Chaotic” and “Periodic”) varying parameters N and r for (A) m = 2, (B) m = 3, (C) m = 4, and (D) m = 5.
Figure 3
Figure 3
Variation of Distribution entropy (DistEn) for Synthetic signal (“Chaotic” and “Periodic”) varying parameters N and M for (A) m = 2, (B) m = 3, (C) m = 4 and (D) m = 5.
Figure 4
Figure 4
Variation of Approximate entropy (ApEn) for physiological signal (Elderly and Young subjects) varying parameters N and r for (A) m = 2, (B) m = 3, (C) m = 4, and (D) m = 5.
Figure 5
Figure 5
Variation of Sample entropy (SampEn) for physiological signal (Elderly and Young subjects) varying parameters N and r for (A) m = 2, (B) m = 3, (C) m = 4, and (D) m = 5.
Figure 6
Figure 6
Variation of Distribution entropy (DistEn) for physiological signal (Elderly and Young subjects) varying parameters N and M for (A) m = 2, (B) m = 3, (C) m = 4 and (D) m = 5.
Figure 7
Figure 7
Variation of Approximate Entropy (ApEn) for physiological signal (Healthy and Arrhythmia) varying parameters N and r for (A) m = 2, (B) m = 3, (C) m = 4, and (D) m = 5.
Figure 8
Figure 8
Variation of Sample Entropy (SampEn) for physiological signal (Healthy and Arrhythmia) varying parameters N and r for (A) m = 2, (B) m = 3, (C) m = 4, and (D) m = 5.
Figure 9
Figure 9
Variation of Distribution Entropy (DistEn) for physiological signal (Healthy and Arrhythmia) varying parameters N and M for (A) m = 2, (B) m = 3, (C) m = 4, and (D) m = 5.
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