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Analysis of short-term heart rate and diastolic period variability using a refined fuzzy entropy method.

Ji L, Li P, Li K, Wang X, Liu C - Biomed Eng Online (2015)

Bottom Line: Besides, rFuzzyEn could distinguish the simulation models with different amount of additive noise even when the percentage of additive noise reached 60%, but neither SampEn nor FuzzyEn showed comparable performance.Clinical HRV analysis did not indicate any significant differences between the patients with coronary artery disease and the healthy volunteers in all the three mentioned entropy measures (all p > 0.20).But clinical DPV analysis showed that the patient group had a significantly higher rFuzzyEn (p < 0.01) than the healthy group.

View Article: PubMed Central - PubMed

Affiliation: School of Control Science and Engineering, Shandong University, 17923 Jingshi Road, Jinan, 250061, People's Republic of China. bmelzji@gmail.com.

ABSTRACT

Background: Heart rate variability (HRV) has been widely used in the non-invasive evaluation of cardiovascular function. Recent studies have also attached great importance to the cardiac diastolic period variability (DPV) examination. Short-term variability measurement (e.g., 5 min) has drawn increasing attention in clinical practice, since it is able to provide almost immediate measurement results and enables the real-time monitoring of cardiovascular function. However, it is still a contemporary challenge to robustly estimate the HRV and DPV parameters based on short-term recordings.

Methods: In this study, a refined fuzzy entropy (rFuzzyEn) was developed by substituting a piecewise fuzzy membership function for the Gaussian function in conventional fuzzy entropy (FuzzyEn) measure. Its stability and robustness against additive noise compared with sample entropy (SampEn) and FuzzyEn, were examined by two well-accepted simulation models-the [Formula: see text] noise and the Logistic attractor. The rFuzzyEn was further applied to evaluate clinical short-term (5 min) HRV and DPV of the patients with coronary artery stenosis and healthy volunteers.

Results: Simulation results showed smaller fluctuations in the rFuzzyEn than in SampEn and FuzzyEn values when the data length was decreasing. Besides, rFuzzyEn could distinguish the simulation models with different amount of additive noise even when the percentage of additive noise reached 60%, but neither SampEn nor FuzzyEn showed comparable performance. Clinical HRV analysis did not indicate any significant differences between the patients with coronary artery disease and the healthy volunteers in all the three mentioned entropy measures (all p > 0.20). But clinical DPV analysis showed that the patient group had a significantly higher rFuzzyEn (p < 0.01) than the healthy group. However, no or less significant difference was observed between the two groups in either SampEn (p = 0.14) or FuzzyEn (p = 0.05).

Conclusions: Our proposed rFuzzyEn outperformed conventional SampEn and FuzzyEn in terms of both stability and robustness against additive noise, particularly when the data set was relatively short. Analysis of DPV using rFuzzyEn may provide more valuable information to assess the cardiovascular states than the other entropy measures and has a potential for clinical application.

No MeSH data available.


Related in: MedlinePlus

SampEn, FuzzyEn, and rFuzzyEn results of simulated  models with different data length. Gray bar indicates the lengths that cannot support good performance of SampEn and FuzzyEn, but can still for rFuzzyEn. The abscissa is in logarithmic scale for better visualization.
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Fig2: SampEn, FuzzyEn, and rFuzzyEn results of simulated models with different data length. Gray bar indicates the lengths that cannot support good performance of SampEn and FuzzyEn, but can still for rFuzzyEn. The abscissa is in logarithmic scale for better visualization.

Mentions: Figure 2 shows the SampEn, FuzzyEn, and rFuzzyEn as functions of data length N. In general, all three measures performed well when the length N > 1,000 points. For the and noise models, the standard deviations of SampEn and FuzzyEn were larger than those of rFuzzyEn. And for the noise, larger standard deviations were observed in N < 1,000 for SampEn, and in N < 500 for FuzzyEn, indicated by the gradually enlarged error bars in Figure 2. The SampEn of overlapped that of when N was no more than 500, and the FuzzyEn of overlapped that of when N was around 200. Invalid values were found in the SampEn of once the data length dropped to less than 200. By contrast, the rFuzzyEn showed excellent performance not only for large data set, but for short series, its performance was still acceptable (see the range marked by gray bar in Figure 2). Besides, the rFuzzyEn was still available to differentiate between the and models even if N reduced to as small as 100 data points.Figure 2


Analysis of short-term heart rate and diastolic period variability using a refined fuzzy entropy method.

Ji L, Li P, Li K, Wang X, Liu C - Biomed Eng Online (2015)

SampEn, FuzzyEn, and rFuzzyEn results of simulated  models with different data length. Gray bar indicates the lengths that cannot support good performance of SampEn and FuzzyEn, but can still for rFuzzyEn. The abscissa is in logarithmic scale for better visualization.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4487860&req=5

Fig2: SampEn, FuzzyEn, and rFuzzyEn results of simulated models with different data length. Gray bar indicates the lengths that cannot support good performance of SampEn and FuzzyEn, but can still for rFuzzyEn. The abscissa is in logarithmic scale for better visualization.
Mentions: Figure 2 shows the SampEn, FuzzyEn, and rFuzzyEn as functions of data length N. In general, all three measures performed well when the length N > 1,000 points. For the and noise models, the standard deviations of SampEn and FuzzyEn were larger than those of rFuzzyEn. And for the noise, larger standard deviations were observed in N < 1,000 for SampEn, and in N < 500 for FuzzyEn, indicated by the gradually enlarged error bars in Figure 2. The SampEn of overlapped that of when N was no more than 500, and the FuzzyEn of overlapped that of when N was around 200. Invalid values were found in the SampEn of once the data length dropped to less than 200. By contrast, the rFuzzyEn showed excellent performance not only for large data set, but for short series, its performance was still acceptable (see the range marked by gray bar in Figure 2). Besides, the rFuzzyEn was still available to differentiate between the and models even if N reduced to as small as 100 data points.Figure 2

Bottom Line: Besides, rFuzzyEn could distinguish the simulation models with different amount of additive noise even when the percentage of additive noise reached 60%, but neither SampEn nor FuzzyEn showed comparable performance.Clinical HRV analysis did not indicate any significant differences between the patients with coronary artery disease and the healthy volunteers in all the three mentioned entropy measures (all p > 0.20).But clinical DPV analysis showed that the patient group had a significantly higher rFuzzyEn (p < 0.01) than the healthy group.

View Article: PubMed Central - PubMed

Affiliation: School of Control Science and Engineering, Shandong University, 17923 Jingshi Road, Jinan, 250061, People's Republic of China. bmelzji@gmail.com.

ABSTRACT

Background: Heart rate variability (HRV) has been widely used in the non-invasive evaluation of cardiovascular function. Recent studies have also attached great importance to the cardiac diastolic period variability (DPV) examination. Short-term variability measurement (e.g., 5 min) has drawn increasing attention in clinical practice, since it is able to provide almost immediate measurement results and enables the real-time monitoring of cardiovascular function. However, it is still a contemporary challenge to robustly estimate the HRV and DPV parameters based on short-term recordings.

Methods: In this study, a refined fuzzy entropy (rFuzzyEn) was developed by substituting a piecewise fuzzy membership function for the Gaussian function in conventional fuzzy entropy (FuzzyEn) measure. Its stability and robustness against additive noise compared with sample entropy (SampEn) and FuzzyEn, were examined by two well-accepted simulation models-the [Formula: see text] noise and the Logistic attractor. The rFuzzyEn was further applied to evaluate clinical short-term (5 min) HRV and DPV of the patients with coronary artery stenosis and healthy volunteers.

Results: Simulation results showed smaller fluctuations in the rFuzzyEn than in SampEn and FuzzyEn values when the data length was decreasing. Besides, rFuzzyEn could distinguish the simulation models with different amount of additive noise even when the percentage of additive noise reached 60%, but neither SampEn nor FuzzyEn showed comparable performance. Clinical HRV analysis did not indicate any significant differences between the patients with coronary artery disease and the healthy volunteers in all the three mentioned entropy measures (all p > 0.20). But clinical DPV analysis showed that the patient group had a significantly higher rFuzzyEn (p < 0.01) than the healthy group. However, no or less significant difference was observed between the two groups in either SampEn (p = 0.14) or FuzzyEn (p = 0.05).

Conclusions: Our proposed rFuzzyEn outperformed conventional SampEn and FuzzyEn in terms of both stability and robustness against additive noise, particularly when the data set was relatively short. Analysis of DPV using rFuzzyEn may provide more valuable information to assess the cardiovascular states than the other entropy measures and has a potential for clinical application.

No MeSH data available.


Related in: MedlinePlus