Inventi Impact: Biomedical Engineering

Inventi:ebe/17449/15

Analysis of Short-term Heart Rate and Diastolic Period Variability Using a Refined Fuzzy Entropy\nMethod

Background: Heart rate variability (HRV) has been widely used in the non-invasive\nevaluation of cardiovascular function. Recent studies have also attached great importance\nto the cardiac diastolic period variability (DPV) examination. Short-term variability\nmeasurement (e.g., 5 min) has drawn increasing attention in clinical practice, since\nit is able to provide almost immediate measurement results and enables the real-time\nmonitoring of cardiovascular function. However, it is still a contemporary challenge to\nrobustly estimate the HRV and DPV parameters based on short-term recordings.\nMethods: In this study, a refined fuzzy entropy (rFuzzyEn) was developed by substituting\na piecewise fuzzy membership function for the Gaussian function in conventional\nfuzzy entropy (FuzzyEn) measure. Its stability and robustness against additive noise\ncompared with sample entropy (SampEn) and FuzzyEn, were examined by two wellaccepted\nsimulation models�the 1/f ? noise and the Logistic attractor. The rFuzzyEn\nwas further applied to evaluate clinical short-term (5 min) HRV and DPV of the patients\nwith coronary artery stenosis and healthy volunteers.\nResults: Simulation results showed smaller fluctuations in the rFuzzyEn than in\nSampEn and FuzzyEn values when the data length was decreasing. Besides, rFuzzyEn\ncould distinguish the simulation models with different amount of additive noise even\nwhen the percentage of additive noise reached 60%, but neither SampEn nor FuzzyEn\nshowed comparable performance. Clinical HRV analysis did not indicate any significant\ndifferences between the patients with coronary artery disease and the healthy volunteers\nin all the three mentioned entropy measures (all p > 0.20). But clinical DPV analysis\nshowed that the patient group had a significantly higher rFuzzyEn (p < 0.01) than\nthe healthy group. However, no or less significant difference was observed between\nthe two groups in either SampEn (p = 0.14) or FuzzyEn (p = 0.05).\nConclusions: Our proposed r Fuzzy En outperformed conventional SampEn and\nFuzzyEn in terms of both stability and robustness against additive noise, particularly\nwhen the data set was relatively short. Analysis of DPV using rFuzzyEn may provide\nmore valuable information to assess the cardiovascular states than the other entropy\nmeasures and has a potential for clinical application.