Entropy Measures in the Assessment of Heart Rate Variability in Patients with Cardiodepressive Vasovagal Syncope (original) (raw)
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Entropy Measures in Analysis of Head up Tilt Test Outcome for Diagnosing Vasovagal Syncope
Entropy
The paper presents possible applications of entropy measures in analysis of biosignals recorded during head up tilt testing (HUTT) in patients with suspected vasovagal syndrome. The study group comprised 80 patients who developed syncope during HUTT (57 in the passive phase of the test (HUTT(+) group) and 23 who had negative result of passive phase and developed syncope after provocation with nitroglycerine (HUTT(−) group)). The paper focuses on assessment of monitored signals’ complexity (heart rate expressed as R-R intervals (RRI), blood pressure (sBP, dBP) and stroke volume (SV)) using various types of entropy measures (Sample Entropy (SE), Fuzzy Entropy (FE), Shannon Entropy (Sh), Conditional Entropy (CE), Permutation Entropy (PE)). Assessment of the complexity of signals in supine position indicated presence of significant differences between HUTT(+) versus HUTT(−) patients only for Conditional Entropy (CE(RRI)). Values of CE(RRI) higher than 0.7 indicate likelihood of a positi...
Progressive decrease of heart period variability entropy-based complexity during graded head-up tilt
Journal of Applied Physiology, 2007
Complexity (or its opposite, regularity) of heart period variability has been related to age and disease but never linked to a progressive shift of the sympathovagal balance. We compare several well established estimates of complexity of heart period variability based on entropy rates [i.e., approximate entropy (ApEn), sample entropy (SampEn), and correct conditional entropy (CCE)] during an experimental protocol known to produce a gradual shift of the sympathovagal balance toward sympathetic activation and vagal withdrawal (i.e., the graded head-up tilt test). Complexity analysis was carried out in 17 healthy subjects over short heart period variability series (ϳ250 cardiac beats) derived from ECG recordings during head-up tilt with table inclination randomly chosen inside the set {0, 15, 30, 45, 60, 75, 90}. We found that 1) ApEn does not change significantly during the protocol; 2) all indices measuring complexity based on entropy rates, including ad hoc corrections of the bias arising from their evaluation over short data sequences (i.e., corrected ApEn, SampEn, CCE), evidence a progressive decrease of complexity as a function of the tilt table inclination, thus indicating that complexity is under control of the autonomic nervous system; 3) corrected ApEn, SampEn, and CCE provide global indices that can be helpful to monitor sympathovagal balance. heart rate variability; autonomic nervous system; head-up tilt complexity Address for reprint requests and other correspondence: A.
Entropy in Investigation of Vasovagal Syndrome in Passive Head Up Tilt Test
This paper presents an application of Approximate Entropy (ApEn) and Sample Entropy (SampEn) in the analysis of heart rhythm, blood pressure and stroke volume for the diagnosis of vasovagal syndrome. The analyzed biosignals were recorded during positive passive tilt tests—HUTT(+). Signal changes and their entropy were compared in three main phases of the test: supine position, tilt, and pre-syncope, with special focus on the latter, which was analyzed in a sliding window of each signal. In some cases, ApEn and SampEn were equally useful for the assessment of signal complexity (p < 0.05 in corresponding calculations). The complexity of the signals was found to decrease in the pre-syncope phase (SampEn (RRI): 1.20–0.34, SampEn (sBP): 1.29–0.57, SampEn (dBP): 1.19–0.48, SampEn (SV): 1.62–0.91). The pattern of the SampEn (SV) decrease differs from the pattern of the SampEn (sBP), SampEn (dBP) and SampEn (RRI) decrease. For all signals, the lowest entropy values in the pre-syncope phase were observed at the moment when loss of consciousness occurred.
Entropy Measures in Heart Rate Variability Data
Lecture Notes in Computer Science, 2000
Standard parameters of heart rate variability are restricted in measuring linear effects, whereas nonlinear descriptions often suffer from the curse of dimensionality. An approach which might be capable of assessing complex properties is the calculation of entropy measures from normalised periodograms. Two concepts, both based on autoregressive spectral estimations are introduced here. To test the hypothesis that these entropy measures may improve the result of high risk stratification, they were applied to a clinical pilot study and to the data of patients with different cardiac diseases. The study shows that the entropy measures discussed here are useful tools to estimate the individual risk of patients suffering from heart failure. Further, the results demonstrate that the combination of different heart rate variability parameters leads to a better classification of cardiac diseases than single parameters.
Approximate Entropy (ApEn) based Heart Rate Variability Analysis
Indian Journal of Science and Technology, 2016
Heart Rate Variability (HRV) has been established as a vital index for diagnostics and prognosis of a number of pathological conditions. Moreover, HRV is a proven indicator of autonomic balance. As HRV is a result of multiple responses acting at various time scales, these interactions need to be quantified. In this paper, a complexity measure called ApEn is utilized to quantify the complexity of HRV. This method is tested on age stratified standard Fantasia database from Physionet. It is observed that young subjects show higher HRV complexity than the older ones. The effect of tolerance threshold 'r' is also evaluated on the HRV complexity estimation of young and old subjects. Further, for r≥0.10, the complexity of HRV is higher for young subjects but the trend is reverse for r<0.10. Therefore, it is concluded that the tolerance threshold 'r' should be carefully selected for the complexity analysis of HRV.
Pacing and Clinical Electrophysiology, 1997
Spectral analysis of heart rate variabHity (HRV) was used to assess changes in autonomic function before and during postural tilt in 28 syncopal patients: 14 (group A) with positive and 14 (group B) v^ith negative tilting test, and 14 normal controls (group C). Frequency-domain measurements of the high (HF) and low (LFj frequency bands and the ratio LF/HF were derived from Holter recordings, computed by Fast Fourier analysis for 4-minute intervals immediately before tilt testing, immediately after tilting, and just before the end of the test. In group A, the mean values of LF and HF decreased slightly in response to tilting while the LF/HF ratio increased, though these changes were not statistically significant. All parameters showed a statistically significant increase just before the onset of syncope. In group B, there were no significant changes in the parameters measured throughout the test. In group C. tbere was an increase in the LF and LF/HF ratio and a decrease in the HF immediately after tilting. Tbere were no further significant cbanges in any of tbe parameters during tbe test. Syncopal patients have a different pattern of response to the orthostatic stimulus, in that tbey do not sbow the increase in sympathetic tone observed in normal individuals immediately after tilting. In the patients witb a positive tilt test, tbere is a sbift in tbe balance ofANS activity towards the sympatbetic system sbortly before tbe onset of syncope. (PACE 1997; 20[Pt. IJ:1332-1341 syncope, vasovagal syndrome, tilting test, heart rate variability ddress for reprints: Prof.
Ectopic beats in approximate entropy and sample entropy-based HRV assessment
International Journal of Systems Science, 2012
Approximate entropy (ApEn) and sample entropy (SampEn) are the promising techniques for extracting complex characteristics of cardiovascular variability. Ectopic beats, originating from other than the normal site, are the artefacts contributing a serious limitation to heart rate variability (HRV) analysis. The approaches like deletion and interpolation are currently in use to eliminate the bias produced by ectopic beats. In this study, normal R-R interval time series of 10 healthy and 10 acute myocardial infarction (AMI) patients were analysed by inserting artificial ectopic beats. Then the effects of ectopic beats editing by deletion, degree-zero and degree-one interpolation on ApEn and SampEn have been assessed. Ectopic beats addition (even 2%) led to reduced complexity, resulting in decreased ApEn and SampEn of both healthy and AMI patient data. This reduction has been found to be dependent on level of ectopic beats. Editing of ectopic beats by interpolation degree-one method is found to be superior to other methods.
Heart rate and blood pressure variability in subjects with vasovagal syncope
Clinical Science, 2004
Autonomic nervous system control in subjects with vasovagal syncope is controversial. In the present study, we used short-term spectral analysis to evaluate autonomic control in subjects with recurrent vasovagal syncope. We assessed the ability of spectral indices of HR (heart rate) variability to predict tilt-test responses. A series of 47 outpatients with recurrent vasovagal syncope and with positive responses to head-up tilt testing underwent a further study of RR variability during controlled breathing at rest and during tilt testing. During controlled breathing, RR interval variability of total power (TPRR; P<0.001), low-frequency power (LFRR; P<0.05), high-frequency power (HFRR; P<0.001) and HF expressed in normalized units (HFnuRR; P<0.001) were all higher, and LF expressed in normalized units (LFnuRR) and LF/HF ratio were lower in subjects with vasovagal syncope than in controls (P<0.001). To assess the ability of spectral components of RR variability to predi...
Chaos: An Interdisciplinary Journal of Nonlinear Science
Despite the widespread di usion of nonlinear methods for heart rate variability (HRV) analysis, the presence and the extent to which nonlinear dynamics contribute to short-term HRV are still controversial. This work aims at testing the hypothesis that di erent types of nonlinearity can be observed in HRV depending on the method adopted and on the physiopathological state. Two entropy-based measures of time series complexity (normalized complexity index, NCI) and regularity (information storage, IS), and a measure quantifying deviations from linear correlations in a time series (Gaussian linear contrast, GLC), are applied to short HRV recordings obtained in young (Y) and old (O) healthy subjects and in myocardial infarction (MI) patients monitored in the resting supine position and in the upright position reached through head-up tilt. The method of surrogate data is employed to detect the presence and quantify the contribution of nonlinear dynamics to HRV. We nd that the three measures di er both in their variations across groups and conditions and in the percentage and strength of nonlinear HRV dynamics. NCI and IS displayed opposite variations, suggesting more complex dynamics in O and MI compared to Y and less complex dynamics during tilt. The strength of nonlinear dynamics is reduced by tilt using all measures in Y, while only GLC detects a signi cant strengthening of such dynamics in MI. A large percentage of detected nonlinear dynamics is revealed only by the IS measure in the Y group at rest, with a decrease in O and MI and during T, while NCI and GLC detect lower percentages in all groups and conditions. While these results suggest that distinct dynamic structures may lie beneath short-term HRV in di erent physiological states and pathological conditions, the strong dependence on the measure adopted and on their implementation suggests that physiological interpretations should be provided with caution.
Physiological Measurement, 2011
Approximate entropy (ApEn) is widely accepted as a complexity measure of the heart rate variability (HRV) signal, but selecting the criteria for the threshold value r is controversial. This paper aims to verify whether Chon's method of forecasting the r max is an appropriate one for the HRV signal. The standard limb lead ECG signals of 120 subjects were recorded for 10 min in a supine position. The subjects were divided into two groups: the heart failure (22 females and 38 males, median age 62.4 ± 12.6) and healthy control group (33 females and 27 males, median age 51.5 ± 16.9). Three types of ApEn were calculated: the ApEn 0.2 using the recommended constant r = 0.2, the ApEn chon using Chon's method and the ApEn max using the true r max . A Wilcoxon rank sum test showed that the ApEn 0.2 (p = 0.267) and the ApEn max (p = 0.813) had no statistical differences between the two groups, while the ApEn chon (p = 0.040) had. We generated a synthetic database to study the effect of two influential factors (the signal length N and the ratio of short-and long-term variability sd 1 /sd 2 ) on the empirical formula in Chon's method (Chon et al 2009 IEEE Eng. Med. Biol. Mag. 28 18-23). The results showed that the empirical formula proposed by Chon et al is a good method for analyzing the random signal, but not an appropriate tool for analyzing nonlinear signals, such as the logistic or HRV signals.