Chaotic global parameters correlation with heart rate variability in obese children

Vanderlei, Franciele Marques; Vanderlei, Luiz Carlos Marques; Garner, David M.

doi:10.7322/jhgd.72041

Cited by 28 publications

(44 citation statements)

References 41 publications

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“…sMTM is the value of the area between the MTM spectrum and the baseline. There has been speculation 14,15 that if spectral entropy and sDFA previously applied to Welch power spectra were applied to multi-taper spectra which are adaptive and more sensitive; results may have greater chaotic parametric response.…”

Section: Introductionmentioning

confidence: 99%

Risk Appraisal by Novel Chaotic Globals to HRV in Subjects With Malnutrition

Barreto

Vanderlei

et al. 2014

J. Hum. Growth Dev.

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The aim of this study is to assess the risk of dynamical diseases in malnourished children. This is achieved by the application of novel chaotic global techniques to the RR-intervals of the electrocardiogram (ECG) in the cohort. Heart Rate Variability (HRV) is an inexpensive and non-invasive tool to measure the autonomic impulses. Here there has been a decrease in chaotic response of HRV. Seventy children were divided into equal groups and the HRV monitored for 20-25 minutes. The Chaos Forward Parameter (CFP) which applies all three chaotic global parameters is suggested to be the most robust algorithm. These three parameters are high spectral entropy (hsEntropy), high spectral detrended fluctuation analysis (hsDFA) and spectral multi-taper method (sMTM). hsEntropy is a function of the irregularity of amplitude and frequency of the power spectrums peaks. It is derived by applying Shannon entropy to the multi-taper method power spectrum. To derive hsDFA we calculate the spectral adaptation in exactly the same way as for hsEntropy using an adaptive multi-taper method power spectrum with the same settings; but DFA rather than Shannon entropy is the algorithm applied. sMTM is the area between the multi-taper method power spectrum and the baseline. After Anderson-Darling and Lilliefors tests of normality; Kruskal-Wallis was used for the statistical analysis, with the level of significance set at (p < 0.01). Principal Component Analysis (PCA) identified two components representing 100% of total variance. Autonomic imbalance measured as HRV and an increased cardiovascular risk are described for overweight children as well as for malnourished and those with anorexia nervosa. The relationship between malnourishment and complexity measures is useful in the risk assessment of dynamical diseases associated with the condition. This is supportive in treatments, assisting the determination of the level of dietary or pharmacological intervention especially in related dynamical diseases.

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Section: Introductionmentioning

confidence: 99%

Risk Appraisal by Novel Chaotic Globals to HRV in Subjects With Malnutrition

Barreto

Vanderlei

et al. 2014

J. Hum. Growth Dev.

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“…Obesity is a major public health problem [11]. Consequences include arterial hypertension, atherosclerosis, diabetes, sleep apnea and depression; amongst others.…”

Section: Introductionmentioning

confidence: 99%

Entropic analysis of HRV in obese children

Vanderlei

Abreu

et al. 2015

Int Arch Med

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The aim of the study was to analyze heart rate dynamics in obese children by functional entropic measures of Heart Rate Variability (HRV). HRV is a simple, reliable, cheap and non-invasive measure of autonomic impulses. We applied five technques based on entropy to assess the level of complexity. These were Shannon, Multiscale Tsallis and Multiscale Rényi entropies. Then, Approximate and Sample entropies. Ninety-four children of mixed gender aged eight to twelve years were divided into two equal groups (n=47) based on body mass index: obese and non-obese weight ranges. HRV was monitored in the dorsal decubitus position for 20 minutes. After Anderson-Darling and Ryan-Joiner tests of normality, the parametric test ANOVA1 was applied for the statistical analysis, with the level of significance set at (p<0.05); so the probability of a type I error was less than 5%. All types of functional entropies were significant at that level with the exception of Sample entropy. Furthermore, for all five measures the chaotic response increased when undergoing change from non-obese to obese. Regarding the application of Principal Component Analysis (PCA) the first two components represent 98.9% of total variance; a steep scree plot. The Multiscale Rényi (α=0.25), Shannon and Multiscale Tsallis (q=0.25) entropies performed simlarly regarding PCA and ANOVA1; whilst the Approximate and Sample entropies were also analogous with respect to these particular statisical tests. The Approximate entropy performed the most strongly with respect to p-value (p=0.0092) by ANOVA1 and PCA. With the exception of Sample entropy the entropic techniques described here were able to significantly quantify the increase in chaotic response when non-obese to obese children were assessed by the HRV.

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“…CFP1 is expected to be the case as it was so in the previous studies by Souza, et al [12] and Vanderlei, et al [13]. Nevertheless, CFP3 with the sDFA parameter absent is more significant.…”

Section: Discussionmentioning

confidence: 67%

“…It is suggested that the length of the time-series be doubled to 1000 RR intervals. This was the case for previous studies by Souza, et al [12] and Vanderlei, et al [13]. Only half this recommended length of data was practiced here.…”

Section: Discussionmentioning

confidence: 86%