Global chaotic parameters of heart rate variability during mental task

Fontes, Alexandra; Garner, David M.; Abreu, Luíz Carlos de; Barbosa, Juliana Cortez; Assis, Elisângela Vilar de; Souza, Ana Cecília Amorim de; Porto, Andrey Alves; Valenti, Vítor Engrácia

doi:10.1002/cplx.21656

Cited by 7 publications

(7 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A previous study reported that chaotic global analysis was unable to identify HRV changes during mental task [59]. Another research study investigated chaotic global analysis in RR intervals during exposure to heavy metal music [60].…”

Section: Discussionmentioning

confidence: 99%

Linear and Complex Measures of Heart Rate Variability during Exposure to Traffic Noise in Healthy Women

et al. 2018

Self Cite

View full text Add to dashboard Cite

Previous studies have described significant impact of different types of noise on the linear behavior of heart rate variability (HRV). However, there are few studies regarding the complexity of HRV during exposure to traffic noise. In this study, we evaluated the complexity of HRV during traffic noise exposure. We analyzed 31 healthy female students aged between 18 and 30 years. Volunteers remained at rest seated under spontaneous breathing during 10 minutes with an earphone turned off, and then they were exposed to traffic noise through an earphone for a period of 10 minutes. The traffic noise was recorded from a very busy city street and the sound was comprised of car, bus, and trucks engines and horn (71-104 dB). We observed no significant changes in the linear analysis of HRV. CFP3 (Cohen's = 1.28, large effect size) and CFP6 (Cohen's = 1.11, large effect size) parameters of chaotic global analysis and Shannon (Cohen's = 1.13, large effect size), Renyi (Cohen's = 1.06, large effect size), and Tsallis (Cohen's = 1.14, large effect size) entropies significantly increased ( < 0.005) during traffic noise exposure. In conclusion, traffic noise under laboratory conditions increased the complexity of HRV through chaotic global analysis and some measures of entropy in healthy females.

show abstract

Section: Discussionmentioning

confidence: 99%

Linear and Complex Measures of Heart Rate Variability during Exposure to Traffic Noise in Healthy Women

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…Entropy-based techniques are routinely employed in analysis of medical data especially cardiovascular [17][18][19], respiratory [20,21] and neurological signals [22,23]. In this case, the cardiovascular system that composes the dynamical systems was scrutinized in obese and non-obese children.…”

Section: Entropic Categoriesmentioning

confidence: 99%

Entropic analysis of HRV in obese children

Vanderlei

Abreu

et al. 2015

Int Arch Med

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Entropy-based techniques are routinely employed in analysis of medical data especially cardiovascular [29,30], respiratory [31,32] and neurological signals [33,34]. A low entropy dataset is highly predictable -whereas a high entropy dataset is less predictable.…”

Section: Methodsmentioning

confidence: 99%