Complex measurements of heart rate variability in obese youths: distinguishing autonomic dysfunction

Introduction: Heart rate variability (HRV) can be assessed from RR-intervals. These are derived from an electrocardiographic PQRST-signature and can deviate in a chaotic or irregular manner. In the past, techniques from statistical physics have allowed researchers to study such systems. Objective: This study planned to assess the heart rate dynamics in young obese subjects by nonlinear metrics to heart rate variability. Methods: 86 subjects were split equally according to status. Heart rate was recorded with the subjects resting in a dorsal (prone) position for 30 minutes. The complexity of the RR-intervals was assessed by five Entropies, Detrended Fluctuation Analysis, Higuchi and Katz’s fractal dimensions Following inconclusive tests of normality we calculated the One-Way Analysis of Variance, Kruskal-Wallis, and the Effect Sizes by Cohen’s d significances. Results: It was established that Shannon, Renyi and Tsallis Entropies and the Higuchi and Katz・s fractal dimensions could significantly discriminate the two groups. The three entropies were higher in obese youths, suggesting less predictable sets of RR intervals (p<0.0001; d≈1.0). Whilst the Higuchi (p<0.003; d≈0.76) and Katz・s (p≈0.02; d≈0.57) fractal dimensions were lower in obese youths. Conclusion: As with chaotic globals an increase in response was detected by three measures of entropy in young obese. This is counter to the decreasing response detected by fractal dimensions. Chaotic globals and entropies are more dependable than fractal dimensions when assessing the responses to obesity.

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Detrended Fluctuation Analysis of Heart Rate Variability in Noise Exposure

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1044-1045.1129 ◽

2014 ◽

Vol 1044-1045 ◽

pp. 1129-1134 ◽

Cited By ~ 1

Author(s):

Shih Tsung Chen ◽

Li Ho Tseng ◽

Yuan Po Lee ◽

Hong Zhun Wu ◽

Chia Yi Chou

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Detrended Fluctuation Analysis ◽

Low Frequency ◽

Fluctuation Analysis ◽

Frequency Noise ◽

Cardiovascular Activity ◽

Low Frequency Noise ◽

Rr Intervals ◽

Detrended Fluctuation

During the past two decades, most studies have employed questionnaires to characterize the effects of noise on behavior and health. Developments in physiological techniques have provided a noninvasive method for recording cardiovascular autonomic activity by using an electrocardiogram (ECG). We investigated cardiovascular activity changes in exposure to exposure to low-frequency noise for various noise intensities by using detrended fluctuation analysis (DFA) of heart rate variability (HRV). We hypothesized that distinct noise intensities would affect cardiovascular activity, which would be reflected in the HRV and DFA parameters. A total of 17 healthy volunteers participated in this study. The test intensities of noises were no noise, 70-dBC, 80-dBC, and 90-dBC. Each noise was sustained for 5 minutes and the ECG was recorded simultaneously. The cardiovascular responses were evaluated using DFA of the beat-to-beat (RR) intervals obtained from ECG signals. The results showed that the mean RR intervals variability and mean blood pressure did not substantially change relative to the noises. However, the short-term scaling exponent (α1) of the DFA of the background noise (no noise) condition was lower than the 70-dBC, 80-dBC and 90-dBC noises (P< 0.05, repeated measures analysis of variance). The α1of 90-dBC noise was significantly higher than the α1of BN condition according to a Mann–Whitney U test (P< 0.01). We concluded that exposure to low-frequency noise significantly affects the temporal correlations of HRV, but it does not influence RR intervals variability.

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