We analysed 11 apparently healthy student women aged between 18 and 30 years old. All volunteers were informed about the procedures and objectives of the study and, after agreeing, signed a consent form. All study procedures were approved by the Research Ethics Committee (REC) of the institution (case number.2011/382) and followed the Resolution 196/96 of the National Health Council.
We did not include women under the following conditions: body mass index (BMI) >35 kg/m2; systolic blood pressure (SBP) >140 mmHg or diastolic blood pressure (DBP) >90 mmHg (at rest); cardiac arrhythmias (atrial flutter or fibrillation, multiple ventricular or atrial ectopy, second or third degree atrioventricular block), smoking, left ventricular dysfunction, neurological or respiratory disorders and serious postural deviation in the chest such as severe scoliosis, kyphosis or hyperlordosis that could influence the respiratory pattern and auditory disorders.
Before the experimental procedure, volunteers were identified by collecting the following information: age, gender, weight, height and body mass index (BMI). Anthropometric measurements were obtained according to Lohman et al. . Weight was determined by using a digital scale (W 200/5, Welmy, Brazil) with a precision of 0.1 kg. Height was determined by using a stadiometer (ES 2020, Sanny, Brazil) with a precision of 0.1 cm and 2.20 m of extension. Body mass index (BMI) was calculated using the following formula: weight (kg)/height (m2). We also measured systolic and diastolic blood pressure and heart rate.
Data were collected in our laboratory under controlled temperature (21°C–25°C) and humidity (50%–60%), and volunteers were instructed to avoid consuming alcohol, caffeine and substances that influence the ANS for 24 hours before evaluation. Data were collected between 8 and 12 AM. All procedures necessary for the data collection were explained to the individuals, and the subjects were instructed to remain at rest and to avoid talking during the data collection.
After the initial evaluation the heart monitor belt was then placed over the thorax, aligned with the distal third of the sternum and the Polar RS800CX heart rate receiver (Polar Electro, Finland) was placed on the wrist. The subject remained 10 minutes seated at rest with spontaneous breathing. After ten minutes the volunteers quickly stood up from a seated position in up to three seconds according to verbal command and remained standing for 15 minutes.
The R-R intervals recorded by the portable HR monitor (with a sampling rate of 1000 Hz) were downloaded to the Polar Precision Performance program (v. 3.0, Polar Electro, Finland). The software enabled the visualization of HR and the extraction of a cardiac period (R-R interval) file in “txt” format. Following digital filtering complemented with manual filtering for the elimination of premature ectopic beats and artifacts, at least 256 R–R intervals were used for the data analysis. Only series with more than 95% sinus rhythm was included in the study [15–17]. HRV was analyzed at four moments: seated rest with spontaneous breathing, 0–5 minutes, 5–10 minutes and 10–15 minutes at standing position. We evaluated the linear and non-linear indices of HRV. For calculation of the indices we used the HRV Analysis software (Kubios HRV v.1.1 for Windows, Biomedical Signal Analysis Group, Department of Applied Physics, University of Kuopio, Finland) [18, 19].
Linear indices of HRV
For HRV analysis in the frequency domain we used spectral components of low frequency (LF: 0.04 to 015 Hz) and high frequency (HF: 0.15-0.40 Hz), inabsolute (ms2) and normalized units and the ratio between components of low and high frequency (LF/HF). Spectral analysis was calculated using the algorithm of fast Fourier transform .
The analysis in the time domain was performed by means of SDNN (standard deviation of the average normal RR intervals), RMSSD (square root of the mean squared differences between adjacent normal RR intervals) and pNN50 (percentage of adjacent RR intervals with a difference of duration greater than 50 ms) .
Fractal analysis of HRV
For the analysis of the fractal properties of the heart rate, detrended fluctuation analysis (DFA) was applied to a time series of the R–R intervals obtained from the participants. The procedure for the calculation of DFA is made up of the following steps:
The R–R series obtained experimentally is integrated using the expression [9, 10]:
in which Y(k) is the k-th term of the integrated series (k = 1, 2,…, N); R–R(i) is the i-th value of the R–R intervals; and R–Rave is the mean of the R–R intervals of the original series, with N length:
The integrated time series is then divided into intervals with a length of n, (n = 1, 2,…, N). In each of these intervals, the local trend of the series is calculated by a straight line of minimum squares adjusted to the data. The y-coordinate of this straight line was denominated Yn(k). The integrated series was then detrended [Y(k)], subtracting the local tendency Yn(k) in each interval. For a given interval of size n, the size characteristic of the fluctuation for the integrated and detrended series is calculated by:
This procedure is repeated for all intervals of size n, thereby obtaining a relation between the mean of the fluctuations [F(n)] and the size of the intervals (n). A linear relation on a log–log graph indicates a scale exponent law, based on the following formula:
in which α is the scale exponent, which can be calculated by linear regression on a log–log graph (16). The following were calculated: short-term fractal exponent (alpha-1), corresponding to a period of 4 to 11 beats; long-term fractal exponent (alpha-2), corresponding to periods longer than 11 beats; and the alpha-1/alpha-2 ratio .
When α = 0.05, there is no correlation and the signal consists of white noise; if α = 1.5, the signal resembles random walk (Brownian motion); and if 0.5 < α < 1.5, there are positive correlations. If alpha is close to 1.0 it indicates a more complex (non-linear) system, if it reaches values above 1.0 the system tends to be less complex and linear.
Standard statistical methods were used for the calculation of means and standard deviations. Normal Gaussian distribution of the data was verified by the Shapiro-Wilk goodness-of-fit test (z value >1.0). For parametric distributions we applied the ANOVA for repeated measures followed by the Bonferroni posttest (SDNN, alpha-2 and alpha-1/alpha-2). For non-parametric distributions we used the Friedman test followed by the Dunn’s test (RMSSD, pNN50, LF, HF, LF/HF and alpha-1). We compared the HRV indices between the four moments (seated rest vs. 0–5 min after the volunteers stood up vs. 5–10 min after the volunteers stood up vs. 10–15 min after the volunteers stood up). Differences were considered significant when the probability of a Type I error was less than 5% (p < 0.05). We used the Software GraphPad StatMate version 2.00 for Windows, GraphPad Software, San Diego California USA.