Comparison of heart and respiratory rate variability ...

1128

ARTICLE Comparison of heart and respiratory rate variability measures using an intermittent incremental submaximal exercise model Appl. Physiol. Nutr. Metab. Downloaded from www.nrcresearchpress.com by University of Ottawa on 08/06/14 For personal use only.

Juliana Barrera-Ramirez, Andrea Bravi, Geoffrey Green, Andrew J. Seely, and Glen P. Kenny

Abstract: To better understand the alterations in cardiorespiratory variability during exercise, the present study characterized the patterns of change in heart rate variability (HRV), respiratory rate variability (RRV), and combined cardiorespiratory variability (HRV–RRV) during an intermittent incremental submaximal exercise model. Six males and six females completed a submaximal exercise protocol consisting of an initial baseline resting period followed by three 10-min bouts of exercise at 20%, 40%, and 60% of maximal aerobic capacity (V˙O2max). The R–R interval and interbreath interval variability were measured at baseline rest and throughout the submaximal exercise. A group of 93 HRV, 83 RRV, and 28 HRV–RRV measures of variability were tracked over time through a windowed analysis using a 5-min window size and 30-s window step. A total of 91 HRV measures were able to detect the presence of exercise, whereas only 46 RRV and 3 HRV–RRV measures were able to detect the same stimulus. Moreover, there was a loss of overall HRV and RRV, loss of complexity of HRV and RRV, and loss of parasympathetic modulation of HRV (up to 40% V˙O2max) with exercise. Conflicting changes in scale-invariant structure of HRV and RRV with increases in exercise intensity were also observed. In summary, in this simultaneous evaluation of HRV and RRV, we found more consistent changes across HRV metrics compared with RRV and HRV–RRV. Key words: complexity, scale-invariant, absolute variation, sympathovagal balance, cardiorespiratory variability, multivariate analysis, variability domains. Résumé : Pour mieux comprendre les modifications de la variabilité cardiorespiratoire au cours d'un exercice physique, la présente étude caractérise les types de modification du rythme cardiaque (HRV), du rythme de la respiration (RRV) et des deux rythmes combinés (HRV–RRV) au cours d'un modèle d'exercice sous-maximal d'intensité croissante par intermittence. Six femmes et six hommes participent a` une séance d'exercice physique d'intensité sous-maximale consistant en une période initiale de repos suivie de trois brèves périodes d'exercice d'une durée chacune de 10 min aux intensités respectives suivantes : 20, 40 et 60 % de la puissance aérobie maximale (V˙O2max). On évalue la variabilité de l'intervalle du rythme de la respiration et de l'intervalle des cycles de la respiration au repos et au cours des efforts d'intensité sous-maximale. On suit dans le temps un groupe de mesures de 93 HRV, 83 RRV et 28 HRV–RRV au moyen d'une fenêtre temporelle d'une durée de 5 min et d'une fenêtre transitoire d'une durée de 30 s. Au total, 91 mesures de HRV détectent la présence de l'exercice physique alors que seulement 46 mesures de RRV et 3 mesures de HRV–RRV détectent le même stimulus. En outre, on note la perte de HRV et de RRV globalement, une perte de complexité de HRV et RRV et une perte de la modulation parasympathique de HRV (jusqu'a` 40 % de V˙O2max) a` l'effort. On observe aussi des modifications conflictuelles de la structure d'invariance d'échelle de HRV et de RRV avec l'augmentation de l'intensité de l'effort. Bref, au cours de cette évaluation simultanée de HRV et de RRV, on observe plus de modifications conformes dans les mesures de HRV comparativement aux mesures de RRV et de HRV–RRV. [Traduit par la Rédaction] Mots-clés : complexité, invariance d'échelle, variation absolue, équilibre sympathico-vagale, variabilité cardiorespiratoire, analyse multivariée, domaines de la variabilité.

Introduction Variability analysis, the study of beat-to-beat or breath-to-breath interval fluctuations (Pumprla et al. 2002; Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology 1996) has been applied to a number of physiological measurements including heart and respiratory rate, known as heart rate variability (HRV) and respiratory rate variability (RRV), respectively. HRV became widely known for its ability to characterize autonomic nervous system (ANS) modulation of heart rate (Akselrod et al. 1985). This has prompted its application in the clinical setting to assess a number of pathological conditions, including cardiovascular diseases

(Almoznino-Sarafian et al. 2009; Bigger et al. 1988; Casolo et al. 1992; de la Cruz Torres et al. 2008; Flapan et al. 1993; Hartikainen et al. 1996; Huikuri et al. 1999), sepsis (Ahmad et al. 2009; Lin et al. 2006), diabetes mellitus (Pagkalos et al. 2008; Velcheva et al. 2011), trauma (Colombo et al. 2008; Fathizadeh et al. 2004), and during anesthesia (Brouse et al. 2011; Shafqat et al. 2011). Furthermore, suppressed HRV has been correlated with disease severity (Ryan et al. 2011) and can serve as a predictor of morbidity and mortality (La Rovere et al. 1998, 2003; Nolan et al. 1998; Perkiomaki et al. 2010; Ryan et al. 2011). Similarly, RRV has been introduced recently in the clinical setting to monitor mechanically ventilated (Papaioannou et al. 2011a, 2011b) and trauma (Colombo et al. 2008;

Received 28 September 2012. Accepted 7 May 2013. J. Barrera-Ramirez and G.P. Kenny.*,† Human and Environmental Physiology Research Unit, Faculty of Health Sciences, University of Ottawa, Ottawa, ON K1N 6N5, Canada. A. Bravi, G. Green, and A.J. Seely. Dynamical Analysis Laboratory, Ottawa Hospital Research Institute, Ottawa Hospital – General Campus, Ottawa, ON K1H 8L6, Canada. Corresponding author: Glen P. Kenny (e-mail: [email protected]). *Present address: School of Human Kinetics, University of Ottawa, 125 University Private, Room 367 Montpetit Hall, Ottawa, ON K1N 6N5, Canada. †All editorial decisions for this paper were made by Maureen MacDonald and Terry Graham.

Appl. Physiol. Nutr. Metab. 38: 1128–1136 (2013) dx.doi.org/10.1139/apnm-2012-0370

Published at www.nrcresearchpress.com/apnm on 16 May 2013.

Barrera-Ramirez et al.

1129

Table 1. Participant characteristics. V˙O2max

Male Female

N

Age (years)

Height (cm)

Weight (kg)

L·min−1

mL·kg−1·min−1

REE (kJ·min−1)

6 6

23±2 23±2

175.0±9.7 160.2±5.4

78.5±9.2 58.8±6

4.2±0.4 2.4±0.4

53.55±3.5 40.62±7.3

5.60±0.50 3.70±0.27

Appl. Physiol. Nutr. Metab. Downloaded from www.nrcresearchpress.com by University of Ottawa on 08/06/14 For personal use only.

Note: Values reflect the mean and standard deviation (±SD) for all 12 participants; V˙O2max, maximum oxygen uptake; REE, resting energy expenditure; N, number of participants.

Fathizadeh et al. 2004) patients and in pediatrics (Kuznetsova and Son'kin 2005). Apart from being used in a clinical setting, variability analysis has also been examined in the context of exercise. HRV has been consistently shown to decrease during exercise (Sandercock and Brodie 2006). Studies assessing HRV during graded maximal exercise (Arai et al. 1989; Armstrong et al. 2011; De Meersman 1993; Shin et al. 1995a, 1995b) and during incremental submaximal exercise (Cottin et al. 2004; Hautala et al. 2003; Lewis et al. 2007; Mendonca et al. 2009; Perini et al. 1990; Warren et al. 1997) have consistently reported decreased overall variability. However, most of these studies have restricted their analysis of variability to few variability domains. Moreover, they have not simultaneously evaluated both HRV and RRV metrics during exercise. Because the characterization of HRV and RRV during exercise provides a potential means of understanding the cardiorespiratory physiology underlying variability, it is important to evaluate both measures at the same time. The study of simultaneous patterns of change between two or more systems, in this case heart and respiratory rates, has a recent history (Schafer et al. 1998) and can be defined as cardiorespiratory variability analysis. Besides the known phenomenon of respiratory sinus arrhythmia (Hirsch and Bishop 1981) (i.e., the modulation of the heart rate due to respiration), there is still a lack of understanding of the coupling between the two systems; this is particularly true when their functions are observed during exercise. Thus, the following study was conducted to evaluate how both single- and multi-organ measures of HRV and RRV perform in discriminating between a series of stimuli of varying intensity. To achieve this, we characterized R–R interval (RRI) and interbreath interval (IBI) variability during intermittent incremental submaximal exercise. This exercise model allowed us to examine variability at precise degrees of stress, namely from rest to 20%, 40%, and 60% of maximal aerobic capacity (V˙O2max). Together with HRV and RRV, this study characterizes the similarity in the patterns of change occurring simultaneously in both RRI and IBI time series, i.e., cardiorespiratory variability (we will refer to it as HRV– RRV). To the best of our knowledge, this is the first study to simultaneously monitor both HRV and RRV during incremental submaximal exercise. The evaluation of cardiorespiratory variability is also a novel component of this study and is of particular interest because it reflects the coupling between two organ systems which (according to complex systems theory) are supposed to be interrelated (Bravi et al. 2011; Cerutti et al. 2009; Seely and Macklem 2012). We hypothesized that (i) HRV and RRV will be able to detect changes in exercise intensity to the same extent and (ii) the additive effect of using both signals, HRV–RRV (cardiopulmonary variability), will allow for a better detection of changes in exercise intensity.

Materials and methods Participants Six males and six females were recruited for this study. All participants were healthy with no prior history of cardiac or pulmonary illness. None was currently taking beta blockers or other medication that could compromise their eligibility. Mean (±SD) age, height, weight, V˙O2max, and resting energy expenditure (REE)

are provided in Table 1. The experimental protocol was approved by the University of Ottawa Health Sciences and Science Research Ethics Board in accordance with the Declaration of Helsinki. Written informed consent was obtained from all subjects prior to their participation in the study. Experimental protocol Preliminary and screening session Participants were asked to complete a Physical Activity Readiness Questionnaire (PAR-Q) to assess their eligibility to engage in physical activity. All participants completed a V˙O2max test consisting of running at a constant speed on a motorized treadmill (DESMO; Woodway USA, Inc., Waukesha, Wisconsin, USA) while the grade was increased by 1% per minute until exhaustion. Throughout the test, participants wore a nose clip while breathing into a one-way valve connected to a metabolic unit (MOXUS; AEI Technologies, Inc., Naperville, Illinois, USA) previously calibrated according to manufacturer recommendations. Oxygen consumption (V˙O2), carbon dioxide output (V˙CO2), respiratory exchange ratio (RER), heart rate, tidal volume (Vt), and ventilation (V˙e) were measured breath-by-breath and averaged over 10-s intervals. Anthropometric measures such as age, height, and weight were also recorded. Participants were asked to refrain from engaging in intense exercise and consuming alcoholic or caffeinated beverages on the day of the test. Experimental session All sessions were scheduled between 0800 and 1100 at least 48 h after the preliminary visit. Resting baseline measures of HRV and RRV were collected during a resting metabolic rate (RMR) test followed by a submaximal exercise protocol. Participants were asked to avoid intense exercise for 48 h, refrain from consuming alcoholic or caffeinated beverages for 24 h, fast for 12 h, and sleep for at least 8 h prior to coming to the lab. RMR test Upon arrival, participants donned a Polar heart rate monitor (Polar Electro Oy, Kempele, Finland) and a BioFusion physiology monitor (Biopeak Corporation, Ottawa, Ontario, Canada), both of which were placed around the thoracic region (BioFusion monitor below the Polar monitor). Participants were transferred to a thermoneutral, semi-darkened, quiet room where they rested for 30 min in a semi-recumbent position. They were instructed to relax and avoid falling asleep; however, they were allowed to read anything except a newspaper, as it can cause emotional arousal. Upon completion of the initial resting period, participants put on a nose clip and a mouthpiece with a one-way valve connected to an automated metabolic unit calibrated prior to the test. Before starting the test, participants were given a 5-min period to become accustomed to breathing through the mouthpiece and to prevent hyperventilation during the test. Metabolic data including V˙O2, V˙CO2, RER, Vt, and V˙e were measured breath-by-breath for 20 min and averaged over 30-s intervals, but only data from minutes 5 to 15 were used for data analysis, as this period has been shown to be the most stable period (Horner et al. 2001). RMR (kJ/min) was determined according the Weir equation: Published by NRC Research Press

1130

(1)

Appl. Physiol. Nutr. Metab. Vol. 38, 2013

RMR ⫽ 4.1868{V˙O2[(RER ⫺ 0.7)/0.3]}ec ⫹ {[(1 ⫺ RER)/0.3]ef}


where V˙O2 is the rate of oxygen consumption (L/min), RER is the respiratory exchange ratio, ec is the caloric equivalent for the oxidation of carbohydrates (5.047 kcal/L O2), and ef is the caloric equivalent for the oxidation of fats (4.686 kcal/L O2) (Weir 1949). Submaximal exercise Upon completion of the RMR test, participants were asked to undergo an incremental steady-state submaximal exercise protocol on a motorized treadmill. The test consisted of an initial 10-min baseline period during which participants stood on the treadmill. This was followed by three 10-min intermittent exercise bouts of increasing intensity at 20%, 40%, and 60% of V˙O2max, respectively. The treadmill grade was held constant throughout the three successive exercise bouts and the speed was adjusted to achieve the desired intensity. After each exercise stage, participants were asked to stop running and to stand on the treadmill for a 5-min resting period. The last exercise stage was followed by a 20-min recovery period. Throughout the test, participants wore a nose plug and a mouthpiece with a one-way valve connected to an automated metabolic unit. Oxygen consumption, V˙CO2, RER, Vt, and V˙e were measured breath-by-breath and averaged over 10-s intervals. The purpose of using this exercise model was to elicit distinct degrees of stress by increasing the exercise intensity at each stage. The exercise intensities employed (i.e., 20%, 40%, and 60% of V˙O2max) were chosen to allow for a clear separation between stages, while at the same time remaining below the anaerobic threshold to ensure stable heart rate and respiratory rate responses. Given the nonlinear increase in ventilation experienced above the anaerobic threshold (Svedahl and MacIntosh 2003), it was important to eliminate this confounding factor when trying to obtain stable respiratory measures for RRV analysis. Furthermore, the use of 10-min exercise stages allowed us to achieve a certain degree of “stationarity” in the signals, which has been noted as an important requirement for variability analysis (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology 1996). HRV and RRV analyses Electrocardiogram monitoring was conducted for the duration of the entire experimental session using a BioFusion physiological monitor. The ECG tracings were converted to R-R intervals time series and, subsequently, to HRV. Interbreath intervals were obtained from the metabolic unit and subsequently converted into RRV. A set of 93 HRV, 83 RRV, and 28 HRV–RRV measures of variability were analyzed. The analysis included both single organ and multiorgan measures. Single-organ variability refers to the analysis of one signal at a given time, whereas multiorgan, (referred to cardiorespiratory variability) examines two or more signals simultaneously to determine how the properties of both signals change over time. In the assessment of single-organ variability, the following measures were included: statistical (standard deviation, kurtosis), geometric (Poincaré plot, recurrence plot), informational (sample entropy, multiscale entropy), energetic (power spectrum, energy operators), and invariant domains (detrended fluctuation analysis, largest Lyapunov exponent) (Bravi et al. 2011). Similarly, the analysis of simultaneous patterns of change in both cardiac and respiratory systems was pursued through measures of cardiorespiratory variability such as the mutual information (Kraskov et al. 2005), Spearman's correlation, crossrecurrence plots (Marwan et al. 2007), and cross-spectral features (Wei 2006). Each measure was tracked over time through a windowed analysis using a 5-min window size and 30-s window step (90% overlap). Whenever needed, spline interpolation was used to synchronize heart and respiratory event time series. This analysis was conducted for the duration of the entire test (⬃2.5 h). Baseline

measures of HRV, RRV, and HRV–RRV are defined as the median variability (calculated over the 5-min windows) during the RMR test. Furthermore, the median variability was also calculated for each of the exercise intensities (i.e., 20%, 40%, and 60% V˙O2max). All analyses of variability were carried out at the Ottawa Hospital Research Institute (OHRI) using the Continuous Individualized Multiorgan Variability Analysis (CIMVA™) software (version 0.99; Dynamical Analysis Laboratory, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada). For a full description of the variability measures used in this analysis, please refer to the CIMVA reference manual (available from http://ohridal.org/cimva/CIMVACore-Description.pdf). Statistical analysis The Friedman test, a nonparametric one-way ANOVA with the repeated factor of “exercise intensity” (4 levels: rest and 20%, 40%, and 60% V˙O2max), was used to determine if there were any differences across exercise intensities for each variability measure. Post hoc analysis of preplanned comparisons was performed on those variability measures that showed a significant difference across exercise intensity. The preplanned comparisons consisted of testing for significant difference between (i) rest and 20% V˙O2max, (ii) 20% and 40% V˙O2max, or (iii) 40% and 60% V˙O2max. The Wilcoxon signed-rank test was used to check the null hypothesis that the lower intensity distribution had a lower or higher median than a higher intensity distribution (single-tail test). The direction of the tail was selected for each measure by comparing the median at rest with the median at 60% V˙O2max (i.e., if the distribution at rest was smaller than at 60% V˙O2max, a left-tail test was performed). Given the large number of measures involved in this analysis, the robust false discovery rate method (Pounds and Cheng 2006) was used to account for multiple comparisons (e.g., for HRV, the false discovery rate was applied to the 93 p values, one for each measure, in each comparison). The method was used to identify the alpha values imposing a 5% of false positives among the rejected null hypotheses for the Friedman test and, separately, for the Wilcoxon signed-rank tests. All statistical analyses were performed using MATLAB 2012a (The Mathworks, Natick, Mass., USA). While CIMVA software allowed us to assess a wide number of measures of variability, the following subset of variability measures was selected for further analysis: absolute variation (standard deviation), sympathovagal modulation (the ratio between low-frequency (LF) power and high-frequency (HF) power (LF/HF ratio), complexity (sample entropy), and scale-invariant (Hurst exponent) dimensions. This subset of measures was selected based on their already known link to human physiology (for details, see Table 2), therefore aiding in the interpretation of the results. All of the measures were used in the analysis of HRV and RRV with the exception of the sympathovagal modulation dimension, which only applies to HRV. A comprehensive list of all of the measures employed in this study, together with their results, is available in electronic format upon request.

Results Of the measures of variability that were extracted, a total of 91 HRV, 46 RRV, and 3 HRV–RRV measures showed a significant difference between any of the exercise intensities (rest, 20%, 40%, and 60% V˙O2max) (Table 3). When examining the differences between specific exercise intensities, 32 HRV measures showed a significant difference between rest and 20% V˙O2max (␣ = 0.006), 57 showed a difference between 20% and 40% V˙O2max (␣ = 0.006), and 25 showed a difference between 40% and 60% V˙O2max (␣ = 0.003) (Table 4). Respiratory rate variability, on the other hand, had 15 measures showing a statistically significant difference between rest and 20% V˙O2max (␣ = 0.01), 17 showed a difference between 20% and 40% V˙O2max (␣ = 0.008), and 4 displayed a difference between the two highest intensities, 40% and 60% V˙O2max (␣ = 0.001). Published by NRC Research Press


1131

Table 2. Physiological dimensions of variability. Dimensions of variability


Absolute variation

Sympathovagal modulation (HRV only)

Complexity

Scale-invariant

Link to physiology

Prototypical measures

This dimension encompasses measures that describe the workload of a system. It is suggested that, physiologically, these measures reflect the ratio of maximal aerobic capacity (V˙O2max) to baseline resting levels of oxygen consumption (V˙O2resting) (Seely and Macklem 2012). This dimension is composed of measures that have the ability to characterize autonomic nervous system modulation on the heart. It further reflects the contributions of the parasympathetic and sympathetic stimulation (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology 1996). This dimension applies only to HRV. This dimension reflects the nonlinear coupling between the considered organ and other organ systems, as well as its ability to adapt to external stimuli (robustness) (Glass 2001; Seely and Macklem 2012). This dimension represents the fractality of a system, which reflects its capacity to dissipate energy gradients. This has been linked to the capacity of the cardiorespiratory system to deliver oxygen and clear carbon dioxide, normalized by mass and time (Seely and Macklem 2012).

Standard deviation

LF/HF ratio (Lomb's method)

Sample entropy

Hurst exponent (scaled windowed variance)

Note: For further details about the measures, refer to Bravi et al. (2011). LF/HF ratio, the ratio between low-frequency and high-frequency powers.

Table 3. Total number of variability measures of all the measures available for HRV, RRV, and HRV–RRV that were able to detect the presence of an exercise stimulus.

No. of measures rejecting the null hypothesis of equal median Median ± IQR of the p values of the measures rejecting the null hypothesis Alpha values for rejection

HRV

RRV

HRV–RRV

91/93

46/83

3/28

0.4×10−4±0.2×10−2

0.5×10−2±0.7×10−2

0.2×10−3±0.6×10−2

0.4

0.6×10−1

0.1×10−1

Note: IQR, interquartile range; HRV, heart rate variability; RRV, respiratory rate variability; HRV–RRV, cardiorespiratory variability. The alpha values were identified applying the robust false discovery rate technique, imposing a 5% of false positives.

Finally, only three measures of HRV–RRV showed a significant change between 20% and 40% V˙O2max (␣ = 0.03), while no measures showed significant differences between rest to 20% and 40% to 60% V˙O2max (Table 4). A total of 10 HRV and 2 RRV measures successfully differentiated all of the selected comparisons (i.e., rest vs. 20%, 20% vs. 40%, and 40% vs. 60% V˙O2max). Median and interquartile range (in parentheses) for heart rate were 55 beats/min (53.8– 66.9) at rest, 78 beats/min (73.9–81.7) at 20% V˙O2max, 109 beats/min (106.0–112.7) at 40% V˙O2max, and 142 beats/min (137.4–149.9) at 60% V˙O2max. Similarly, median and interquartile range (in parentheses) for breathing rate were 16 breaths/min (13.3–16.8) at rest, 20 breaths/min (17.6–23.2) at 20% V˙O2max, 30 breaths/min (26.2–35.3) at 40% V˙O2max, and 37.9 breaths/min (31.9–41.0) at 60% V˙O2max. From the subset of measures selected for further analysis (Table 2), all of the measures of variability rejected the Friedman test for HRV and RRV. Changes in variability from rest to the different stages of exercise for all four selected measures of HRV are illustrated in Fig. 1. The absolute variation showed a significant decrease with increases in exercise intensity. The complexity dimension, on the other hand, showed decreasing trends at higher stages of exercise, even if not statistically significant (Fig. 1). Only the LF/HF ratio and the Hurst exponent showed a significant increase with exercise, in particular, from 20% to 40% V˙O2max (Fig. 1). Changes in variability in absolute variation, complexity, and scale-invariant measures of RRV are illustrated in

Fig. 2. With the exception of standard deviation, no measures showed statistical significant changes, even if the majority had a decreasing trend. The measure that showed the largest change was the Hurst exponent in the scale-invariant domain, although it did not reach statistical significance (Fig. 2). The alpha values for the significance of all of the described comparisons can be found in Tables 3 and 4.

Discussion In this simultaneous evaluation of HRV and RRV during incremental submaximal exercise, we have uncovered more consistent changes across the spectrum of HRV metrics in comparison with RRV and HRV–RRV with increasing exercise. HRV was able to detect the presence of the exercise stimulus with a greater number of its measures (91/93), whereas RRV and HRV–RRV were only able to detect the stimulus with 46 of 83 and 3 of 28 measures, respectively. Furthermore, HRV was able to track changes in exercise intensity with multiple measures, which was not possible for HRV–RRV and difficult for RRV. These results are congruent with previous studies on HRV and exercise (Hautala et al. 2003; Lewis et al. 2007) and further expand our understanding of the performance of RRV and HRV–RRV. The findings from the present study also showed that all three types of variability were able to detect the specific change in exercise intensity from 20% to 40% of Published by NRC Research Press

Note: IQR, interquartile range; HRV, heart rate variability; RRV, respiratory rate variability; HRV–RRV, cardiorespiratory variability. The alpha values were identified applying the robust false discovery rate technique, imposing a 5% of false positives.

— 0.3×10−1 0.1×10−2 0.7×10−2 0.7×10−2

0.3×10−2

0.1×10−1

0.8×10−2

—

— 0.2×10−2±0.1×10−1 0.4×10−3±0.1×10−1 0.2×10−2±0.3×10−2 0.4×10−3 0.2×10−2±0.3×10−2

0.4×10−3±0.6×10−3

0.4×10−2±0.4×10−2

—

3/3 0/3 4/46 17/46 57/91

No. of measures rejecting the null hypothesis of equal median Median ± IQR of the p values of the measures rejecting the null hypothesis Alpha values for rejection

32/91

25/91

15/46

Rest vs. 20%

20% vs. 40% HRV–RRV

20% vs. 40%

40% vs. 60%

40% vs. 60% Rest vs. 20% Rest vs. 20%

20% vs. 40% RRV HRV

Table 4. Total number of measures of variability for HRV, RRV, and HRV–RRV that tracked changes in exercise intensity from rest to 20%, 20% to 40%, and 40% to 60% V˙O2max.


0/3


40% vs. 60%

1132

V˙O2max. This may suggest a nonlinear relationship between exercise intensity and changes in HRV, RRV, and HRV–RRV, i.e., each type of variability might exhibit a different rate by which variability is lost in association with increasing exercise. The simultaneous assessment of HRV and RRV further showed that although HRV distributions at any given stage of exercise had a small range, their corresponding RRV distributions were quite variable. More specifically, the median of several RRV measures showed a trend proportional to the exercise intensity, yet failed statistical significance (Wilcoxon test) because of the larger variance in the distributions (see Fig. 2). This observation highlights the “biologically noisier” nature of RRV extracted from the IBI time series, which can be attributed to at least three factors. The first is the effect of the numerical inconsistency of the measures (i.e., each measure is an estimator with an accuracy that depends on the number of samples available). Given that an IBI time series has fewer samples than an RRI time series for a fixed time interval, it results in a nosier signal. However, this effect can be attenuated by using a window size larger than 5 min for the windowed analysis. The protocol used in the present study limited the windowed analysis to smaller windows, thus making it difficult to increase the window size. Second, an intrinsic property of the IBI results in different spreads between the mean heart rate and respiratory rate distributions. Taking into consideration that the sample mean is a consistent estimator that converges to the true mean with few samples, the higher variance of the IBI with respect to the RRI has to be related to the signal rather than just the number of samples. Third, given that respiratory rate has a greater degree of conscious control than heart rate, we hypothesize that subjects with heterogeneous specific respiratory training (e.g., voice training, meditation, or wind musical instrument training) may contribute to the observed greater variation in RRV (compared with HRV) as exercise increases. Regardless, with this intrinsic higher variance, larger population studies evaluating RRV are warranted to further explore this finding. Physiological considerations Literature on the physiological meaning of each measure of variability is very limited. However, we have attempted to create a model of the hypothetical links between measures of variability and physiology with a subset of measures (Table 2). Although this model has been based on published hypotheses and empirically derived data, it is likely to change with future research. The selection of these measures was primarily based on the lack of correlation between them, allowing for independent sets of variability to be explored. Also, each measure has been placed under a potential “dimension” of variability analysis (i.e., absolute variation, sympathovagal modulation, complexity, and scale-invariant dimensions). The discussion below will focus on the observed changes in measures within each of the proposed “dimensions” of variability. The most widely known measures of HRV in human physiology are HF power and LF power due to their application in cardiac autonomic modulation. Early studies using pharmacological blockade and postural challenge in dogs (Akselrod et al. 1985) and humans (Pomeranz et al. 1985; Warren et al. 1997) documented that spectral analysis has the ability to characterize both sympathetic and parasympathetic stimulation in the body by means of spectral frequencies (Akselrod et al. 1981; Saul 1990). More specifically, HF power has been suggested to reflect parasympathetic activity on the sinoatrial node of the heart, whereas LF power represents both sympathetic and parasympathetic stimulation (Akselrod et al. 1985; Pomeranz et al. 1985; Saul 1990; Stein et al. 2005; Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology 1996). Our findings showed that HF power significantly dropped from rest to the start of exercise, which can be interpreted as a decrease in parasympathetic modulation. It is widely accepted that at the Published by NRC Research Press


1133


Fig. 1. Changes in the physiological dimensions of heart rate variability (HRV) from baseline rest throughout the 3 stages of the submaximal exercise protocol (20%, 40%, and 60% V˙O2max). From left to right: standard deviation (SD) (seconds), ratio between low-frequency (LF) power and high-frequency (HF) power through Lomb's method (LF/HF ratio), sample entropy (Sample Ent), and Hurst exponent (Hurst Exp). Each sample in the distributions (circle) represents the median variability over the analysis windows for one subject at the different stages of the experimental protocol (i.e., rest, 20%, 40%, and 60% V˙O2max). The median of each distribution is represented by a solid black line, and the grey box represents the interquartile range. An asterisk (*) indicates statistically significant differences between stages.

Fig. 2. Changes in the physiological dimensions of respiratory rate variability (RRV) from baseline rest throughout the 3 stages of the submaximal exercise protocol (20%, 40%, and 60% V˙O2max). From left to right: standard deviation (SD) (seconds), sample entropy (Sample Ent), and Hurst exponent (Hurst Exp). Each sample in the distributions (circle) represents the median variability over the analysis windows for one subject at the different stages of the experimental protocol (i.e., rest, 20%, 40%, and 60% V˙O2max). The median of each distribution is represented by a solid black line, and the grey box represents the interquartile range. An asterisk (*) indicates statistically significant differences between stages.

start of exercise, heart rate increases primarily due to parasympathetic withdrawal (Goldsmith et al. 2000), which explains the dramatic drop in HF power observed at the start of exercise. In contrast, sympathetic activity cannot be as easily isolated with LF power (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology 1996), and therefore, it is hard to establish the degree of sympathetic stimulation throughout the exercise protocol. Nonetheless, it is expected to increase with every subsequent bout of exercise as increases in heart rate during exercise are mainly the result of increased sympathetic stimulation (Goldsmith et al. 2000). Another measure of sympathovagal modulation is the LF/HF ratio, which has been suggested to reflect cardiac sympathovagal balance (Seely and Macklem 2004). While some studies have reported a drop in LF/HF ratio during exercise (Casties et al. 2006; Cottin et al. 2004), others have reported a more atypical behavior characterized by an initial increase followed by a drop at higher exercise intensities (Hautala et al. 2003). In the present study, the LF/HF ratio significantly increased during the initial stages of exercise (20% and 40% V˙O2max); however, this increase was followed by a dramatic drop at the highest intensity (60% V˙O2max) (Fig. 1). A similar response has also been reported by Hautala et al. (2003), who showed that the LF/HF ratio dropped after reaching 40% V˙O2max during a maximal exercise protocol. This atypical pattern of change in variability has been suggested to reflect the inability of the LF/HF ratio to detect changes in autonomic regulation during intense exercise (Hautala et al. 2003). Even though our findings showed a reduction in the LF/HF ratio at 60% V˙O2max, we are unable to assess whether this decrease is also evident at even higher exercise intensities. It remains unclear what may have

caused the drop in the LF/HF ratio after 40% V˙O2max considering that other studies (Casties et al. 2006; Cottin et al. 2004) did not report such a decrease when employing exercise protocols that exceeded an intensity of 40% V˙O2max. A more recently explored dimension of variability is the “complexity” dimension, which has been linked to the degree of health of a system. It has been suggested that healthy systems show high degrees of complexity (Costa et al. 2005; Goldberger et al. 2002) and have the ability to adapt to external stimuli (Goldberger et al. 2002; Pumprla et al. 2002). Furthermore, the assessment of complexity can also help expand our understand of the various systems that mediate changes in the cardiovascular system (Javorka et al. 2002). To our knowledge, the change in complexity with subsequent increases in exercise intensity has not been previously evaluated in both HRV and RRV. Findings in the present study showed that sample entropy was significantly reduced after having participants exercise at 60% of their maximal capacity when compared with their baseline resting values for both HRV and RRV. This loss of irregularity in both HRV and RRV may reflect lack of complexity of organ coupling, as well as lack of adaptability of the various feedback loops with increasing exercise, affecting both the heart and lungs. In future studies, longer periods of exercise would enable the evaluation of multiscale measures of complexity such as multiscale entropy to determine if the loss of complexity persists over time scales. The “absolute variation” dimension of variability is hypothesized to reflect the workload of a system. In other words, it represents the ratio between maximal work and current work output (Seely and Macklem 2012), estimated by maximal V˙O2 divided by current V˙O2. In both HRV and RRV, standard deviation decreased Published by NRC Research Press


1134

with increases in exercise intensity (current V˙O2); however, it is evident that loss of variability is not linear with increasing exercise intensity (see Figs. 1 and 2). Nonetheless, the loss of variability has been attributed to the increase in workload and strain on both cardiovascular and respiratory systems seen during exercise (Vatner and Pagani 1976). Among the four proposed dimensions of variability (Table 2), the scale-invariant measure remains the most challenging and least studied to date. Both HRV and RRV have been shown to display scale-free (fractal) characteristics, i.e., when the time series is magnified, the same pattern is displayed (Goldberger 1996). The present study showed that the Hurst exponent of HRV increased with exercise intensity, while it simultaneously decreased for RRV. Although a more variable respiration is associated with increased respiratory function, e.g., intubated ICU patients (Gamma de Abreu et al. 2009), the fact that the Hurst exponent changed in opposite directions highlights the presence of mechanisms that alter variability and still require further characterization. A final aspect of the present study was the assessment of combined cardiorespiratory variability. Although there is much work remaining to be done in how best to characterize cardiorespiratory variability, we attempted to evaluate HRV–RRV during exercise. Our findings showed that HRV–RRV measures were not able to track changes in stimuli, in this case, exercise intensity, as effectively as HRV or RRV, contrary to our second hypothesis. However, this may be explained by two main factors. First, the short-time fluctuations of RRI and IBI time series are strongly independent of each other and therefore should be related to different physiological phenomena. Second, given that RRI and IBI time series track in a similar way the stages of exercise (data not shown), it is possible that for cardiorespiratory variability, a 5-min windowed analysis may not be appropriate, and therefore, larger windows should be used. In keeping with the MMM paradigm (multivariate, multiorgan, and multiscale) pioneered by Cerutti et al. (2009), we support the simultaneous and comprehensive analysis of cardiorespiratory variability. Exploring the means to track HRV and RRV in a composite manner remains the subject of ongoing study. Limitations Given the explorative nature of this study, a major limitation is the small sample size, which may account for the poor performances in the tracking of changes in exercise intensity of certain measures. Furthermore, extraction of other features of the respiratory waveform for the analysis of RRV should be considered for future research. For instance, using the time series of the time of expirations or inspirations rather than the IBI may help provide more stable results. Also, there is a number of other means to characterize cardiorespiratory variability, making the interpretation of its utility still partial. Nevertheless, this work can serve as the basis for further discussion on its relevance and physiological explanation. Noteworthy baseline resting measures of variability were performed during a resting metabolic rate test in a semirecumbent posture. The transition in posture associated with the upright treadmill exercise would likely have influenced the level of sympathetic and parasympathetic activation. As such, this would in turn affect the magnitude of change in variability in HRV, RRV, and HRV–RRV in our comparison between resting and the first exercise stage (i.e., 20% V˙O2max) only. Conclusion The simultaneous evaluation of HRV and RRV resulted in more consistent changes across HRV measures in comparison with RRV and HRV–RRV. Greater variance in RRV was observed even when exercise intensity was increased. By examining a subset of variability measures, we demonstrated loss of overall HRV and RRV, loss of complexity of HRV and RRV, and loss of parasympathetic


modulation of HRV (up to 40% V˙O2max) and conflicting changes in scale-invariant structures of HRV and RRV with increased exercise intensity. While the physiological interpretation of individual domains of variability remains to be further elucidated, this study provides a broad and comprehensive analysis of variability as basis for future research. The science of variability analysis is now grappling with the potential complimentary information contained in cardiorespiratory variability, as well as the information available across a multitude of measures, and this pilot study provides a first attempt to address these challenges with respect to a physiologic stimulus (i.e., exercise). While the application of the study lies strictly with the interpretation of HRV, RRV, and HRV–RRV analysis in healthy adults, this information could eventually be used to better understand changes in variability associated with states of disease. Increased understanding of the physiology of cardiorespiratory variability assists with the interpretation of cardiorespiratory variability monitoring in clinical environments.

Acknowledgements This research was supported indirectly through financial support from the Canadian Institutes of Health Research and a Leaders Opportunity Fund from the Canada Foundation for Innovation (22529). G.P. Kenny is supported by a University of Ottawa Research Chair in Environmental Physiology. J. Barrera-Ramirez, A. Bravi, and G.P. Kenny have no conflicts of interest to disclose. G. Green is the Product Manager, and A. Seely is Founder and Chief Science Officer of Therapeutic Monitoring Systems, founded to improve care through clinical decision support rooted in Continuous Individualized Monitoring Variability Analysis (CIMVA) software.

References Ahmad, S., Ramsay, T., Huebsch, L., Flanagan, S., McDiarmid, S., Batkin, I., et al. 2009. Continuous multi-parameter heart rate variability analysis heralds onset of sepsis in adults. PLoS One, 4(8): e6642. doi:10.1371/journal.pone. 0006642. PMID:19680545. Akselrod, S., Gordon, D., Ubel, F.A., Shannon, D.C., Berger, A.C., and Cohen, R.J. 1981. Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. Science, 213(4504): 220–222. doi: 10.1126/science.6166045. PMID:6166045. Akselrod, S., Gordon, D., Madwed, J.B., Snidman, N.C., Shannon, D.C., and Cohen, R.J. 1985. Hemodynamic regulation: investigation by spectral analysis. Am. J. Physiol. 249(4): H867–H875. PMID:4051021. Almoznino-Sarafian, D., Sarafian, G., Zyssman, I., Shteinshnaider, M., Tzur, I., Kaplan, B.Z., et al. 2009. Application of HRV-CD for estimation of life expectancy in various clinical disorders. Eur. J. Intern. Med. 20(8): 779–783. doi:10. 1016/j.ejim.2009.08.006. PMID:19892308. Arai, Y., Saul, J.P., Albrecht, P., Hartley, L.H., Lilly, L.S., Cohen, R.J., et al. 1989. Modulation of cardiac autonomic activity during and immediately after exercise. Am. J. Physiol. 256(1): H132–H141. PMID:2643348. Armstrong, R.G., Kenny, G.P., Green, G., and Seely, A.J. 2011. Diurnal variation in heart rate variability before and after maximal exercise testing. Chronobiol. Int. 28(4): 344–351. doi:10.3109/07420528.2011.559674. PMID:21539426. Bigger, J.T., Jr., Kleiger, R.E., Fleiss, J.L., Rolnitzky, L.M., Steinman, R.C., and Miller, J.P. 1988. Components of heart rate variability measured during healing of acute myocardial infarction. Am. J. Cardiol. 61(4): 208–215. doi:10.1016/ 0002-9149(88)90917-4. PMID:3341195. Bravi, A., Longtin, A., and Seely, A.J. 2011. Review and classification of variability analysis techniques with clinical applications. Biomed. Eng. Online, 10: 90. doi:10.1186/1475-925X-10-90. PMID:21985357. Brouse, C.J., Karlen, W., Myers, D., Cooke, E., Stinson, J., Lim, J., et al. 2011. Wavelet transform cardiorespiratory coherence detects patient movement during general anesthesia. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2011: 6114– 6117. doi:10.1109/IEMBS.2011.6091510. PMID:22255734. Casolo, G.C., Stroder, P., Signorini, C., Calzolari, F., Zucchini, M., Balli, E., et al. 1992. Heart rate variability during the acute phase of myocardial infarction. Circulation, 85(6): 2073–2079. doi:10.1161/01.CIR.85.6.2073. PMID:1591826. Casties, J.F., Mottet, D., and Le Gallais, D. 2006. Non-linear analyses of heart rate variability during heavy exercise and recovery in cyclists. Int. J. Sports Med. 27(10): 780–785. doi:10.1055/s-2005-872968. PMID:16586334. Cerutti, S., Hoyer, D., and Voss, A. 2009. Multiscale, multiorgan and multivariate complexity analyses of cardiovascular regulation. Philos. Transact. A Math. Phys. Eng. Sci. 367(1892): 1337–1358. doi:10.1098/rsta.2008.0267. PMID: 19324712. Colombo, J., Shoemaker, W.C., Belzberg, H., Hatzakis, G., Fathizadeh, P., and Demetriades, D. 2008. Noninvasive monitoring of the autonomic nervous Published by NRC Research Press



system and hemodynamics of patients with blunt and penetrating trauma. J. Trauma, 65(6): 1364–1373. doi:10.1097/TA.0b013e31818cc307. PMID:19077628. Costa, M., Goldberger, A.L., and Peng, C.K. 2005. Multiscale entropy analysis of biological signals. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 71(2): 021906. doi:10.1103/PhysRevE.71.021906. PMID:15783351. Cottin, F., Medigue, C., Lepretre, P.M., Papelier, Y., Koralsztein, J.P., and Billat, V. 2004. Heart rate variability during exercise performed below and above ventilatory threshold. Med. Sci. Sports Exerc. 36(4): 594–600. doi:10.1249/01.MSS. 0000121982.14718.2A. PMID:15064586. de la Cruz Torres, B., López López, C., and Naranjo Orellana, J. 2008. Analysis of heart rate variability at rest and during aerobic exercise: a study in healthy people and cardiac patients. Br. J. Sports Med. 42(9): 715–720. doi:10.1136/bjsm.2007.043646. PMID:18199627. De Meersman, R.E. 1993. Heart rate variability and aerobic fitness. Am. Heart J. 125(3): 726–731. doi:10.1016/0002-8703(93)90164-5. PMID:8438702. Fathizadeh, P., Shoemaker, W.C., Wo, C.C., and Colombo, J. 2004. Autonomic activity in trauma patients based on variability of heart rate and respiratory rate. Crit. Care Med. 32(6): 1300–1305. doi:10.1097/01.CCM.0000127776.78490. E4. PMID:15187510. Flapan, A.D., Wright, R.A., Nolan, J., Neilson, J.M., and Ewing, D.J. 1993. Differing patterns of cardiac parasympathetic activity and their evolution in selected patients with a first myocardial infarction. J. Am. Coll. Cardiol. 21(4): 926– 931. doi:10.1016/0735-1097(93)90349-6. PMID:8450162. Gamma de Abreu, M., Spieth, P., and Pelosi, P. 2009. Variable mechanical ventilation; breaking the monotomy. Springer-Verlag, Berlin, Heidelberg. Glass, L. 2001. Synchronization and rhythmic processes in physiology. Nature, 410(6825): 277–284. doi:10.1038/35065745. PMID:11258383. Goldberger, A.L. 1996. Non-linear dynamics for clinicians: chaos theory, fractals, and complexity at the bedside. Lancet, 347(9011): 1312–1314. doi:10.1016/S01406736(96)90948-4. PMID:8622511. Goldberger, A.L., Amaral, L.A., Hausdorff, J.M., Ivanov, P., Peng, C.K., and Stanley, H.E. 2002. Fractal dynamics in physiology: alterations with disease and aging. Proc. Natl. Acad. Sci. U.S.A. 99(Suppl. 1): 2466–2472. doi:10.1073/ pnas.012579499. PMID:11875196. Goldsmith, R.L., Bloomfield, D.M., and Rosenwinkel, E.T. 2000. Exercise and autonomic function. Coron. Artery Dis. 11(2): 129–135. doi:10.1097/00019501200003000-00007. PMID:10758814. Hartikainen, J.E., Malik, M., Staunton, A., Poloniecki, J., and Camm, A.J. 1996. Distinction between arrhythmic and nonarrhythmic death after acute myocardial infarction based on heart rate variability, signal-averaged electrocardiogram, ventricular arrhythmias and left ventricular ejection fraction. J. Am. Coll. Cardiol. 28(2): 296–304. doi:10.1016/0735-1097(96)00169-6. PMID: 8800101. Hautala, A.J., Makikallio, T.H., Seppanen, T., Huikuri, H.V., and Tulppo, M.P. 2003. Short-term correlation properties of R–R interval dynamics at different exercise intensity levels. Clin. Physiol. Funct. Imaging, 23(4): 215–223. doi:10. 1046/j.1475-097X.2003.00499.x. PMID:12914561. Hirsch, J.A., and Bishop, B. 1981. Respiratory sinus arrhythmia in humans: how breathing pattern modulates heart rate. Am. J. Physiol. 241(4): H620–H629. PMID:7315987. Horner, N.K., Lampe, J.W., Patterson, R.E., Neuhouser, M.L., Beresford, S.A., and Prentice, R.L. 2001. Indirect calorimetry protocol development for measuring resting metabolic rate as a component of total energy expenditure in freeliving postmenopausal women. J. Nutr. 131(8): 2215–2218. PMID:11481420. Huikuri, H.V., Jokinen, V., Syvanne, M., Nieminen, M.S., Airaksinen, K.E., Ikaheimo, M.J., et al. 1999. Heart rate variability and progression of coronary atherosclerosis. Arterioscler. Thromb. Vasc. Biol. 19(8): 1979–1985. doi:10.1161/ 01.ATV.19.8.1979. PMID:10446081. Javorka, M., Zila, I., Balharek, T., and Javorka, K. 2002. Heart rate recovery after exercise: relations to heart rate variability and complexity. Braz. J. Med. Biol. Res. 35(8): 991–1000. doi:10.1590/S0100-879X2002000800018. PMID:12185393. Kraskov, A., Stogbauer, H., Andrzejak, R.G., and Grassberger, P. 2005. Hierarchical clustering using mutual information. Europhys. Lett. 70(2): 278–284. doi: 10.1209/epl/i2004-10483-y. Kuznetsova, O.V., and Son'kin, V.D. 2005. Spectral analysis of the heart rate, blood pressure, and respiratory rate variability in 8- to 11-year-old children at rest. Fiziol. Cheloveka, 31(1): 33–39. La Rovere, M.T., Bigger, J.T., Jr., Marcus, F.I., Mortara, A., and Schwartz, P.J. 1998. Baroreflex sensitivity and heart-rate variability in prediction of total cardiac mortality after myocardial infarction. ATRAMI (Autonomic Tone and Reflexes After Myocardial Infarction) investigators. Lancet, 351(9101): 478–484. doi:10.1016/S0140-6736(97)11144-8. PMID:9482439. La Rovere, M.T., Pinna, G.D., Maestri, R., Mortara, A., Capomolla, S., Febo, O., et al. 2003. Short-term heart rate variability strongly predicts sudden cardiac death in chronic heart failure patients. Circulation, 107(4): 565–570. doi:10. 1161/01.CIR.0000047275.25795.17. PMID:12566367. Lewis, M.J., Kingsley, M., Short, A.L., and Simpson, K. 2007. Rate of reduction of heart rate variability during exercise as an index of physical work capacity. Scand. J. Med. Sci. Sports, 17(6): 696–702. doi:10.1111/j.1600-0838.2006.00616.x. PMID:17346290. Lin, M.T., Wang, J.K., Lu, F.L., Wu, E.T., Yeh, S.J., Lee, W.L., et al. 2006. Heart rate variability monitoring in the detection of central nervous system complica-

1135

tions in children with enterovirus infection. J. Crit. Care, 21(3): 280–286. doi:10.1016/j.jcrc.2006.02.005. PMID:16990099. Marwan, N., Carmen Romano, M., Thiel, M., and Kurths, J. 2007. Recurrence plots for the analysis of complex systems. Physics Reports, 438(56): 237. doi:10.1016/j.physrep.2006.11.001. Mendonca, G.V., Fernhall, B., Heffernan, K.S., and Pereira, F.D. 2009. Spectral methods of heart rate variability analysis during dynamic exercise. Clin. Auton. Res. 19(4): 237–245. doi:10.1007/s10286-009-0018-1. PMID:19479301. Nolan, J., Batin, P.D., Andrews, R., Lindsay, S.J., Brooksby, P., Mullen, M., et al. 1998. Prospective study of heart rate variability and mortality in chronic heart failure: results of the United Kingdom Heart Failure Evaluation and Assessment of Risk Trial (UK-HEART). Circulation, 98(15): 1510–1516. doi:10. 1161/01.CIR.98.15.1510. PMID:9769304. Pagkalos, M., Koutlianos, N., Kouidi, E., Pagkalos, E., Mandroukas, K., and Deligiannis, A. 2008. Heart rate variability modifications following exercise training in type 2 diabetic patients with definite cardiac autonomic neuropathy. Br. J. Sports Med. 42(1): 47–54. doi:10.1136/bjsm.2007.035303. PMID: 17526623. Papaioannou, V.E., Chouvarda, I., Maglaveras, N., Dragoumanis, C., and Pneumatikos, I. 2011a. Changes of heart and respiratory rate dynamics during weaning from mechanical ventilation: a study of physiologic complexity in surgical critically ill patients. J. Crit. Care, 26(3): 262–272. doi:10.1016/j.jcrc. 2010.07.010. PMID:20869842. Papaioannou, V.E., Chouvarda, I.G., Maglaveras, N.K., and Pneumatikos, I.A. 2011b. Study of multiparameter respiratory pattern complexity in surgical critically ill patients during weaning trials. BMC Physiol. 11: 2. doi:10.1186/ 1472-6793-11-2. PMID:21255420. Perini, R., Orizio, C., Baselli, G., Cerutti, S., and Veicsteinas, A. 1990. The influence of exercise intensity on the power spectrum of heart rate variability. Eur. J. Appl. Physiol. Occup. Physiol. 61(1–2): 143–148. doi:10.1007/BF00236709. PMID:2289492. Perkiomaki, J.S., Hamekoski, S., Junttila, M.J., Jokinen, V., Tapanainen, J., and Huikuri, H.V. 2010. Predictors of long-term risk for heart failure hospitalization after acute myocardial infarction. Ann. Noninvasive Electrocardiol. 15(3): 250–258. doi:10.1111/j.1542-474X.2010.00372.x. PMID:20645968. Pomeranz, B., Macaulay, R.J., Caudill, M.A., Kutz, I., Adam, D., Gordon, D., et al. 1985. Assessment of autonomic function in humans by heart rate spectral analysis. Am. J. Physiol. 248(1): H151–H153. PMID:3970172. Pounds, S., and Cheng, C. 2006. Robust estimation of the false discovery rate. Bioinformatics, 22(16): 1979–1987. doi:10.1093/bioinformatics/btl328. PMID: 16777905. Pumprla, J., Howorka, K., Groves, D., Chester, M., and Nolan, J. 2002. Functional assessment of heart rate variability: physiological basis and practical applications. Int. J. Cardiol. 84(1): 1–14. doi:10.1016/S0167-5273(02)00057-8. PMID: 12104056. Ryan, M.L., Ogilvie, M.P., Pereira, B.M., Gomez-Rodriguez, J.C., Manning, R.J., Vargas, P.A., et al. 2011. Heart rate variability is an independent predictor of morbidity and mortality in hemodynamically stable trauma patients. J. Trauma, 70(6): 1371–1380. doi:10.1097/TA.0b013e31821858e6. PMID:21817974. Sandercock, G.R., and Brodie, D.A. 2006. The use of heart rate variability measures to assess autonomic control during exercise. Scand. J. Med. Sci. Sports, 16(5): 302–313. doi:10.1111/j.1600-0838.2006.00556.x. PMID:16774653. Saul, J.P. 1990. Beat-to-beat variations of heart rate reflect modulation of cardiac autonomic outflow. Physiology, 5(1): 32–37. Schafer, C., Rosenblum, M.G., Kurths, J., and Abel, H.H. 1998. Heartbeat synchronized with ventilation. Nature, 392(6673): 239–240. doi:10.1038/32567. PMID: 9521318. Seely, A.J., and Macklem, P.T. 2004. Complex systems and the technology of variability analysis. Crit. Care, 8(6): R367–R384. doi:10.1186/cc2948. PMID: 15566580. Seely, A.J., and Macklem, P. 2012. Fractal variability: an emergent property of complex dissipative systems. Chaos, 22(1): 013108. doi:10.1063/1.3675622. PMID:22462984. Shafqat, K., Pal, S.K., Kumari, S., and Kyriacou, P.A. 2011. HRV analysis in local anesthesia using continuous wavelet transform (CWT). Conf. Proc. IEEE Eng. Med. Biol. Soc. 2011: 4808–4811. doi:10.1109/IEMBS.2011.6091191. PMID: 22255414. Shin, K., Minamitani, H., Onishi, S., Yamazaki, H., and Lee, M. 1995a. The power spectral analysis of heart rate variability in athletes during dynamic exercise — Part I. Clin. Cardiol. 18(10): 583–586. doi:10.1002/clc.4960181011. PMID:8785905. Shin, K., Minamitani, H., Onishi, S., Yamazaki, H., and Lee, M. 1995b. The power spectral analysis of heart rate variability in athletes during dynamic exercise — Part II. Clin. Cardiol. 18(11): 664–668. doi:10.1002/clc.4960181114. PMID:8590537. Stein, P.K., Domitrovich, P.P., Huikuri, H.V., and Kleiger, R.E. 2005. Traditional and nonlinear heart rate variability are each independently associated with mortality after myocardial infarction. J. Cardiovasc. Electrophysiol. 16(1): 13– 20. doi:10.1046/j.1540-8167.2005.04358.x. PMID:15673380. Svedahl, K., and MacIntosh, B.R. 2003. Anaerobic threshold: the concept and methods of measurement. Can. J. Appl. Physiol. 28(2): 299–323. doi:10.1139/ h03-023. PMID:12825337. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. 1996. Heart rate variability. StanPublished by NRC Research Press

1136

Warren, J.H., Jaffe, R.S., Wraa, C.E., and Stebbins, C.L. 1997. Effect of autonomic blockade on power spectrum of heart rate variability during exercise. Am. J. Physiol. 273(2): R495–R502. PMID:9277531. Wei, W.W.S. 2006. Time series analysis: univariate and multivariate methods. 2nd ed. Pearson Education. Weir, J.B. 1949. New methods for calculating metabolic rate with special reference to protein metabolism. J. Physiol. 109(1–2): 1–9. PMID:15394301.


dards of measurement, physiological interpretation, and clinical use. Eur. Heart J. 17(3): 354–381. PMID:8737210. Vatner, S.F., and Pagani, M. 1976. Cardiovascular adjustments to exercise: hemodynamics and mechanisms. Prog. Cardiovasc. Dis. 19(2): 91–108. doi:10.1016/ 0033-0620(76)90018-9. PMID:790460. Velcheva, I., Damianov, P., Mantarova, S., and Antonova, N. 2011. Hemorheology and heart rate variability in patients with diabetes mellitus type 2. Clin. Hemorheol. Microcirc. 49(1–4): 513–518. doi:10.3233/CH-2011-1500. PMID: 22214722.


Published by NRC Research Press