In the last decades, a growing interest has been focused on sleep disorders, mainly due to their epidemiological and clinical relevance and to their possible association with cardiovascular diseases. The International Classification of Sleep Disorders classified sleep disorders into several categories, such as (1) insomnias, (2) SDB, (3) hypersomnias, (4) parasomnias, and (5) sleep related movement disorders.

It is worth noting that most sleep disorders are characterized by important modifications of physiological sleep, and often by signs and symptoms of sleep loss, which have been demonstrated to be independent risk factors for cardiovascular morbidity and mortality (Wingard et al., 1982; Gallicchio and Kalesan, 2009; Cappuccio et al., 2010; Redline and Foody, 2011). It has been suggested that autonomic cardiovascular control could be importantly implicated as a potential physiopathological link between sleep disorders and their physiological consequences.

This section will briefly explore HRV changes in three pathological conditions, i.e., SDB, Insomnia, and Sudden Unexpected Death in Epilepsy, which are characterized by noteworthy alterations of cardiovascular autonomic regulation and increased cardiovascular risk.

#### **HRV IN SLEEP DISORDERED BREATHING**

SDB is a group of diseases characterized by significant alterations of breathing during sleep, which cause fragmentation of physiological sleep, symptoms of sleep deprivation and altered gas exchanges during the night. The most common SDB is Obstructive Sleep Apnea (OSA), which has a prevalence of 2–4% in middle age population.

OSA is associated with repetitive episodes of apneas during sleep, with partial or complete collapse of the upper airway during inspiration; thus, the increased resistance in the upper airways, caused by the collapse of the pharynx and hypopharynx muscles, lead to paradoxical thoracic and abdominal movements in order to overcome the airway obstruction. The frequent episodes of apneas have three main effects: hypoxia and hypercapnia due to gas exchange alterations, sleep fragmentation with repetitive arousal and, finally, irritability, daytime sleepiness and altered cognitive performance.

The clinical relevance of OSA is related to its strong association with obesity, hypertension, and increased cardiovascular risk (Kales et al., 1984; Bliwise et al., 1988; Hung et al., 1990; Somers et al., 1995; Narkiewicz and Somers, 2001). Although the pathophysiological factors linking OSA and cardiovascular risk are not completely understood, several evidences support the hypothesis that sleep fragmentation and intermittent hypoxia cause a chronic hyperactivation of the sympathetic nervous system, a key component of the progression to cardiovascular disease. In addition, endothelial dysfunction and activation of inflammatory cascade have been described in OSA patients, possibly mediated by the activation of the SNS.

OSA must be suspected in patients with snoring, obesity, resistant hypertension and in patients with signs and/or symptoms of sleep loss (fatigue, hypersomnolence, daytime sleepiness, cognitive impairment etc.). The diagnosis of OSA is confirmed in the presence of apneas (defined as a stop in airflow of at least 10 s) and hypopneas (reduction of 50% of the flow, with an oxygen desaturation of *>*4% and lasting at least 10 s). The number of apneic events is calculated as the apnea/hypopnea index (AHI), i.e., the total amount of apneas and hypopneas per hour of sleep. An AHI lower than five identifies a normal subjects, an AHI between 5 and 15 identifies mild OSA, an AHI between 15 and 30 a moderate OSA and an AHI greater than 30 a severe OSA (Somers et al., 1995).

The repetitive episodes of apneas have important hemodynamic and cardiovascular consequences, which result evident both during nighttime and daytime. Subjects with OSA usually have higher resting HR and BP (Vanninen et al., 1996). The autonomic consequences of OSA mainly involve chemoreflex and baroreflex regulation, with a global shift of the sympathovagal balance toward a sympathetic predominance and a blunted parasympathetic control (Narkiewicz et al., 1998), evident either during wake, and during night. Namely, during apneic events, the physiological inhibition of sympathetic activity by lung inflation is lacking, causing a significant sympathetic activation which is responsible for increased in BP and changes in HR. Sympathetic activity is highest at the end of an apnea, when hypoxia and hypercapnia reach their maximum levels; after the upper airways re-opening, it is possible to observe a large raise in blood pressure, which activates baroreflex control and induces a temporary withdrawal of sympathetic overactivity (Narkiewicz and Somers, 2001). The analysis of HRV revealed that during daytime, moderate to severe OSA patients were characterized by increased cardiac sympathetic modulation compared to mild-OSA and controls (Narkiewicz et al., 1998). Compared to controls, OSA subjects are characterized by a lower total variability and a possible shift of the sympatho-vagal balance toward a sympathetic predominance and a vagal withdrawal, as shown by the increase of LF component and LF/HF and the decrease of HF component (Narkiewicz and Somers, 2003; Smietanowski et al., 2006; Kesek et al., 2009) either during wakefulness and during nighttime (Shiomi et al., 1996; Vanninen et al., 1996).

It is important to underline a limitation of HRV application to sleep studies in OSA at this point. Indeed, the analysis of HRV during sleep is limited just by the presence of repetitive apneas, leg movements, or arousals, which artificially modify HRV analysis, introducing a "rhythmic" biological noise able to alter autonomic cardiovascular oscillations. This problem was highlighted by few studies that found that a higher AHI was associated with a higher vagal modulation during NREM sleep (da Silva et al., 2009) and, in contrast to the general expectations, in severe OSA patients, a decreased sympathetic regulation during REM sleep was observed (Gula et al., 2003). In fact, severe OSA induced an important modification in breathing pattern, which could have *per se* been a relevant confounding factor able to impinge upon HRV rhythmical oscillations. For this reason, cardiovascular autonomic assessment in severe OSA patients can importantly be affected by non-neural oscillations related to the continuous episodes of apnea which could thereby alter HRV analysis (Wang et al., 2008); this factor must be taken into account when analyzing PSG data and interpreting the results. To this regard, in a recent paper assessing HRV during sleep in patients with Brugada syndrome diagnosed with or without SDB, ECG recordings derived from polysomnographic studies were analyzed, carefully avoiding periods with apneas/hypopneas and considering only ECG segments associated with stable and regular breathing (Tobaldini et al., 2013). This approach allowed the observation that Brugada syndrome, a rare but life- threatening disease characterized by ventricular arrhythmias and sudden cardiac death, more frequent during nighttime, was associated with an impaired autonomic cardiovascular control in the presence of comorbid SDB (Tobaldini et al., 2013).

The gold standard therapy for OSA is the application of continuous positive airway pressure (CPAP), a device able to maintain open the upper airways during sleep by inflating a positive pressure airflow, preventing the repetitive airway collapses as previously mentioned (Patel et al., 2003). CPAP therapy is able to improve cardiovascular outcome (Marin et al., 2005), with a significant reduction of arterial pressure (Faccenda et al., 2001), inflammatory markers, insulin resistance (Brooks et al., 1994), and coagulation factors (Phillips et al., 2012). As to autonomic effects of CPAP, a pioneer study by Somers and colleagues showed that CPAP is able to acutely affect ANS, with a significant decrease of MSNA during wake and sleep (Narkiewicz et al., 1999). Interestingly, even one night of CPAP treatment was able to affect HRV with a reduction of sympathetic modulation and an improvement of baroreflex control (Bonsignore et al., 2006; Kufoy et al., 2012). Longer CPAP treatments revealed positive effects on hemodynamic and metabolic variables, such as an improvement of arterial stiffness, a reduction of inflammatory response (Arias et al., 2008; Dorkova et al., 2008) and a decrease of platelet aggregation (Shimizu et al., 2002). However, the effects of longer CPAP treatments showed contrasting results and conclusive results on its consequences on HRV are still lacking. For instance, a reduction of the LF component and the LF/HF ratio, have been described, likely to be related to an improvement of chemoreflex and baroreflex responses (Roche et al., 1999; Khoo et al., 2001). Very recently, it has been described that long term CPAP (2 years treatment) is able to improve the coupling between parasympathetic modulation and delta wave sleep (Jurysta et al., 2013), suggesting a positive effect of this therapy on central and peripheral oscillations.

#### **HRV IN INSOMNIA**

Insomnia is a sleep disorder characterized by an important inability to fall asleep, to stay asleep or to wake up too early, causing daytime sleepiness, fatigue, mood alterations, and memory impairment. Insomnia is one of the most common sleep disorders and it can be classified into (1) comorbid insomnia, i.e., associated with other diseases, either physical and mental, (2) primary insomnia (PI), i.e., unrelated to any other disease, and (3) chronic (or long-standing) insomnia.

Apart from the relevant effects on day-life activity, the importance of insomnia as a potential modifiable risk factor for the development of cardiovascular diseases has been recently underlined (Spiegelhalder et al., 2010; Redline and Foody, 2011).

The assessment of ANS control in insomniac patients revealed interesting results. A pioneer study by Bonnet and colleagues showed a significant increase of the LF component and a decrease of the HF component of HRV in insomniacs compared to healthy subjects during sleep; these data suggested for the first time that insomnia is characterized by a predominant sympathetic modulation not only during wake but also across sleep stages (Bonnet and Arand, 1998).

These results have been confirmed by successive studies, which showed that PI patients exhibit a constant sympathetic overactivity during night (de Zambotti et al., 2013) and a marked reduction of vagal modulation, indicated by the decrease of the HF component of HRV during the night (Spiegelhalder et al., 2011; Yang et al., 2011).

These data support the hypothesis that in PI patients, sympatho-vagal balance is shifted toward a sympathetic predominance. Furthermore, the analysis of complexity indexes using entropy derived measures showed a considerable decrease in complexity during nighttime compared to healthy subjects, thus suggesting a possible link with cardiovascular diseases (Yang et al., 2011).

It is worth noting that PI patients do not only have an impaired autonomic cardiac modulation, but also an altered coupling between HRV and delta sleep. In fact, analyzing the relationship between the HF component and EEG delta power, PI patients showed a decreased HF-delta EEG coherence with respect to controls, thus suggesting a significant change in the interaction between central and peripheral drives (Jurysta et al., 2009).

In summary, insomnia is a very common sleep disorder, relevant for its prevalence over general population and for its clinical consequences. An altered autonomic cardiovascular control has been described in insomniac patients, who show a shift of the sympatho-vagal balance toward a predominance of sympathetic modulation both during wake and night; this alteration could be responsible for increased risk of cardiovascular diseases.

### **HRV, SLEEP, AND SUDDEN UNEXPECTED DEATH IN EPILEPSY (SUDEP)**

Epilepsy is a brain disorder characterized by an enduring predisposition to generate seizures. Seizures are paroxysmal transient disturbances of brain functions that may be manifested as episodic impairment or loss of consciousness, abnormal motor phenomena and psychic or sensory disturbances. Moreover, seizures can induce a perturbation of the ANS; indeed, neurovegetative symptoms such as cardiovascular and respiratory changes, gastro-intestinal, cutaneous, and genito-urinary manifestations, frequently occur during epileptic seizures (Baumgartner et al., 2001; Leutmezer et al., 2003). Finally, it has been shown that also sub-clinical epileptic discharges may be associated with autonomic instability (Brotherstone and McLellan, 2012).

In epileptic patients, especially in those with pharmacoresistant epilepsy, the risk of sudden unexpected death (SUDEP) is 24–40 times higher with respect to the general population (Ficker et al., 1998; Mohanraj et al., 2006). SUDEP is considered to be the result of a peri-ictal concurrence of a number of predisposing and precipitating factors (Nobili et al., 2011); nevertheless SUDEP is primarily a sleep related phenomenon and sleep related seizures seem to be an independent risk factor for SUDEP (Lamberts et al., 2012). Previous studies have shown decreased HRV in chronic epileptic patients suggesting that this might play a role in the pathophysiology of SUDEP (Tomson et al., 1998; Ansakorpi et al., 2000; Ronkainen et al., 2005). There are many reports suggesting that the autonomic changes observed in epileptic patients are mainly evident during nocturnal sleep. Indeed, patients (both adult and children) with focal epilepsy exhibit a higher reduction of HRV during night-time with respect to control subjects, indicating that the sleep period in epileptic patients might be more at risk of developing alterations of autonomic heart control (Ferri et al., 2002; Ronkainen et al., 2005; Persson et al., 2007). In a recent study, conducted in a population of drug resistant epileptic children with different clinical syndromes, authors found a striking reduction in vagal modulation during slow-wave sleep and a small autonomic modulation capacity (Jansen et al., 2011).

There is no definitive interpretation whether the observed autonomic changes in epileptic patients are due to the recurrence of seizures, the interictal epileptic discharges, and/or to the drug treatment. Seizures and periodic epileptic discharges, increasing arousal fluctuations during NREM sleep, might lead to a chronic stimulation of the ANS which are reflected by changes in HRV parameters. The relevant role of seizures and epileptic discharges on HRV change seems to be confirmed by the observation that, in drug resistant patients, HRV improves after epilepsy surgery especially in those with a positive outcome (Hilz et al., 2002; Dütsch et al., 2004; Persson et al., 2005). Also antiepileptic drugs could influence the autonomic state; in particular carbamazepine and polytherapy seem to reduce HRV (Persson et al., 2003; Yildiz et al., 2011). A recent systematic review of case-control studies, enrolling patients with different epilepsy syndromes and from infancy to adulthood, confirmed that epileptic patients present lower HF values, indicating impaired vagal control associated with increased cardiovascular risk and arrhythmias (Lotufo et al., 2012). Moreover a trend

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for lower LF ratios was identified in epileptic patients using pharmacotherapy.

In conclusion, the reduction of HRV that has been found in patients with epilepsy seems to be more pronounced during the night, thus affecting the circadian HRV. Further studies are needed to assess the possible association between altered HRV and risk of sudden death during sleep in epileptic patients.

## **CONCLUSIONS**

In summary, sleep is a complex biological phenomenon regulated by different biological pathways. Cardiovascular autonomic control plays a key role, varying among the transition to different sleep stages. In addition, the sleep-autonomic link has to be considered bidirectional: in fact, autonomic changes can importantly alter sleep regulation and, on the other side, sleep disturbances can profoundly alter the physiological cardiac autonomic modulation. Nowadays, an increasing prevalence of sleep disorders such as SDB and neurological sleep related disturbances have been described. The assessment of autonomic cardiovascular control using classical linear and more recent non-linear analysis of HRV have been widely used as non-invasive tools to provide important information on autonomic changes in physiological and pathological sleep.

## **ACKNOWLEDGMENTS**

This work was partly supported by a European Regional Development Fund—Project FNUSA-ICRC (No. CZ.1.05/1.1.00/02.0123) to Nicola Montano.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 09 July 2013; accepted: 26 September 2013; published online: 16 October 2013.*

*Citation: Tobaldini E, Nobili L, Strada S, Casali KR, Braghiroli A and Montano N (2013) Heart rate variability in normal and pathological sleep. Front. Physiol. 4:294. doi: 10.3389/fphys.2013.00294*

*This article was submitted to Cardiac Electrophysiology, a section of the journal Frontiers in Physiology.*

*Copyright © 2013 Tobaldini, Nobili, Strada, Casali, Braghiroli and Montano. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## Do physiological and pathological stresses produce different changes in heart rate variability?

## *Andrea Bravi 1,2\*, Geoffrey Green3, Christophe Herry2, Heather E. Wright 4, André Longtin5, Glen P. Kenny4 and Andrew J. E. Seely1,2,3,6*

#### *Edited by:*

*Karin Trimmel, Medical University of Vienna, Austria*

#### *Reviewed by:*

*Milan Stengl, Charles University, Czech Republic Edward Lakatta, National Institutes of Health, USA*

#### *\*Correspondence:*

*Andrea Bravi, Dynamical Analysis Laboratory, 501 Smyth Road, Room 6371, Ottawa, ON K1H 8L6, Canada e-mail<EMAIL_ADDRESS>
Although physiological (e.g., exercise) and pathological (e.g., infection) stress affecting the cardiovascular system have both been documented to be associated with a reduction in overall heart rate variability (HRV), it remains unclear if loss of HRV is ubiquitously similar across different domains of variability analysis or if distinct patterns of altered HRV exist depending on the stressor. Using Continuous Individualized Multiorgan Variability Analysis (CIMVA™) software, heart rate (HR) and four selected measures of variability were measured over time (windowed analysis) from two datasets, a set (*n* = 13) of patients who developed systemic infection (i.e., sepsis) after bone marrow transplant (BMT), and a matched set of healthy subjects undergoing physical exercise under controlled conditions. HR and the four HRV measures showed similar trends in both sepsis and exercise. The comparison through Wilcoxon sign-rank test of the levels of variability at baseline and during the stress (i.e., exercise or after days of sepsis development) showed similar changes, except for LF/HF, ratio of power at low (LF) and high (HF) frequencies (associated with sympathovagal modulation), which was affected by exercise but did not show any change during sepsis. Furthermore, HRV measures during sepsis showed a lower level of correlation with each other, as compared to HRV during exercise. In conclusion, this exploratory study highlights similar responses during both exercise and infection, with differences in terms of correlation and inter-subject fluctuations, whose physiologic significance merits further investigation.

**Keywords: dimensions of variability, domains of variability, exercise, physical activity, disease, sepsis**

## **INTRODUCTION**

Several studies have shown that heart rate variability (HRV) can be used to characterize physiological (e.g., physical exercise, heat and cold stress), as well as pathological stress affecting the cardiovascular system. The utility of HRV in identifying illness states has been discussed in multiple reviews, and specific applications include the classification of heart failure, the prediction of mortality after myocardial infarction, and the estimation of autonomic modulation (Task Force, 1996; Seely and Macklem, 2004; Huikuri et al., 2009; Bravi et al., 2011). Similarly for physiological stress, measurement of HRV during physical exercise has been shown to estimate autonomic modulation, assess the level of fitness, characterize the beneficial effects of physical exercise, and many other applications (Perini and Veicsteinas, 2003; Lewis and Short, 2010; Routledge et al., 2010).

One of the major findings extracted from this collection of results is that when the cardiovascular system is under stress, either physiological or pathological, there is a decrease in variability. When pathological stresses are considered, a widespread view is to interpret this loss as a decomplexification of the cardiac system due to illness (Goldberger, 1996; Varela et al., 2010). In the domain of physiological exercise, however, a clear explanation for this phenomenon is lacking (Lewis and Short, 2010), nor is there a theory interpreting these two phenomena together. Developing a theoretical framework in this area of investigation might generate critical knowledge to understand the boundaries between health and disease, and allow knowledge translation from one domain to the other.

The first step to develop this framework consists of an extended comparison of how examples of pathological and physiological stresses affect HRV. With this in mind, the overall aim of this pilot investigation was to initiate, for the first time, a focused comparison of the patterns of change in HRV from a physiologic (i.e., exercise) and a pathologic (i.e., sepsis) stressor. The underlying hypothesis was that physiological and pathological stresses produce different effects on the body, and therefore their effects on HRV should differ. To test this hypothesis, we studied the changes in mean heart rate (HR), as well as four measures of variability belonging to different domains (Bravi et al., 2011), and associated to distinct physiological dimensions (Barrera-Ramirez et al., 2013). Each domain of variability highlights the mathematical nature of a measure of variability, while the physiological dimensions are supposed to provide a physiological rationale to the changes in variability. Therefore, by investigating those four measures, we can shed light on the values of the two classification systems. In particular, we evaluated the trends (variation over time) of those measures from two datasets, one describing healthy subjects undergoing physical exercise under dry/warm conditions, the other ambulatory patients who developed sepsis after bone marrow transplant (BMT). Through analysis of the changes between baseline variability and variability during stress (i.e., either exercise or sepsis), as well as the analysis of the correlation between the measures, we highlighted similarities and differences between the two types of stressors.

## **MATERIALS AND METHODS SUBJECTS**

This study focuses on two datasets: (1) R-R interval (time between two successive R peaks during normal sinus rhythm) monitoring of 13 patients who developed systemic infection (i.e., sepsis) after BMT, and (2) R-R interval monitoring of 13 matched (by gender, age, weight, and height) healthy subjects undergoing controlled physical exercise under heat exposure, as described in more detail below. **Table 1** summarizes the subject characteristics.

## **SEPSIS**

The first dataset (SEPSIS) consisted of patients undergoing BMT for hematological malignancy or other disorders. Inclusion criteria were treatment with myeloablative chemoradiotherapy followed by an allogeneic or autologous BMT. Exclusion criteria were pre-existing cardiopulmonary disease, taking betablockers or calcium-channel blockers, pre-existing arrhythmia (e.g., atrial fibrillation, atrial bigeminy), and contraindication to electrocardiogram adhesives (e.g., allergy, severe psoriasis). Sepsis was defined as the systemic inflammatory response syndrome along with a clinically suspected infection requiring treatment.

*CI, Confidence Interval; \* Wilcoxon rank-sum test was used to compare the medians,* χ*<sup>2</sup> test was used to compare the proportions.*

Over 50% of the patients were diagnosed with sepsis based on the presence of fever, defined a priori as one recording greater than 38.5◦C or two recordings greater than 38.0◦C within 12 h. The remaining diagnoses were based on clinical suspicion, bacteremia, productive cough, and mucositis. For further details refer to (Ahmad et al., 2009; Bravi et al., 2012). The dataset consisted of 17 subjects: 3 who did not develop sepsis and showed an increase in HRV, 13 who developed sepsis and showed a reduction in HRV, and one insulin dependent diabetic subject who developed sepsis but showed an increase in HRV during the development of sepsis. To enable the comparison with the reduction in HRV during exercise, in this study we focused only on the 13 subjects who developed sepsis and showed a reduction in HRV. The average length of a recording was 12 days. A Zymed DigiTrak-Plus (Philips Healthcare, Canada) Holter system was used to record the R-R interval time series of each subject. Written informed consent was obtained from all participants, and the Ottawa Hospital Research Ethics Board authorized the study.

## **EXERCISE**

The second dataset (EXERCISE) included healthy normally active (i.e., untrained but not sedentary) volunteers performing intermittent exercise in warm/dry conditions. We selected 13 subjects from a pool of 73, to match the septic patients. The matching was done prior to any data analysis. The experimental protocol was approved by the University of Ottawa Health Sciences and Science Research Ethics Board. Prior to the experimental session, participants were asked to complete a Physical Activity Readiness Questionnaire (PAR-Q) to assess their eligibility to do physical activity. Maximal oxygen uptake (VO2max) was subsequently measured during a progressive cycle ergometer protocol which consisted of a 2-min warm-up at 40 W followed by 20 W increments every minute until the participant could no longer maintain a pedaling cadence of at least 60 rpm.

The experiments were performed at the same time of day. Participants were asked to arrive at the laboratory after eating a small breakfast and to refrain from consuming alcohol and caffeine for 24 h prior to experimentation and to avoid major thermal stimuli on their way to the laboratory. Participants were also encouraged to arrive well-hydrated as no fluid replacements were provided during the experiment. After 30 min of rest on a chair, participants performed four bouts of 15-min cycling (Corival recumbent cycle ergometer, Lode, Netherlands) at a constant rate of heat production (400 W) in an environmental chamber regulated at 35◦C and 20% relative humidity. Each exercise bout was separated by 15-min of rest on the recumbent cycle, with a final 60 min recovery (not reported in the following analyses). Therefore, the length of each recording was of 150 min (60 of exercise, 90 of rest).

Electrocardiographic waveforms for each subject were recorded through a Holter DigiTrak XT (Philips Medical Systems, USA) physiological monitor. For simplicity we will refer to this dataset as EXERCISE.

## **VARIABILITY COMPUTATION**

For both datasets, only the beats characterized as normal sinus rhythm were included, while all premature beats were excluded. The classification was automatically performed by the Holter monitors and through delta detector on both datasets (Clifford et al., 2002). Using Continuous Individualized Multiorgan Variability Analysis (CIMVA™) software (Bravi, 2013), HR and four measures of variability were extracted from the R-R interval time series of each subject through a windowed analysis, namely by using 5-min windows for both EXERCISE and SEPSIS. This means that five values were computed using 5 min of data, and successive values of those measures were computed by repeatedly shifting the 5-min window by 30 s for EXERCISE and 2.5 min for SEPSIS; these window steps were different because of the shorter length of the EXERCISE recordings compared to the SEPSIS recordings. This procedure created five variability time series per subject. Together with mean HR, the measures we investigated are standard deviation (statistical domain), the ratio between the power at low (LF) and high (HF) frequencies (LF/HF ratio—computed through the Lomb–Scargle periodogram, energetic domain), sample entropy (informational domain), and the Hurst exponent (computed through the Scaled windowed variance method, invariant domain). For details on the domains of variability, refer to Bravi et al. (2011).

## **STATISTICAL ANALYSIS**

Before any analysis, each of the time series for the patients in the SEPSIS dataset were aligned with respect to the time of administration of antibiotics, and the time series for the subjects in the EXERCISE dataset were aligned with respect to the start of the exercise routine. Following the alignment, three analyses were performed. (1) Qualitative inspection of the population trends of HR and the four HRV measures (reported as median and 95% confidence intervals around the median). The population trends were computed by taking at each time instant the median value across the different subjects, as show in **Figure 1**. This led to five population trends for each dataset. (2) Statistical evaluation of the change between variability at baseline, and variability during stress. First, the variability time series were segmented, as summarized in **Table 2**, and then for each subject the median variability over time was computed, creating a distribution of values representative of either baseline or stress, for both SEPSIS and EXERCISE. The Wilcoxon sign-rank test was used to evaluate

**Table 2 | Segmentation of the time series.**

the null hypothesis of zero change between baseline and stress. It is worth mentioning that what we defined as "variability at baseline" for the SEPSIS dataset is not really representative of baseline conditions, because likely some subjects were already developing sepsis 72 h before the administration of antibiotics. However, not all the subjects had more than 72 h of data prior the administration of antibiotics, therefore to include all of them we decided to use that time interval. (3) Spearman's non-linear correlation analysis across the five population trends of SEPSIS and EXERCISE during the time intervals representative of "stress," as specified in **Table 2**.

## **RESULTS**

The alignment of the variability time series of each subject, as well as the creation of the population trends of the mean HR

highlighted the areas we referred to as "baseline" and "stress."

*This table shows the time intervals that were used to compare the changes in HRV between baseline condition and stress. The variability time series were segmented as specified, and the median value in each time interval was computed. This procedure created two values (i.e., at baseline and during stress) for each subject and for each variability time series. Those intervals are reported in Figures 1, 2 as gray areas. \*Different exercise bouts did not produce significant changes in the results.*

are reported in **Figure 1**. The figure shows that the mean HR considerably increased during exercise, and slightly increased during sepsis. Because of the low inter-subject variability, the 95% confidence intervals around the population trends are tight. Similarly, **Figure 2** shows the population trends for standard deviation, LF/HF ratio, sample entropy and Hurst exponent. Standard deviation presented a clear population trend in both sepsis and exercise, both in terms of a decrease in time as well as a tightening of the confidence intervals. The same phenomenon was not observed in either sample entropy nor Hurst exponent, even though they showed like standard deviation, a high sensitivity to non-stationarity (i.e., spikes in the transition between exercise and rest, and vice versa). Lastly, the LF/HF ratio showed a slight reduction in only the EXERCISE population trend, supported by a considerable reduction of the 95% confidence intervals around the population trends.

**FIGURE 2 | Population trends in physiological dimensions of variability.** These eight panels show the population trends of four measures of variability, hypothetically linked to separate physiological dimensions. From the top, each row presents: standard deviation, LF/HF ratio (computed through the Lomb–Scargle periodogram), sample entropy, and Hurst exponent (computed through Scaled windowed variance). The columns depict the median trend across all the subjects (bold solid line) and its 95% confidence intervals (dashed line) for the EXERCISE dataset (left) and the SEPSIS dataset (right). In gray are highlighted the areas we referred to as "baseline" and "stress."

To evaluate the changes between baseline and stress in a quantitative way, the values of the measures (median and 95% confidence intervals), together with results of the Wilcoxon sign-rank test are reported in **Table 3. Figure 3** provides a visual representation of the distributions of change from baseline to stress of **Table 3**. Mean HR and standard deviation confirmed the changes observed from a visual inspection, rejecting the null hypothesis of no change in the transition from baseline to stress for both EXERCISE (*p* = 0.0002) and SEPSIS (*p* = 0.003). The LF/HF ratio rejected the null hypothesis only in EXERCISE (*p* = 0.04). Hurst exponent and sample entropy were not able to reject the null hypothesis in neither EXERCISE nor SEPSIS.

The final analysis compared the degree of correlation between the population trends at the time of stress. The results are summarized in **Table 4**. Mean HR and standard deviation showed a good correlation (−0.68) during exercise, but lower correlation during sepsis (−0.40). Similarly, the levels of correlation between mean HR, standard deviation, LF/HF ratio and sample entropy resulted above |0.5|during exercise and below |0.5|during sepsis. The Hurst exponent showed low levels of correlation with all the measures in both sepsis and exercise, exception made for the correlation with standard deviation, which reached 0.51 during sepsis.

## **DISCUSSION**

The main objective of this study was to explore the differences between changes in variability due to exercise vs. sepsis; the underlying hypothesis being that the two types of stress produce differential impact on HRV measures.

Before interpreting the results, it is essential to highlight at a higher level what each measure of variability represents. It is well-established that despite the differences from a mathematical point of view, many (but not all) measures of variability are highly correlated with each other (Maestri et al., 2007), and are sensitive to different types of stressors affecting the cardiovascular system; this is compatible with our findings. Therefore, we believe that each measure of variability can be interpreted as a nonspecific sensor, which may be sensitive to multiple physiological mechanisms.

HRV demonstrated two main behaviors, possibly associated with two specific types of physiological phenomena: (1) sensitivity to exercise only (with "sensitivity" we intend the ability to show a change between baseline and stress—as shown in **Table 3**), or (2) to both sepsis and exercise. Although a loss of complexity might have been expected with sepsis development as opposed to exercise, this was not observed. Furthermore, we repeated the same analyses described in this article with the 11 HRV measures found to be relevant in tracking sepsis development in a previous study making use of the same SEPSIS dataset (Bravi et al., 2012), and we identified that all those measures changed significantly in both SEPSIS and EXERCISE (results not shown). This highlights that HRV responds similarly to both sepsis and exercise, although with differences, as exemplified by the particular behavior of the LF/HF ratio. This is similar to what was seen in a subsequent analysis on a set of other 17 measures of HRV, out of a pool of 96 measures (results not shown). Furthermore, when multiple measures of variability are compared through multivariate time series analysis (as done in **Table 4** through correlation analysis), further

#### **Table 3 | Statistical results.**

*This table shows the values for each measure after the segmentation specified in Table 2, together with the Change (Stress minus Baseline, within each subject). Since baseline and stress were computed from repeated measurements on the same subjects, the p-values were computed through Wilcoxon sing-rank test. In bold are reported significant rejections of the null hypothesis of zero change (*α*-value* = *0.05).*

#### **Table 4 | Spearman correlation matrix of the population trends in EXERCISE and SEPSIS, during stress.**

*This table shows the correlation values between the population time series in the two datasets, making use of only those samples representative of the stress phase (as specified in Table 2). The coefficients for SEPSIS are reported in bold, while the others are for EXERCISE.*

differences between sepsis and exercise arise. This result supports the idea that the understanding of the physiology underlying variability may require the integration of information from multiple HRV measures.

The similarities between sepsis and exercise could be explained based on physiology. Exercise-induced immune responses and inflammatory-related immune responses share similar physiological mechanisms, such as the release of cytokines and mediators, albeit with a different intensity (Shephard, 2001; Bente Klarlund, 2005). However, elevated levels of physical stress, such as those associated with performing exercise at very high intensity (=90% of VO2peak), produce a much lower immune response compared to the septic one (Shephard, 2001). Because we saw major changes during exercise rather than sepsis for both mean HR and standard deviation (**Table 3**), it is unlikely that immune response played a major role on the observed changes in HRV. Septic patients tend to show a normal or high cardiac output, due to an increased HR, a reduced afterload (due to the reduced vascular tone), and either increased (due to catecholamines) or reduced contractility (myocardial depression mediated by inflammatory cytokines) (Vincent, 2008). These similarities in the cardiovascular response could justify the similar behavior observed during sepsis and exercise, highlighting that major changes in HRV are likely driven by pure cardiovascular regulation.

On the other hand, the particular behavior of the LF/HF ratio is more difficult to explain, especially given the fact that this measure was documented to be sensitive to sepsis in multiple pilot studies, as specified in a recent review (Buchan et al., 2012). The key difference with the literature is that we did not compare septic patients with controls, but rather evaluated the change of LF/HF ratio during sepsis development. For instance, a study on 81 patients (Chen and Kuo, 2007) showed that septic patients (*n* = 21) who subsequently developed shock had lower LF/HF ratio respect to patients who did not develop sepsis (*n* = 60). In our study the LF/HF ratio showed a reduction during sepsis development (**Table 3**) which was not statistically significant. However, the three patients which we excluded from the analysis because did not develop sepsis showed instead an average LF/HF ratio of 3.3 (CI: 3.0, 3.6) during stress, showing therefore a larger average LF/HF ratio (see **Table 3**), consistent with the literature. Although the lack of control of posture imperils our ability to further interpret the LF/HF ratio, it is of interest that this measure exhibited different behavior during sepsis and exercise. For future reference, **Figure A1** in Appendix shows the details of LF and HF power (in normalized units) for both EXERCISE and SEPSIS, highlighting the major contribution of LF power in the LF/HF ratio population shown for EXERCISE (**Figure 2**).

An outcome of our analyses is that HRV measures taken from different domains of variability are likely to bring "unique" pieces of information, as shown by their low level of correlation (**Table 4**). The physiological interpretation of each measure in lights of the physiological dimensions of (Barrera-Ramirez et al., 2013) is instead more challenging. Standard deviation decreased during both sepsis and exercise, possibly confirming the hypothesis that it represents the level of cardiopulmonary reserve of the body (i.e., the lowest the measure, the more the body is toward its maximal capacity). LF/HF ratio, measure of sympathovagal modulation, was reduced during exercise, rather than increasing as shown in Barrera-Ramirez et al. (2013). Furthermore, despite the increase in HR, no significant change of LF/HF was detected during sepsis. Sample entropy, being a measure of complexity, was supposed to be primarily affected by a state of illness; despite the expectations, no change was observed during sepsis development. Lastly, the Hurst exponent has been linked to the capacity of the cardiorespiratory system to deliver oxygen and clear carbon dioxide. Given the increased demand of oxygen during exercise, we would have expected to see a decrease in the measure (*H* = 0.5 indicates no fractality), which however did not appear.

## **LIMITATIONS**

While a wider analysis using additional measures of variability is possible, we are cognizant of the small size of the patient populations, and wish not to over-analyze nor over-interpret the results. With a larger dataset in the future, it may be possible to better relate changes in HRV to the current theories about the nature of variability in physiological systems (Seely and Macklem, 2012; Barrera-Ramirez et al., 2013). It is worth noticing also the need to characterize the sensitivity of each measure of variability to non-stationarity. As shown in **Figure 2**, standard deviation, sample entropy and Hurst exponent showed significant sensitivity to the transitions between rest and exercise (and vice versa). This was not a problem for the data analysis, given we knew the acquisition protocol; however, if we imagine the monitoring of ambulatory patients, the necessity of distinguishing non-stationarity from clinically relevant changes in HRV becomes clear.

An inherent limitation to comparing these two cohorts is the lack of standardization of the severity of the stressor, particularly true for the subjects developing sepsis. Indeed, most of the subjects in that group where diagnosed based on fever and clinical impression, possibly introducing a confounding factor in the analysis. Further work on the definition of what "stress" is and how to quantify it would enable a better comparison of different physiologic and pathologic forms of stress. On the same line, the choice of the specific type of physiologic and pathologic stress is a limiting factor of the analyses, and the investigation of alternative protocols or pathologies would be of interest to validate the results. Another limitation is that, the patients in the two populations were not matched by fitness level (e.g., VO2max), because that information was unavailable for the septic population. Given the similar changes seen in univariate HRV in both populations, it is likely that the fitness level did not produce a bias in the analysis. A similar argument could be applied to other systematic differences between the two datasets, e.g., different definition of baseline state or degree of intensity of the stressor. Also, our results are based on a set of arbitrary choices determined *a priori*, e.g., chosen window size, measures of variability, their computational parameters, time segments for the definition of baseline and stress (i.e., **Table 2**), and use of the median to create the population trend. A larger dataset would enable a sensitivity analysis respect to those parameters. Despite these limitations, this work represents an initial step toward the definition of the differences between physiological and pathological stressors, and the understanding of the physiological mechanisms underlying HRV.

## **CONCLUSIONS**

Qualitative and quantitative comparisons of the changes in HRV during physiological and pathological stress were performed on two datasets, one from patients who developed sepsis after a BMT (pathological stress), the other from a matched group of healthy subjects performing physical exercise (physiological stress). The comparison showed that HRV measures behave similarly during both exercise and sepsis development, although with subtle differences, whose explanation remains fertile ground for further investigation.

## **AUTHOR CONTRIBUTIONS**

Andrea Bravi, Andrew J. E. Seely, and André Longtin conception and design of research; Andrea Bravi analyzed data; Andrea Bravi, Andrew J. E. Seely, André Longtin, Christophe Herry, Geoffrey Green, Glen P. Kenny interpreted results; Andrea Bravi, Andrew J. E. Seely, André Longtin, Christophe Herry, Geoffrey Green, Glen P. Kenny, Heather E. Wright edited and revised manuscript; Andrea Bravi, Andrew J. E. Seely, André Longtin, Christophe Herry, Geoffrey Green, Glen P. Kenny, Heather E. Wright approved final version of manuscript.

## **ACKNOWLEDGMENTS**

We gratefully acknowledge the support of the Ottawa Hospital Research Institute (OHRI), the Canadian Institutes of Health Research (CIHR), the Natural Sciences and Engineering Research Council (NSERC) and MITACS for this research program. The authors would like to acknowledge Joanie Larose for her role in the data collection of the EXERCISE dataset.

## **REFERENCES**

**Conflict of Interest Statement:** Andrew J. E. Seely is founder and Chief Science Officer of Therapeutic Monitoring Systems, Inc. (TMS), created to commercialize patented Continuous Individualized Multi-organ Variability Analysis (CIMVA) technology, with the objective of delivering variabilitydirected clinical decision support to improve quality and efficiency of care. Geoffrey Green is currently employed by TMS in the position of Product Manager. The other authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 23 May 2013; accepted: 10 July 2013; published online: 30 July 2013. Citation: Bravi A, Green G, Herry C, Wright HE, Longtin A, Kenny GP and Seely AJE (2013) Do physiological and pathological stresses produce different changes in heart rate variability? Front. Physiol. 4:197. doi: 10.3389/fphys. 2013.00197*

*This article was submitted to Frontiers in Cardiac Electrophysiology, a specialty of Frontiers in Physiology.*

*Copyright © 2013 Bravi, Green, Herry, Wright, Longtin, Kenny and Seely. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## **APPENDIX**

computed through the Lomb–Scargle periodogram. Each panel depicts the median trend across all the subjects (bold solid line) and its 95% confidence intervals (dashed line) for the EXERCISE dataset (left) and the SEPSIS dataset (right).

## Cardiac rehabilitation outcomes following a 6-week program of PCI and CABG Patients

*Herbert F. Jelinek1,2,3\*, Zhaoqi Q. Huang4,5, Ahsan H. Khandoker 2, Dennis Chang6 and Hosen Kiat 1,5*

*<sup>1</sup> Australian School of Advanced Medicine, Macquarie University, Sydney, NSW, Australia*

*<sup>2</sup> Department of Biomedical Engineering, Khalifa University of Science, Technology and Research, Abu Dhabi, United Arab Emirates*

*<sup>3</sup> School of Community Health, Charles Sturt University, Albury, NSW, Australia*

*<sup>4</sup> Department of Cardiology, The Third Affiliated Hospital of Guangzhou Medical College, Guangzhou, China*

*<sup>5</sup> Department of Cardiac Rehabilitation, Sydney Adventist Hospital, Sydney, NSW, Australia*

*<sup>6</sup> Centre for Complementary Medicine Research, University of Western Sydney, Sydney, NSW, Australia*

#### *Edited by:*

*Karin Trimmel, Medical University of Vienna, Austria*

#### *Reviewed by:*

*Peter Taggart, University College London, UK Alessandro Capucci, Universita' Politecnica delle Marche, Italy*

#### *\*Correspondence:*

*Herbert F. Jelinek, School of Community Health, Charles Sturt University, Albury, 2640, Australia e-mail<EMAIL_ADDRESS>
Coronary artery events requiring intervention are associated with depressed cardiac autonomic function. Whether a 6-week cardiac rehabilitation (CR) differs in effectiveness in improving exercise capacity (6MWT), cardiorespiratory function (peakVO2), and autonomic function (HRV) following either cardiac bypass surgery (CABG) or percutaneous coronary revascularization (PCI) is unknown. The current study therefore compared the change in 6MWT and peak VO2 to HRV variables following a 6-week CR program and with patients having either PCI or CABG. Thirty-eight patients, (PCI, *n* = 22 and CABG, *n* = 16) participated in the CR program and results for pre and post 6 min walk test (6MWT), peakVO2, and heart rate variability (HRV) were obtained. Our study has shown that a 6 weeks program following either PCI or CABG improves function. However, the effect on post-CABG differs to that of post-PCI patients. The change in distance walked (6MWT, metres) was higher in the CABG (-6MWT: 61, *p* < 0.001) compared to the PCI group (-6MWT: 41, *p* < 0.001). Maximum exercise capacity (peak VO2, ml/kg.min) also changed significantly with a greater change in the CABG group (-PCI: 0.7, *P* < 0.001; -CABG: 1.0, *P* < 0.001) but did not reach normal population values. Although an improvement in HRV parameters was noted for the PCI group, a statistically significant improvement in HRV was observed only in the CABG group for the following; SDNN (ms) (baseline vs. post-rehabilitation (median ± IQR): 31.2 ± 25.6 vs. 51.8 ± 23.1, *p* < 0.01), RMSSD (19.<sup>32</sup> <sup>±</sup> <sup>19</sup>.9 vs. 42 <sup>2</sup> .<sup>1</sup> <sup>±</sup> <sup>34</sup>.2, *<sup>p</sup>* <sup>&</sup>lt; <sup>0</sup>.01); LF (ms ) (191 <sup>±</sup> 216 vs. 631 <sup>±</sup> 693, *<sup>p</sup>* <sup>&</sup>lt; <sup>0</sup>.01) and HF (107 ± 201 vs. 449 ± 795.0, *p* < 0.05). A significant interaction in the PCI group but not in the CABG group was observed using correlation analysis between the 6MWT and peak VO2 with HRV parameters indicating that being healthier that is, a better 6MWT and peak VO2 led to better HRV results but no significant effect of CR in the PCI group. When the results were investigated for baseline 6MWT and peak VO2 effect using a covariate analysis, a significant influence of CR on HRV parameters was retained in the CABG group (*p* = 0.0072). Our study indicates that a 6-weeks CR program benefits both patient groups in terms of exercise capacity, cardiorespiratory function and autonomic nervous system modulation of heart rate, with CABG patients showing the most improvement. HRV can be a useful additional variable to gauge cardiac function following CR.

**Keywords: cardiac rehabilitation, exercise, percutaneous coronary angioplasty, coronary artery bypass drafting, heart rate variability**

## **INTRODUCTION**

Percutaneous coronary angioplasty intervention (PCI) and coronary artery bypass grafting (CABG) are effective and established treatments for clinically significant coronary artery disease (CAD) (Eagle et al., 2004; Bravata et al., 2007). The long-term survival benefits of cardiac rehabilitation (CR) among patients with CAD have been documented following several large-scale trials and meta-analyses with no direct comparisons between cardiac intervention (CABG vs. PCI) or current measures, including the 6-min walk test and peak oxygen volume (6MWT, peak VO2) (Giannuzzi et al., 2003; Taylor et al., 2004; Leon et al., 2005; Suaya et al., 2009). In recent papers there has been some speculation this may be due to an improvement in cardiac autonomic function (Lucini et al., 2002; Routledge et al., 2010).

The 6MWT is now used routinely to demonstrate the physical and physiological benefits of CR following coronary intervention with peak VO2 still used in major metropolitan hospitals (Fiorina et al., 2007). Recently Soumagne reported improvement in functional capacity following CR but did not report direct comparisons between PCI and CABG (Soumagne, 2012).

Heart rate variability (HRV) is a valid marker of cardiac autonomic activity and complexity that reflects sympathetic and parasympathetic balance and overall tone (Mäkikallio et al., 2006; Jelinek et al., 2010; McLachlan et al., 2010). HRV can be measured in both time and frequency domains, where global activity in the time domain is indicated by the standard deviation of the RR intervals (SDNN) and parasympathetic function by the root mean square of the standard deviation of the RR intervals (RMSSD) (TFESC, 1996). Sympathetic output in the frequency domain is loosely correlated with low frequency (LF) power, although a parasympathetic component has been noted, while parasympathetic/vagal output is in part correlated with high frequency (HF) power of the HRV spectrum (Grassi et al., 2009). The LF/HF ratio is a derived value from the calculated HF and LF spectral component (Reed et al., 2005; Billman, 2013). Reduced HRV may be associated with abnormal adaptability of the cardiac autonomic nervous system to changes in cardiac pathology and may increase risk of adverse or fatal cardiac events (Kleiger et al., 1987; Quintana et al., 1997; Weber et al., 1999; Jelinek et al., 2010).

Parasympathetic indices are reduced in patients within 24 h after PCI (Tseng et al., 1996; Osterhues et al., 1998; Wennerblom et al., 2000; Kanadasi et al., 2002). However, this reduction in parasympathetic function appears to be a transient phenomenon (Osterhues et al., 1998; Kanadasi et al., 2002; Janowska-Kulinska ´ et al., 2009). In patients with greater than one target-vessel affected and/or with other comorbidities, HRV remains lower due to several factors (Tseng et al., 1996; Birand et al., 1998; Wennerblom et al., 2000; Kanadasi et al., 2002). In advanced heart failure with low left ventricular ejection fraction autonomic dysfunction may be due to a decrease in muscarinic receptor density, or changes in neuro-hormonal output leading to a decrease in parasympathetic output and an increase in sympathetic activity (Eckberg et al., 1971). In further studies, HRV has been shown to be independent of ejection fraction, extent of CAD and other variables, where a decreased HRV is a potent independent predictor of mortality in the 12 months following elective coronary angiography in patients without recent myocardial infarction (Rich et al., 1988). A number of studies have found that CR can improve both HRV and exercise capacity in patients following PCI (Chien et al., 2006; Munk et al., 2009; Baumert et al., 2011).

In general, outcomes for post-CABG reported in earlier studies differ to PCI in that impairment of cardiac autonomic regulation assessed by HRV remains suboptimal for several months or years post CABG and an increased risk of atrial fibrillation remains present (Demirel et al., 2002; Bauernschmitt et al., 2004; Cygankiewicz et al., 2004; Wu et al., 2005; Laitio et al., 2006; Kalisnik et al., 2007). Accordingly, strategies resulting in favorable recovery of cardiac autonomic tone as soon as possible after CABG may be clinically important in these patients. Longterm outpatient exercise-based CR has been reported to positively improve exercise capacity and cardiac autonomic function post-CABG hospital discharge in programs of 2 months duration or longer (Hirschhorn et al., 2008; Baumert et al., 2011). However, data evaluating the impact of CR on HRV in outpatients after CABG are limited (Iellamo et al., 2000; Baumert et al., 2011). It is uncertain whether short-term CR applied post-CABG during an outpatient CR program has a substantial beneficial impact on HRV and how this compares to current measures of exercise capacity (6MWT) and cardiorespiratory function (peak VO2). No study has directly compared the physiological parameters measured as 6MWT and peak VO2 and cardiac autonomic function improvements for CABG and PCI patients following a 6-week CR program.

The current study prospectively compared the impact of a 6 weeks CR program on baseline HRV parameters and exercise capacity outcomes with patients recovering from both PCI and CABG in a standardized program.

## **METHODS**

## **PARTICIPANTS**

The project was conducted in accordance with ethics guidelines and approval by the Sydney Adventist Hospital and Charles Sturt University Human Ethics Research Committee. All participants were provided with an information sheet and gave informed consent.

The participants who agreed to participate in this study were prospectively and consecutively enrolled between July and December 2010 and part of the CR program at the Sydney Adventist Hospital, Wahroonga, NSW, Australia. All patients had revascularization procedures (either PCI or CABG) within 1 month of enrolment. Patients enrolled into CR were excluded from the study if the principal diagnosis was of cardiac failure, valvular surgery, or patients who had a documented myocardial infarction within 6 months prior to study enrolment. Participants were also excluded from the analysis if they were unable to complete the 6-week CR program or could not participate in the designated exercise program.

## **REHABILITATION PROGRAM**

Exercise sessions were performed three times per week for 6 weeks at 55–70% of peak VO2, measured during the first exercise session at commencement of the CR program combined with a patient perceived exertion rating of 11–13 (fairly light to somewhat hard) on the Borg scale (Borg, 1982). The program included 16 periods of exercise training and six education sessions on cardiovascular risk factors, life style modification measures and the pathophysiology of atherosclerosis. The exercise component of the program was conducted according to the National Heart Foundation of Australia & Australian Cardiac Rehabilitation Association (NHFA and ACRA, 2004) guidelines. Each participant was given an individualized exercise program consisting of aerobic exercise (cycle ergometry, treadmill walking, and rowing) that was devised by an exercise physiologist to ensure the participants could exercise continually throughout the session at the prescribed level of intensity. Each session consisted of stretching and warmup exercise (5–10 min), endurance training (15–20 min), resistance training (10–15 min), or strength training (10–15 min) and cool-down/relaxation exercise (5–10 min). Participants were also advised to complete a home walking program, as recommended by the National Heart Foundation to achieve 30 min of moderate intensity physical activity on most or all days of the week.

## **DATA COLLECTION**

The 6-min walk test (6MWT) is a standard measure of functional walking capacity and as such provides insight into the likely effect of participation on patients' ability to carry out activities of daily living (Fiorina et al., 2007). This test consists of walking up and down an 18 m indoor track as many times as possible within a 6-min period (NHFA and ACRA, 2004; West et al., 2012). In accordance with the American Thoracic Society guidelines this test was conducted twice prior to commencing CR, with the better of the two tests recorded as baseline (Argyropoulos and Harper, 2002). Patients were allowed to rest if required, and the assessment was ceased if there was angina pectoris or undue shortness of breath, which would normally limit activity and/or necessitate the use of coronary vasodilator therapy. Patients were advised of elapsed time at each minute of the assessment. No other encouragement was given. The peak VO2 was collected throughout the 6MWT and calculated using the American College of Sports Medicine formula (ACSM, 2006).

A 20-min, 3-lead ECG recording (PowerLab 4/30, LabChart Version 7, Castle Hill, NSW, Australia) was obtained prior and post CR. Time series variables were analyzed over the 20-min recording range, whereas frequency domain measures were analyzed using the accepted 5-min successive interval method to avoid the influence of non-stationarity (Task Force, 1996). The time domain variables considered in this study were the mean RR interval and its standard deviation of the N-N intervals (SDNN), representing the overall HRV and its root mean square successive difference (rMSSD), representing the vagal tone. The frequency-domain variables measured were LF and HF and the ratio LF/HF, which provide information on vagal and sympathetic input modulating HRV (Carney et al., 2005).

Other information obtained from patients attending the CR included medications, blood pressure, smoking, and diabetes status. Height and weight were measured, and the body mass index (BMI) calculated. Waist circumference was measured within an accuracy of 0.1 cm. Blood pressure was measured with a clinical zero sphygmomanometer. The average of two measurements was used to determine blood pressure. A cardiac history questionnaire was given to all participants to ascertain any other factors that may impact on their exercise capacity and HRV.

## **DATA ANALYSIS**

All the data were analyzed by LabChart version 7 (ADInstruments, Castle Hill, Sydney, Australia). In accordance with the American Thoracic Society guidelines (ATS, 2002), 6MWT results were expressed as an absolute value. Exercise capacity was expressed as peak VO2 in ml/kg.min.

Statistical analysis was performed using SPSS Version 20 (Copyright IBM Inc.). The Wilcoxon Signed-Ranks Test was used to compare HRV parameters for the CABG and PCI groups before and following CR. A student *t*-test was used to compare the 6MWT and peak VO2 results before and following CR. To assess the correlation on a nominal scale the Pearson's correlation test was used. Covariate analysis to model the influence of 6MWT and peak VO2 on effectiveness of CR was determined using Friedman's test. Differences were considered significant when *p* < 0.05. Values were expressed as means and standard deviation for normally distributed data and medians and interquartile range (IQR) if the data was not normally distributed.

## **RESULTS**

## **BASELINE VALUES OF PATIENTS**

Forty-two patients were consecutively enrolled after successful cardiac intervention procedures if they agree to the research and provided informed consent. The PCI group consisted of 25 patients and the CABG group of 17 patients. One patient was unable to complete the CR program, two missed the follow-up appointment and one patient was hospitalized during the rehabilitation program. No patients experienced angina during the exercise component of the rehabilitation program. Data from 22 patients in the PCI group and 16 patients in the CABG group were used for the final analysis.

There were no significant differences in risk profile including age, blood pressure, smoking, and diabetes, and clinical presentation between the two groups (**Table 1**). Medication use was similar between the two groups apart from a significant difference in the PCI group used angiotensin converting enzyme inhibitors (ACEI). One patient in the PCI group and two patients in the CABG group discontinued β-blocker therapy and were excluded from the analysis.

#### **Table 1 | Patient demographics and clinical history.**

#*Mean and standard deviation; \*PCI vs. CABG p* < *0.05; \*\*Atrial fibrillation.*

#### **CLINICAL VARIABLES**

BMI and waist circumference were measured before and after CR. No significant differences were observed after the 6-week CR programme (**Table 2**).

## **HEART RATE VARIABILITY**

Significant increases were seen for SDNN, RMSSD, LF, and HF in the CABG group following CR compared to baseline (**Table 3**).

There was no significant change in HRV post CR in the PCI group for all HRV measures. Significant differences were seen for the majority of HRV measures for the CABG group when comparing post CR to baseline levels (**Table 3**).

Analysis of the difference in the extent of change (-) between the CABG and PCI groups for the time and frequency domain parameters following CR revealed no significant differences in the HRV parameters. However, CABG patients showed a greater improvement in all HRV measures.

A significant interaction in the PCI group but not in the CABG group was observed using covariant analysis between the 6MWT and peak VO2 with HRV parameters, indicating that being healthier that is, better 6MWT and peak VO2, led to better HRV results but no significant effect of CR in the PCI group. When the results were investigated for baseline 6MWT and peak VO2 in the CABG group a significant influence of CR on HRV parameters was retained in the CABG group (*p* = 0.0072).

## **EXERCISE CAPACITY**

Clinically significant improvements in exercise capacity were observed in both PCI and CABG groups following CR as judged

*\*\*waist circumference.*

**Table 3 | Changes of the HRV indices in PCI and CABG group.**

by the American Thoracic Society guidelines (2002), which stipulate that an increase of 54 m in the 6MWT is the minimum distance required (**Table 4**).

Peak VO2 increased significantly in both groups. However, it remained below international and Australian population norms that are set between 20 and 35 ml/kg.min (see **Table 4**). CABG patients improved by 8% and PCI by 1% compared to baseline following the CR program.

## **DISCUSSION**

Our study is the first to investigate whether there is a difference in CR outcomes following a 6 week, moderate-intensity exercise program for patients that have undergone either CABG or PCI with differing levels of parasympathetic suppression at baseline (within the first month following intervention but immediately prior to rehabilitation). Effect of CR on HRV was compared to the traditional measures of 6MWT and peak VO2. CABG patients clearly entered the program with lower HRV parameters, particularly for LF power activity and total HRV, which may be due to more extensive myocardial or cardiovascular disease and the more invasive intervention (Santangeli et al., 2008; Lakusic et al., 2009). CABG intervention has been shown to decrease HRV post-CABG intervention with HRV parameters not improving past preoperative levels even after 6 months (Kuo et al., 1999). There were two principal findings: (1) CR significantly improved both HF power and LF power in the CABG group, which suggests significant changes to the extent or functionality of the autonomic nervous system, (2) CR had a significant impact on the exercise capacity in both groups. Our study also supports that a short 6 week CR program as recommended by the Australian guidelines is sufficient to improve exercise capacity, cardiorespiratory function regardless of intervention (PCI or CABG) and cardiac autonomic function in CABG patients (NHFA and ACRA, 2004). Our study agrees with previous reports that despite patients with diabetes having a higher risk of adverse cardiac events, adherence to a CR program can improve functional capacity (Hindman et al., 2005).

Time after intervention is an important component that influences CR outcomes. We recruited patients between 2 and 4 weeks after intervention and observed that the baseline values for HRV in the CABG group were much lower than for the PCI group in agreement with previous studies (Demirel et al., 2002; Cygankiewicz et al., 2004; Laitio et al., 2006). While there was a correlation between HRV parameters and 6MWT as well as peak VO2, this did not explain all the variance, hence indicating HRV is an independent physiological measure that provides information

*\*Mean and standard deviation.*

on the outcome of CR. HRV may be a more sensitive marker for effectiveness of improvement in cardiac function. HRV can be determined from 2-, 5-min, or longer lead II ECG recordings (Buchheit et al., 2007). 6MWT did show an improvement in both groups with only the CABG group meeting the recommended 56 meters cut-off compared to the PCI group. Peak VO2, demonstrated significant improvement as well for both groups, but results remained well below published population norms for our study and age range. This lower than previously reported outcome in peak VO2 may be due to higher baseline values on entry to the program especially in the PCI group or possibly due to patient motivation, home-based compliance, age, gender, and fitness of the patients. However, even modest improvements are correlated with better long-term outcomes in cardiorespiratory function (Swank et al., 2012).

CR has been demonstrated to decrease mortality following PCI (Goel et al., 2011) and either improved survival or morbidity compared to no CR following CABG in longitudinal studies (Hedbäck et al., 2001). However, comparative studies addressing CR effectiveness with respect to the type of cardiac intervention (PCI vs. CABG) and improvement in HRV compared to the traditional measures of 6MWT and peak VO2 has not been investigated in the one study design. Our study indicates that CR following CABG may improve long-term outcome by reducing the risk of future sudden cardiac death by increasing parasympathetic tone and therefore HRV (Peng et al., 1995).

The effectiveness of CR is influenced by type of surgery and length of rehabilitation program (Eagle et al., 2004). CABG patients usually have more than one target vessel involved, postoperative pain, morbidity, and hence their recovery period is often longer. Patients undergoing PCI, on the other hand, can begin CR 24–48 h after PCI if there is no evidence of hematoma at the catheterization site (West et al., 2012). Length of CR may be a

## **REFERENCES**

*J. Heart Fail.* 7, 411–417. doi: 10.1016/j.ejheart.2004.10.004

factor in its effectiveness, with many studies reporting an 8-week period (Austin et al., 2004; Freyssin et al., 2012).

Our study shows in a single center cohort that CR consistently improved HRV in patients following CABG even when pre CR values of 6MWT and peak VO2 are considered.

## **CONCLUSION**

This study indicates that the effect of CR is of benefit to patients with reduced parasympathetic tone prior to the start of CR and that CR has a greater effect in post-CABG compared to post-PCI. In addition HRV is independent of 6MWT and peakVO2, suggesting that HRV is a useful additional measure to employ for CR.

## **AUTHOR CONTRIBUTIONS**

Herbert F. Jelinek: Lead investigator, organized study protocol, data interpretation, statistical analysis, and writing; Zhaoqi Q. Huang: Cardiologist, performing all experimental work, data analysis, and writing; Hosen Kiat: Cardiologist in charge, responsible for developing exercise protocol for participants, study protocol, interpretation, and writing of article; Dennis Chang: Data interpretation and writing of article; Ahsan H. Khandoker: ECG analysis, data interpretation, and writing of article.

## **ACKNOWLEDGMENTS**

We are grateful to the physical therapists James Bank and Alexandra Scott, for their care of the patients undergoing cardiac rehabilitation. We would like to acknowledge Ms Sharyn Scott for her assistance with co-ordinating the study and all the staff of Sydney Adventist Hospital who assisted in the study. Associate Professor Ian Spence for valuable comments on a previous draft of this manuscript. ECG analysis was performed by Salah Mohamed Widatalla. Herbert Jelinek is currently on leave from Charles Sturt University, Albury, Australia.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 21 May 2013; accepted: 02 October 2013; published online: 30 October 2013.*

*Citation: Jelinek HF, Huang ZQ, Khandoker AH, Chang D and Kiat H (2013) Cardiac rehabilitation outcomes following a 6-week program of PCI and CABG Patients. Front. Physiol. 4:302. doi: 10.3389/fphys.2013.00302*

*This article was submitted to Cardiac Electrophysiology, a section of the journal Frontiers in Physiology.*

*Copyright © 2013 Jelinek, Huang, Khandoker, Chang and Kiat. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## Heart rate variability during simulated hemorrhage with lower body negative pressure in high and low tolerant subjects

## *Carmen Hinojosa-Laborde1\*, Caroline A. Rickards 2, Kathy L. Ryan1 and Victor A. Convertino1*

<sup>1</sup> US Army Institute of Surgical Research, Fort Sam Houston, TX, USA

<sup>2</sup> Department of Health and Kinesiology, University of Texas at San Antonio, San Antonio, TX, USA

#### *Edited by:*

Heikki Veli Huikuri, University of Oulu, Finland

#### *Reviewed by:*

Mikko Paavo Tulppo, Verve, Finland Phyllis Kravet Stein, Washington University School of Medicine, USA

#### *\*Correspondence:*

Carmen Hinojosa-Laborde, U.S. Army Institute of Surgical Research, 3698 Chambers Pass, Fort Sam Houston, TX 78234-6315, USA. e-mail<EMAIL_ADDRESS>Heart rate variability (HRV) decreases during hemorrhage, and has been proposed as a new vital sign to assess cardiovascular stability in trauma patients. The purpose of this study was to determine if any of the HRV metrics could accurately distinguish between individuals with different tolerance to simulated hemorrhage. Specifically, we hypothesized that (1) HRV would be similar in low tolerant (LT) and high tolerant (HT) subjects at presyncope when both groups are on the verge of hemodynamic collapse; and (2) HRV could distinguish LT subjects at presyncope from hemodynamically stable HT subjects (i.e., at a submaximal level of hypovolemia). Lower body negative pressure (LBNP) was used as a model of hemorrhage in healthy human subjects, eliciting central hypovolemia to the point of presyncopal symptoms (onset of hemodynamic collapse). Subjects were classified as LT if presyncopal symptoms occurred during the −15 to −60 mmHg levels of LBNP, and HT if symptoms occurred after LBNP of −60 mmHg. A total of 20 HRV metrics were derived from R–R interval measurements at the time of presyncope, and at one level prior to presyncope (submax) in LT and HT groups. Only four HRV metrics (Long-range Detrended Fluctuation Analysis, Forbidden Words, Poincaré Plot Descriptor Ratio, and Fractal Dimensions by Curve Length) supported both hypotheses.These four HRV metrics were evaluated further for their ability to identify individual LT subjects at presyncope when compared to HT subjects at submax. Variability in individual LT and HT responses was so high that LT responses overlapped with HT responses by 85–97%.The sensitivity of these HRV metrics to distinguish between individual LT from HT subjects was 6–33%, and positive predictive values were 40–73%.These results indicate that while a small number of HRV metrics can accurately distinguish between LT and HT subjects using group mean data, individual HRV values are poor indicators of tolerance to hypovolemia.

**Keywords: lower body negative pressure, hypovolemia, hemorrhage, heart rate variability, heart period variability**

## **INTRODUCTION**

In trauma patients, the primary cause of death within the first hour of injury is hemorrhage (Bellamy, 1984; Sauaia et al., 1995; Champion et al., 2003). Early detection of hypovolemia in trauma patients is therefore critical to improving triage and providing life saving interventions (LSIs). Unfortunately, the severity of hemorrhage or central hypovolemia is difficult to detect by first responders or other emergency medical personnel, as the traditional vital signs currently available to them, such as arterial pressure, heart rate, respiration rate, and pulse character do not consistently change until hemorrhage has progressed to the point of decompensation (i.e., hemodynamic collapse; Heckbert et al., 1998; Parks et al., 2006; Convertino et al., 2008). Assessing the severity of hemorrhage in trauma patients is further complicated by the reality that there are individuals who have a low tolerance to hemorrhage such that, for the same level of blood loss, low tolerant (LT) individuals reach the point of cardiovascular collapse in less time than those who are high tolerant (HT; Shoemaker

et al., 1973, 2006; Klemcke et al., 2011). Thus, there is an urgent need to identify physiological parameters that can accurately track hypovolemia in trauma patients, and have the sensitivity to provide early identification of a patient that has a low tolerance to this physiological insult. New vital signs are needed which can detect the onset, progression, and severity of hypovolemia in individual patients.

The measurement of the variation in R–R intervals, i.e., heart rate variability (HRV; Malik, 1996), can provide information regarding overall cardiovascular status. For instance, healthy individuals with normal autonomic function have high HRV, while individuals with a stressed cardiovascular system (via activity, disease, or dysfunction) can be identified by reduced HRV (Thayer and Sternberg, 2006). HRV has been extensively studied in hospital settings such as intensive care units to assess cardiovascular status and predict mortality in trauma patients (Winchell and Hoyt, 1996, 1997; Grogan et al., 2005; Norris et al., 2005, 2008; Morris et al., 2006). Considering that monitoring electrocardiogram

(ECG) signals is non-invasive, can be accomplished in all echelons of care (including the pre-hospital environment), and can potentially provide information regarding cardiovascular status, the utility of HRV as a pre-hospital triage assist tool is the focus of many investigations (Cooke et al., 2006a,b; Batchinsky et al., 2007a; Cancio et al., 2008; Ryan et al., 2011).

One criterion for the development of accurate and timely assessment of the clinical status of patients with hemorrhage is the capability to investigate the continuous physiological response to progressive reductions in central blood volume. This capability has become available with the emergence of lower body negative pressure (LBNP) as a human experimental model for hemorrhage (Cooke et al., 2004). Cardiovascular responses during LBNP are reproducible, and are consistent with those observed during hemorrhage (Convertino and Sather, 2000; Convertino, 2001; Cooke et al., 2004). In addition, LBNP allows for the assessment of cardiovascular responses which are specific to hypovolemia as confounding factors associated with trauma such as pain and inflammation are absent. Thus, the controlled experimental environment of LBNP allows for optimal conditions in which to study HRV. Using the LBNP model to induce a clinical endpoint (i.e., presyncope), human subjects can be identified as having HT or LT to central hypovolemia (Sather et al., 1986; Convertino and Sather, 2000; Rickards et al., 2011). If HRV metrics reflect the status of cardiovascular stability during progressive central hypovolemia, then LT subjects would be expected to display greater reductions in HRV compared to HT subjects. We therefore used LBNP tolerance as a model to test the hypotheses that: (1) HRV at presyncope is similar in LT and HT subjects when both groups are on the verge of cardiovascular collapse, and (2) LT subjects have lower HRV at presyncope compared to HT subjects who remain hemodynamically stable.

## **MATERIALS AND METHODS**

## **SUBJECTS**

This study was conducted under a protocol reviewed and approved by the Brooke Army Medical Center and US Army Medical Research and Materiel Command Institutional Review Boards and in accordance with the approved protocol. All studies were conducted at the U.S. Army Institute of Surgical Research, Fort Sam Houston, TX, USA. Normotensive, non-smoking healthy human volunteers (*N* = 120; male = 75, female = 45, age 28 ± 1 years; height 174 ± 1 cm; weight 76 ± 1 kg) participated in this study after evaluation of their medical history and physical examination by a physician to ensure the absence of previous and current medical conditions that would exempt them as participants. All female subjects were confirmed as not pregnant by a urine pregnancy test conducted within an hour before the experiment. For 24 h prior to the study, participants were instructed to maintain their normal sleep patterns, refrain from exercise, and abstain from alcohol and autonomic stimulants including prescription and non-prescription drugs such as caffeine, alcohol, and decongestants. The potential effect of caffeine withdrawal on baseline hemodynamics was not assessed in this study. Subjects were given a written description of the experimental protocol and the risks associated with the study. On the day of the study, the subjects were made familiar with the laboratory, given a verbal briefing on

## **LBNP PROTOCOL**

Subjects were instrumented for standard lead II ECG to record R–R intervals (RRI), and a finger cuff to record beat-by-beat finger arterial pressure by photoplethysmography (Finometer Blood Pressure Monitor, TNO-TPD Biomedical Instrumentation, Amsterdam, The Netherlands). To simulate hemorrhage in conscious humans, central hypovolemia was induced by application of LBNP. Previous studies have shown that the LBNP protocol can closely simulate the hemodynamic challenges associated with pre-shock hemorrhage (Cooke et al., 2004; Ward et al., 2010). Subjects were positioned supine within an airtight chamber that was sealed at the level of the iliac crest by a neoprene skirt. The LBNP protocol consisted of a 5-min control period (baseline) followed by 5 min of chamber decompression at −15, −30, −45, and −60 mmHg, and then additional increments of −10 mmHg every 5 min until the onset of cardiovascular collapse followed by a 10-min recovery period. The application of LBNP was terminated based on three criteria.: (a) sudden onset of relative bradycardia, (b) progressive fall of systolic pressure below 80 mmHg; and, (c) voluntary subject termination due to the onset of presyncopal symptoms such as sweating, nausea, dizziness, vision alterations, or general discomfort. During the LBNP protocol, arterial pressure waveform (Finometer) and ECG signals were sampled continuously at 500 Hz and digitally recorded with WinDaq data acquisition software (DATAQ Instruments, Akron, OH, USA).

## **HIGH TOLERANCE VS. LOW TOLERANCE**

Subjects were categorized as LT or HT based on the level of LBNP at which they experienced the onset of cardiovascular collapse (Sather et al., 1986; Rickards et al., 2011). LT subjects experienced cardiovascular collapse before or during −60 mmHg of LBNP, and HT subjects experienced cardiovascular collapse after −60 mmHg of LBNP.

## **DATA ANALYSIS**

Arterial pressure waveform and ECG signals were analyzed during the last 3 min of each LBNP level using a commercially available software program (WinCPRS, Absolute Aliens, Turku, Finland). Beat-to-beat stroke volume was derived from the arterial pressure waveform using the pulse contour method (Jansen et al., 1990).

Electrocardiogram signals were visually inspected and confirmed to be free of noise, persistent ectopic beats, or arrhythmias. When ectopic beats were identified, the presence of a single event within the 3-min analysis window resulted in the correction of the beat by linear interpolation prior to analysis. If more than one ectopic beat occurred during an LBNP level, the ECG data for that level were not analyzed. Acceptable ECG signals were used to calculate RRI and heart rate. The RRI signal was used to derive 20 indices of HRV described in detail in previous publications from this laboratory (Rickards et al., 2010a; Ryan et al., 2010). Linear methods were used to calculate HRV metrics in both the time domain [*n* = 9; RRI standard deviation (RRISD), RRI root mean squared standard deviation (RMSSD), percentage adjacent RRIs varying by at least 50 ms (pNN50), Poincaré

plot descriptors standard deviation 1 and standard deviation 2 (SD1 and SD2), SD1/SD2 ratio, SD2/SD1 ratio, complex demodulation high frequency (CDM HF), complex demodulation-low frequency (CDM LF)]; and frequency domain [*n* = 2; RRI low frequency power (RRI LF), RRI high frequency power (RRI HF)]. For frequency domain analysis, the RRI signals were plotted using linear interpolation, sampled at 5 Hz, and then passed through a low-pass impulse response filter with a cutoff frequency of 0.5 Hz. Data sets were submitted to a Fourier transform with a Hanning window. Non-linear methods were used to calculate heart rate complexity metrics from the RRI signal (*n* = 9; sample entropy (SampEn), Lempel-Ziv entropy (LZEn), fractal dimensions by curve length (FD-L), fractal dimensions by dispersion analysis (FD-DA), symbol dynamics entropy (SymDyn), normalized symbol dynamics entropy (DisnEn), long-range detrended fluctuation analysis (DFA long), short-range detrended fluctuation analysis (DFA short), and Forbidden Words (FW).

Heart rate variability metrics were compared in LT and HT subjects at the level of LBNP at which cardiovascular collapse was imminent (presyncope). In HT subjects, HRV metrics were also calculated one level of LBNP prior to the presyncopal level (submax), and compared with LT presyncopal values. HRV metrics which measured equal variability in LT and HT at presyncope and also less variability in LT at presyncope compared to HT at submax were further evaluated for their ability to distinguish between individual LT and HT subjects.

#### **SENSITIVITY AND POSITIVE PREDICTIVE VALUE**

The effectiveness of HRV metrics to accurately determine group membership (HT or LT) was evaluated by calculating sensitivity and positive predictive value. Sensitivity assesses the ability of a HRV metric to correctly identify a LT subject from all the subjects that were actually LT (i.e., true positives/true positives + false negatives). Positive predictive value is a measure of the ability of a HRV metric to identify a subject as LT from all the subjects that "appear" to be LT (i.e., true positives/true positives + false positives). A logistic regression model was used to evaluate the validity of HRV metrics as predictors of low tolerance to hemorrhage. Using this model, cutoff values were determined for each parameter at each level of LBNP based on receiver-operator characteristic analysis with specificity no less than 0.95. Sensitivity and positive predictive values were then calculated based on this criterion (Pregibon, 1981).

## **STATISTICAL ANALYSIS**

Data are expressed as mean ± SE. Statistical analysis was conducted with commercially available software (SigmaStat; Systat Software, Richmond, CA, USA). Unpaired *t*-test, Chi-square, and *z*-test analyses were used to compare subject demographics and conditions at LBNP termination between tolerance groups. For hemodynamic responses, a two way analysis of variance with repeated measures on one variable was used to determine significant variable (tolerance and LBNP level) main effects (*p* ≤ 0.05). Subsequent multiple comparison tests (Holm–Sidak) determined significant differences between HT and LT groups (*p* ≤ 0.05). HRV measurements at presyncope and submax in LT and HT subjects were compared by unpaired *t*-test.

## **RESULTS**

**SUBJECTS**

The LBNP procedure was conducted on 120 subjects. LBNP was terminated in 2 subjects during −30 mmHg, in 5 subjects during −45 mmHg, in 26 subjects during −60 mmHg, in 43 subjects during −70 mmHg, in 29 subjects during −80 mmHg, in 13 subjects during −90 mmHg, and in 2 subjects during −100 mmHg. As a result, 33 subjects were identified as LT (27.5%) and 87 subjects were identified as HT (72.5%). The demographics and baseline mean arterial pressure and heart rates of these two groups of subjects were not statistically different (**Table 1**). However, baseline RRI was significantly lower in LT subjects compared to HT subjects. The presyncopal conditions observed at LBNP termination are shown in **Table 2**. Cardiovascular conditions include systolic pressure below 80 mmHg and/or sudden onset of relative bradycardia, and presyncopal symptoms represent discomfort reported by the subject. LT and HT subjects experienced similar presyncopal conditions at LBNP termination.

## **RRI TRACKS HYPOVOLEMIA**

Stroke volume and RRI at baseline (LBNP = 0 mmHg) and during LBNP in LT and HT subjects are shown in **Figures 1A,B**. LT subjects experienced presyncopal conditions and/or symptoms before or during LBNP level of −60 mmHg. HT subjects experienced presyncopal conditions and/or symptoms after −60 mmHg.

#### **Table 1 | Demographics and baseline hemodynamic variables for low tolerant and high tolerant subjects.**

Data are expressed as mean ± SE.

Cardiovascular conditions include systolic blood pressure below 80 mmHg and/or sudden bradycardia. Symptoms represent presyncopal symptoms such as sweating, nausea, dizziness, vision alterations, or general discomfort as reported by the subject. Data are expressed as number of occurrences; group percentages are shown in parentheses.

**lower body negative pressure (LBNP) in low tolerant subjects (closed circles, solid line) and high tolerant subjects (open circles, dashed line).** At baseline, there are 33 low tolerant subjects and 87 high tolerant subjects.

Data for HT subjects are shown to LBNP level of −80 mmHg. Both stroke volume and RRI decreased in a similar incremental fashion during the progression of LBNP, and average RRI measurements reflect and are correlated with the hypovolemia induced by LBNP (amalgamated *r* <sup>2</sup> = 0.988 between stroke volume and RRI). Stroke volume was not different between LT and HT groups, but RRI was significantly lower in LT subjects than HT subjects at rest and during −15, −30, and −45 mmHg LBNP (*p* ≤ 0.01).

## **HRV MEASUREMENTS AT PRESYNCOPE AND SUBMAX LEVELS OF LBNP**

**Figure 2** demonstrates the variation in RRI in a single LT subject and a single HT. These sample tracings of RRI were obtained during LBNP level of −60 mmHg, and are 3 min in duration. The variation in RRI is low in the LT subject who is on the verge of cardiovascular collapse (at presyncope). In contrast, the variability in RRI is high in the HT subject who is hemodynamically stable (at submax).

Heart rate variability was quantified at presyncope in all LT and HT subjects, and at submax in HT subjects by 20 HRV metrics. These results are shown in **Table 3**. The first two columns in **Table 3** indicate the parameter or HRV metric and its proportionality to variability. All HRV metrics are quantitatively proportional (P) to HRV except SD2/SD1, DFA long, DFA short, and FW, which are inversely proportional (I) to HRV. The values of proportional metrics decrease as variability decreases, while the values of inversely proportional metrics increase as variability decreases. Columns 3, 4, and 5 in **Table 3** display the data for three groups: LT at presyncope, HT at presyncope, and HT at submax. The comparisons of variability between LT at presyncope verses HT at presyncope, and LT at presyncope verses HT at submax are also shown in **Table 3**, in columns 6 and 7, respectively. The hypotheses of our study were (1) that at presyncope HRV would be equal in LT and HT subjects (column 6) because both groups are on the verge of cardiovascular collapse; and (2)

The number of subjects at the subsequent time points during LBNP decline as subjects experience presyncopal symptoms at various levels of LBNP. Data are shown as mean ± SE. <sup>∗</sup> Denotes group differences at the same LBNP level (p ≤ 0.05).

**tracing, closed circles) are shown for a 2-min time span during LBNP level of** −**60 mmHg.**

that LT subjects would have less HRV at presyncope compared to HT subjects who remain hemodynamically stable at submax (column 7). These hypotheses are stated in **Table 3** above the comparisons of HRV metrics (i.e., "LT = HT," "LT < HT"). When comparing HRV between groups it is important to keep in mind that the numerical values of *proportional* metrics are consistent with variability (i.e., high value indicates high variability), but numerical values of metrics which are *inversely* proportional to variability are opposite to variability (i.e., high value indicates low variability). At presyncope, HRV was equivalent between LT and HT groups as measured by 7 HRV metrics (bold type in column 6 of **Table 3**). When HRV was compared between LT subjects at presyncope and HT subjects at submax, 8 metrics were less **Table 3 | HR, RRI, and HRV metrics in LT at presyncope, HT at presyncope, and HT at submax.**

Data are shown in three groups: low tolerant (LT) at presyncope; high tolerant (HT) at presyncope; and HT at submax. Values (mean ± SE) are shown for Heart Rate (HR), R–R interval (RRI), RRI standard deviation (RRISD), RRI root mean squared standard deviation (RMSSD), percentage adjacent RRIs varying by at least 50 ms (pNN50), Poincaré plot descriptors standard deviation 1 (SD1), and standard deviation 2 (SD2), SD1/SD2 ratio, SD2/SD1 ratio, complex demodulation high frequency (CDM HF), complex demodulation-low frequency (CDM LF), RRI high frequency power (HF), RRI low frequency power (LF), sample entropy (SampEn), Lempel-Ziv entropy (LZEn), fractal dimensions by curve length (FD-L), fractal dimensions by dispersion analysis (FD-DA), symbol dynamics entropy (SymDyn), normalized symbol dynamics entropy (DisnEn), long-range detrended fluctuation analysis (DFA long), short-range detrended fluctuation analysis (DFA short). P indicates HRV metrics values proportional to RRI variability; I indicates HRV metrics values inversely proportional to RRI variability. Comparisons between groups are shown as LT = HT, LT < HT, and LT > HT; <, > denote significant difference between LT and HT group means (p ≤ 0.05); bold type comparisons support the related hypothesis. ∗ Denotes the metrics with responses to support both hypotheses.

variable in LT at presyncope (bold type in column 7 of **Table 3**). The responses of DFA long, FW, SD1/SD2, and FD-L were the only metrics to support both hypotheses, and are indicated by an asterisk (∗) in **Table 3**. These four HRV metrics were further evaluated for their ability to distinguish individual LT and HT subjects.

## **DFA LONG, FW, SD1/SD2, AND FD-L IN INDIVIDUAL LT AND HT SUBJECTS**

DFA long, FW, SD1/SD2, and FD-L in individual LT subjects at presyncope and individual HT subjects at submax are shown as box and whisker plots in **Figures 3A–D**. Statistical analysis of group averages (**Table 3**) reveal significant differences in the four HRV measurements between LT and HT groups. However, when the measurements of DFA long, FW, SD1/SD2, and FD-L from individual subjects are graphically displayed (**Figures 3A–D**), it is evident that individual responses from LT subjects at presyncope are predominantly within the range of responses observed in HT subjects at submax despite the fact that LT subjects are on the verge of cardiovascular collapse and HT subjects are hemodynamically stable. The percentage of LT responses that were coincident with HT responses were 97% for DFA long and FW, 94% for SD1/SD2, and 85% for FD-L. The sensitivities of DFA long, FW, SD1/SD2, and FD-L to identify individual LT subjects were low (12, 6, 24, 33%), and positive predictive values were 50, 40, 67, and 73%.

long), **(B)** forbidden words (FW), **(C)** Poincare plot standard deviations ratio (SD1/SD2), and **(D)** fractal dimensions by curve length (FD-L) in low tolerant (LT) at presyncope (solid circles,

## **DISCUSSION**

To investigate the application of HRV monitoring for assessment of hemodynamic stability during hemorrhage, we used an experimental model to compare HRV metrics in LT and HT subjects during LBNP. In order for any specific HRV metric to be considered as a valid candidate for the assessment of cardiovascular stability in the context of central blood volume loss, two conditions must hold true using this model: (1) LT and HT subjects should display similar HRV at presyncope; and (2) HRV in LT subjects should be less (i.e., LT subjects would be less stable) than HRV in HT subjects at the point in time when LT subjects experienced presyncope. These conditions are based on the premise that hemodynamic instability reflects a specific physiological condition defined by the inability of cardiovascular mechanisms to adequately compensate for reduced central blood volume in all subjects independent of their tolerance, and that LT subjects are hemodynamically unstable at presyncope when HT subjects remain stable. Our results indicate that of the 20 HRV metrics evaluated, only four metrics (DFA Long, FW, SD1/SD2, and FD-L; three of which were calculated by non-linear methods to assess RRI irregularity or complexity) supported both hypotheses.

of lower body negative pressure (LBNP). Data are shown as box (25th/75th percentiles) and whisker (90th/10th percentiles) plots with median value (black line).

We propose that a metric which can predict low tolerance in an *individual* subject prior to cardiovascular collapse would be optimal for use as a triage assist tool. Identification of LT patients is particularly important as the first responder will have less time to initiate effective treatment in this patient population compared with HT patients. Upon identifying the metrics which supported our initial hypotheses, we further evaluated DFA Long, FW, SD1/SD2, and FD-L on their utility as triage assist tools in individual subjects. First, we compared the individual responses from LT subjects at presyncope and HT subjects at submax to determine the overlap in responses between the two groups. Second, we assessed the accuracy of these HRV metrics in detecting LT subjects at presyncope from the total subject pool (sensitivity and positive predictive value).

While the group mean values of DFA Long, FW, SD1/SD2, and FD-L (**Table 3**) distinguished between LT at presyncope (unstable) and HT at submax (stable), the responses in individual LT and HT subjects varied extensively such that 85–97% of the LT responses overlapped with those of HT subjects (**Figures 3A–D**). In addition, the sensitivities of DFA Long, FW, SD1/SD2, and FD-L were 12, 6, 24, and 33%; while positive predictive values were 50, 40, 67, and

73%. Considering that identifying LT subjects by chance alone has 50% sensitivity and 50% positive predictive value, the accuracy of DFA Long and FW to identify LT subjects at presyncope was not much better than flipping a coin, or worse. Essentially, DFA Long and FW did not adequately identify a LT individual even when the subject was on the verge of cardiovascular collapse.

According to our results, FD-L had the highest positive predictive value (73%), and thus had the highest potential for accurately assessing hypovolemia of all 20 HRV metrics evaluated. However, the utility of FD-L to accurately monitor *individual* trauma patients may be limited by potentially high variability in individual responses. In the present study, under very controlled laboratory conditions, the sensitivity of FD-L to identify a LT subject was only 33%, and the overlap of FD-L measurements from LT subjects and HT subjects was 85% (**Figure 3D**). Overall, these results indicate that the utility of FD-L as an accurate triage assisting tool for first responders is also limited.

Heart rate variability has been studied extensively during hemorrhage and trauma. HRV decreases with hypovolemia in human LBNP studies (Cooke and Convertino, 2005; Cooke et al., 2008) and in animal hemorrhage experiments (Batchinsky et al., 2007b, 2010). HRV has also been studied in pre-hospital environments during transport of trauma patients. By retrospective analysis of ECG data from actual trauma patients in transport to hospital care, a number of investigators (Cooke et al., 2006a,b; Batchinsky et al., 2007a; Cancio et al., 2008; Ong et al., 2008; King et al., 2009) have identified several HRV metrics that are associated with mortality or the need for a LSI. Importantly, however, all of these analyses were based on evaluation of group mean data only, with no consideration of the appropriateness of application to individual patients.

Despite the observation of depressed HRV in subsets of trauma patients, the use of HRV as a prognostic tool to assess the severity of hemorrhage or trauma is controversial for a number of important reasons. Ryan et al. (2010) evaluated the ability of numerous HRV metrics to track hypovolemia in individual subjects undergoing simulated hemorrhage with LBNP. They observed that when group means were evaluated several metrics correlated very well with reductions in stroke volume (*r* ≥ 0.87), but none of the HRV metrics consistently correlated with changes in stroke volume in individual subjects (*r* ≤ 0.49).

To further investigate the prognostic relevance of HRV, Rickards et al. (2010b) evaluated a wide range of HRV metrics and their association with the administration of LSIs in actual trauma patients who all had normal vital signs during transport. These patients would benefit most from identification of an early predictor of cardiovascular collapse, as their physiological status could not be accurately determined from currently available standard vital signs. Of the HRV metrics studied, only one metric, FD-L, was uniquely associated with the administration of a LSI. However, FD-L variance in both groups of patients was too high to accurately determine group membership (LSI vs. No-LSI) on an individual basis, and the number of false negatives identified with FD-L further limited the power of this metric as an accurate indicator of LSIs in individual trauma patients. Interestingly, FD-L also showed the highest positive predictive value for identifying LT subjects in the current study but, just as in trauma patients, the

overlap between individual responses from LT and HT subjects was large (85%), and we found the sensitivity (33%) of this metric to be limited.

There are several technical factors associated with ECG monitoring which can limit the usefulness of HRV metrics in a prehospital setting. First, ECG signals must display normal sinus rhythm for accurate calculation of HRV, but trauma patients often develop arrhythmias such as premature atrial and ventricular contractions (Sethuraman et al., 2010). Second, ECG signals must have a low level of random noise, which usually results from motion artifact, an unavoidable consequence of patient transport in the pre-hospital setting. Third, the data from the ECG signal should be stationary, i.e., a relatively stable RRI signal without wide fluctuationsfrom interventions or patient manipulation. Unfortunately, standard pre-hospital care typically requires extensive patient interventions and manipulation. Other factors which can also contribute to ECG non-stationarity such as disease, age, recreational drugs, medications, alcohol, smoking, and postural changes are widely present in the trauma patient population (Bilchick and Berger, 2006). Finally, the length of the ECG data set required for accurate measurements varies by HRV metric, and can range from 100 to 800 RRIs; which can be an inordinately long time in a trauma patient with rapidly changing physiological status (Acharya et al., 2006; Rickards et al., 2010a). Thus, the quality of HRV measurements are optimal when they can be calculated from extended, stable ECG recordings under standardized and very stable conditions; these conditions, however, are not typically encountered when first responders are treating trauma patients.

Historically, heart rate has been closely monitored in trauma patients because of the widely accepted notion that the magnitude of tachycardia reflects the degree of hypovolemia. However, the reliability of tachycardia in response to hypovolemia has been questioned as tachycardia can be absent in many trauma patients despite the development of hypotension associated with bleeding (Victorino et al., 2003; Brasel et al., 2007). In addition, the results of the current study indicate that tachycardia was a poor indicator of tolerance to hypovolemia because LT subjects at presyncope had lower heart rates than HT subjects at greater levels of central hypovolemia (**Table 3**). Based on the clinical doctrine that tachycardia signals a more severe state of hypovolemia and the approach of cardiovascular collapse, the HT subjects would have been erroneously identified as more hemodynamically unstable and at greater risk of developing circulatory shock than LT subjects. Although a change in cardiac rhythm may represent an adverse clinical status during hemorrhage, the results of the present study reinforce the unreliability of heart rate (and subsequently calculated metrics derived from heart rate such as HRV) for patient triage.

In summary, the results of the current study are consistent with the findings that ECG-derived metrics of HRV failed to provide reliable information about clinical status in individual subjects during progressive reductions in central blood volume similar to those experienced during hemorrhage (Ryan et al., 2010), or the need for LSIs in individual trauma patients (Rickards et al., 2010b). In the present study, heart rate and HRV metrics derived from ECG signals were found to be poor indicators of LT to hypovolemia even in the controlled experimental environment of simulated hemorrhage. This study raises further concern that monitoring heart rate or calculated derivatives of heart rate (e.g., HRV) will not reliably identify those patients who are least tolerant to hypovolemia and therefore at highest risk for early hemodynamic collapse during hemorrhage.

## **ACKNOWLEDGMENTS**

We thank the research volunteers for their cheerful participation, Dr. James Aden for his assistance in statistical analysis, and

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## **DISCLAIMER**

The opinions or assertions contained herein are the private views of the authors, and are not to be construed as official or as reflecting the views of the Department of the Army or the Department of Defense.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 25 July 2011; paper pending published: 22 August 2011; accepted: 01* *November 2011; published online: 21 November 2011.*

*Citation: Hinojosa-Laborde C, Rickards CA, Ryan KL and Convertino VA (2011) Heart rate variability during simulated hemorrhage with lower body negative pressure in high and low tolerant subjects. Front. Physio. 2:85. doi: 10.3389/fphys.2011.00085*

*This article was submitted to Frontiers in Clinical and Translational Physiology, a specialty of Frontiers in Physiology.*

*Copyright © 2011 Hinojosa-Laborde, Rickards, Ryan and Convertino. This is an open-access article subject to a nonexclusive license between the authors and Frontiers Media SA, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and other Frontiers conditions are complied with.*

## Why should one normalize heart rate variability with respect to average heart rate

## *Jerzy Sacha\**

*Department of Cardiology, Regional Medical Center, Opole, Poland \*Correspondence<EMAIL_ADDRESS>
*Edited by:*

*Mikko Paavo Tulppo, Verve, Finland*

**Keywords: heart rate variability, heart rate, autonomic nervous system, analysis, R-R interval**

explains such a case (Sacha and Pluta, 2005a).

Another mathematical problem concerning the association between HRV and

**FIGURE 1 | (A)** The non-linear (mathematical) relationship between R-R interval and heart rate is depicted. One can see that the oscillations of a slow average heart rate (*x*-axis, dark gray area) result in much greater oscillations of RR intervals (*y*-axis, dark gray area) than the same oscillations of a fast average heart rate (light gray area). As a consequence, the variability of R-R intervals is higher for the slow average heart rate than for the fast one, despite the fact that the variability of heart rate is the same (reprinted with modification from Sacha and Pluta, 2008). **(B)** The relationship between heart rate and R-R interval is shown—the same oscillations of R-R intervals yield much greater oscillations of HR for the fast average heart rate (dark blue area) than for the slow one (light blue area). Consequently, the variability of HR is higher for the case with fast average heart rate, despite the fact that the variability of R-R intervals is the same in both cases. **(C)** The relationship between R-R interval and heart rate is depicted along with two signals oscillating in different extents. Signal A oscillates between 60 and 80 bpm but signal B between 80 and 110 bpm. One can see that signal A is more variable (its amplitude is higher) than signal B when expressed as R-R interval signals, and conversely signal A is less variable than B if expressed as HR ones. The example clearly shows how the same signals may reveal an inverse relationship with each other depending on the way they are expressed (reprinted from Sacha and Pluta, 2005a). **(D)** The relationship between R-R interval and heart rate with two hypothetical examples of R-R interval oscillations (i.e., A and B) are presented. It is shown that the fluctuations of R-R intervals may be potentially quite high for a slow average HR (A), however, such fluctuations are not possible for a fast average HR (B) since the R-R intervals should have become negative.

Heart rate variability (HRV) is a recognized risk factor in many disease states (Bravi et al., 2011; Sacha et al., 2013a). However, HRV is significantly correlated with an average heart rate (HR), and this association is both physiologically and mathematically determined. The physiological determination comes from the autonomic nervous system activity (Task Force of the European Society of Cardiology, and the North American Society of Pacing and Electrophysiology, 1996), but the mathematical one is caused by the non-linear (inverse) relationship between R-R interval and HR (Sacha and Pluta, 2005a,b, 2008).

HRV may be estimated by using R-R interval (the most frequent method) or HR signals, yet, they both do not yield the same results since they are inversely related with each other—indeed, the analyses are mathematically biased (Sacha and Pluta, 2005a,b). If one uses R-R intervals, the same changes of HR cause much higher fluctuations of R-R intervals for the slow average HR than for the fast one (**Figure 1A**). Conversely, if one employs HR signals, the same changes of R-R intervals cause much higher fluctuations of HR for the fast than slow average HR (**Figure 1B**). Consequently, due to these mathematical reasons, HRV estimated from R-R intervals should negatively correlate with average HR (or positively with average R-R interval), but HRV estimated from HR signals should be positively correlated with average HR (or negatively with average R-R interval) (Sacha and Pluta, 2005a,b). Moreover, due to the inverse relationship between R-R interval and HR, there is a possibility that a given patient may have higher HRV than another in terms of R-R intervals and lower HRV in terms of HRs—**Figure 1C** HR is presented in **Figure 1D**. One can see that the fluctuations of R-R intervals may be potentially very high for slow average HR, however, the same fluctuations are not possible for fast average HR, since the R-R intervals should have become negative. The same problem can be met if one calculates HRV from HR signals, i.e., the average HR of 80 bpm may potentially fluctuate between 30 and 130 bpm (i.e., the fluctuation amplitude equals 100 bpm), however, such fluctuations are not possible for the average HR of 40 bpm, since the heart rhythm must have fluctuated between −10 and 90 bpm.

Due to the above facts, the standard HRV analysis is mathematically biased, particularly if patients differ in terms of their average HRs. The only way to overcome it is to calculate HRV with respect to the average value, i.e., to normalize the fluctuations with respect to the mean (Sacha and Pluta, 2005a,b, 2008). One can do that by division of the signal by the average R-R interval in the case of R-R interval signal, or by the average HR in the case of HR signal. Moreover, this way the same results are obtained no matter if one calculates HRV from R-R intervals or HRs (Sacha and Pluta, 2005a).

Such an approach enables to explore the HR contribution to the physiological and clinical significance of HRV (Billman, 2013; Sacha et al., 2013a). Recently, this approach has been further developed to enhance or completely remove the HR influence (even physiological one) on HRV, what turned out to provide valuable information on cardiac and non-cardiac prognosis in patients after myocardial infarction—the details have been published elsewhere (Sacha et al., 2013a,b,c).

To conclude, HRV is significantly associated with HR, which is caused by both physiological and mathematical phenomena, however, by a simple mathematical modification one may exclude mathematical bias and explore a real clinical value of HR and its variability.

## **REFERENCES**

et al. (2013a). How to select patients who will not benefit from ICD therapy by using heart rate and its variability? *Int. J. Cardiol*. 168, 1655–1658. doi: 10.1016/j.ijcard. 2013.03.040

*Received: 18 September 2013; accepted: 04 October 2013; published online: 22 October 2013.*

*Citation: Sacha J (2013) Why should one normalize heart rate variability with respect to average heart rate. Front. Physiol. 4:306. doi: 10.3389/fphys.2013.00306*

*This article was submitted to Clinical and Translational Physiology, a section of the journal Frontiers in Physiology.*

*Copyright © 2013 Sacha. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## The effect of heart rate on the heart rate variability response to autonomic interventions

## *George E. Billman\**

*Department of Physiology and Cell Biology, The Ohio State University, Columbus, OH, USA*

#### *Edited by:*

*Jerzy Sacha, Regional Medical Center, Poland*

#### *Reviewed by:*

*Stephen E. DiCarlo, Wayne State University, USA David C. Randall, University of Kentucky College of Medicine, USA*

#### *\*Correspondence:*

*George E. Billman, Department of Physiology and Cell Biology, The Ohio State University, 304 Hamilton Hall, 1645 Neil Ave., Columbus, OH 43210-1218, USA e-mail<EMAIL_ADDRESS>
Heart rate variability (HRV), the beat-to-beat variation in either heart rate (HR) or heart period (R-R interval), has become a popular clinical and investigational tool to quantify cardiac autonomic regulation. However, it is not widely appreciated that, due to the inverse curvilinear relationship between HR and R-R interval, HR *per se* can profoundly influence HRV. It is, therefore, critical to correct HRV for the prevailing HR particularly, as HR changes in response to autonomic neural activation or inhibition. The present study evaluated the effects of HR on the HRV response to autonomic interventions that either increased (submaximal exercise, *n* = 25 or baroreceptor reflex activation, *n* = 20) or reduced (pharmacological blockade: β-adrenergic receptor, muscarinic receptor antagonists alone and in combination, *n* = 25, or bilateral cervical vagotomy, *n* = 9) autonomic neural activity in a canine model. Both total (RR interval standard deviation, RRSD) and the high frequency (HF) variability (HF, 0.24–1.04 Hz) were determined before and in response to an autonomic intervention. All interventions that reduced or abolished cardiac parasympathetic regulation provoked large reductions in HRV even after HR correction [division by mean RRsec or (mean RRsec)<sup>2</sup> for RRSD and HF, respectively] while interventions that reduced HR yielded mixed results. β-adrenergic receptor blockade reduced HRV (RRSD but not HF) while both RRSD and HF increased in response to increases in arterial blood (baroreceptor reflex activation) even after HR correction. These data suggest that the physiological basis for HRV is revealed after correction for prevailing HR and, further, that cardiac parasympathetic activity is responsible for a major portion of the HRV in the dog.

**Keywords: heart rate, heart rate variability, autonomic nervous system, cholinergic receptor antagonists, β-adrenergic receptors, exercise, baroreceptor reflex**

## **INTRODUCTION**

Heart rate variability (HRV, beat-to-beat changes in the heart period, R-R interval) is increasingly used to quantify cardiac autonomic regulation and to identify patients at an increased risk for adverse cardiovascular events (Appel et al., 1989; Task Force of the European Society of Cardiology, and the North American Society of Pacing and Electrophysiology, 1996; Berntson et al., 1997; Denver et al., 2007; Thayler et al., 2010; Billman, 2011, 2013). However, it is not widely appreciated that the prevailing heart rate (HR) can influence HRV independent of changes in cardiac autonomic regulation.

As a consequence of the inverse curvilinear relationship between HR and R-R interval, identical changes in HR will elicit profoundly different changes in the R-R interval variability depending upon the baseline HR (Sacha and Pluta, 2008). For examples, as is illustrated in **Figure 1**, the same HR variability (±1.6 beats/min) is associated with a much greater R-R interval variability (RRSD) at lower (RRSD at 30 beats/min = 105.9 ms) as compared to higher (RRSD at 180 beats/min = 2.9 ms) prevailing HRs. Several studies have reported a strong inverse correlation between HR and various time domain indices of HRV (e.g., the standard deviation (SD) of normal beats, SDNN; Kleiger et al., 1987; Van Hoogenhuyze et al., 1991; Fleiss et al., 1992) such

that R-R interval variability increases as average HR decreases. Frequency domain analysis of HRV is similarly affected by mean HR. Sacha and co-workers (Sacha and Pluta, 2005a,b; Sacha et al., 2013a,b) demonstrated that the high frequency (HF) component of HRV was inversely, while the low frequency (LF) component was directly, related to average baseline HR of the subject. As such, differences in average HR *per se* will influence HRV magnitude independent of cardiac autonomic nerve activity either magnifying or masking (diminishing) the autonomic component of HRV as HR changes. It is therefore essential to correct HRV for the prevailing HR in order to identify physiological (changes in cardiac autonomic regulation), as opposed to artifactual (that merely arise as a consequence of a mathematical relationship), changes in HRV.

Although Sacha and co-worker (Sacha and Pluta, 2005a,b, 2008; Sacha et al., 2013a,b) have recently examined the relationship between average HR and indices of HRV under baseline conditions and compared methods to correct HRV for HR, the effects of HR on HRV during the activation or inhibition of cardiac autonomic regulation remained to be determined. As autonomic interventions will alter the prevailing HR, it is particularly important to correct indices of HRV for HR in order to differentiate between the HRV changes that are directly related

to cardiac autonomic neural activation or inhibition from those changes that result merely as a mathematical consequence of increases or decreases in the baseline HR. It, therefore, was the purpose of the present study to evaluate the effects of wellcharacterized autonomic interventions on HRV after correction for average HR. Using a canine model, Cardiac autonomic neural activity was increased by submaximal exercise or the activation of the baroreceptor reflex and reduced by pharmacological (autonomic blockade: β-adrenergic receptor, muscarinic receptor antagonists alone and in combination) or by surgical (bilateral cervical vagotomy) interventions.

## **METHODS**

All the animal procedures were approved by the Ohio State University Institutional Animal Care and Use Committee and conformed to the *Guide for the Care and Use of Laboratory Animals* published by the US National Institutes of Health (NIH publication N. 85-23, revised 1996).

Archived data from 74 heartworm free mixed breed dogs (1–3 y old, male *n* = 32, female *n* = 42) weighing 19*.*3 ± 0*.*4 kg (range = 11.6–26.8 kg) were used in the present study. The sole selection criterion was an ECG signal of sufficient quality to determine HRV both at baseline and in response to autonomic neural interventions (i.e., submaximal exercise, baroreceptor reflex activation, pharmacological autonomic blockade, or bilateral cervical vagotomy).

## **HEART RATE VARIABILITY PROTOCOLS**

Body surface electrodes were placed on either side of the animal's chest and secured with surgical tape. HRV was then calculated using a Delta-Biometrics vagal tone monitor triggering off the electrocardiogram R-R interval (Urbana-Champaign, IL). This device employs the time-series signal processing techniques as developed by Porges to estimate the amplitude of respiratory sinus arrhythmia [the HF component of R-R interval variability (Porges, 1986)]. Details of this analysis have been described previously (Billman and Hoskins, 1989; Billman and Dujardin, 1990; Houle and Billman, 1999). Data were averaged over 30s intervals before and after the autonomic interventions (see below). The following indices of HRV were determined: Vagal Tone Index - the HF component of R-R interval variability (HF, 0.24–1.04 Hz), and SD of the R-R intervals (a marker of total variability) for the same 30 s time periods.

In order to remove any mathematical bias from HRV calculations, Sacha and co-workers (Sacha and Pluta, 2005a,b; Sacha et al., 2013a,b) previously demonstrated that SD of R-R and frequency data (power spectra) should be corrected by division with the corresponding mean R-R interval or mean R-R interval (in seconds) squared, respectively. These correction factors were used in all subsequent analyses.

## **AUTONOMIC INTERVENTIONS**

Animals received the following interventions to increase or decrease cardiac autonomic regulation: pharmacological blockade (*n* = 25); baroreceptor reflex activation (*n* = 20); submaximal exercise (*n* = 25); and bilateral cervical vagotomy (*n* = 9).

## **AUTONOMIC BLOCKADE (***n* **= 25)**

First, the dogs were trained to lie quietly and unrestrained on a laboratory table. Once the animals had habituated to the laboratory environment, a catheter was percutaneously placed in a cephalic vein for the administration of a non-selective βadrenergic receptor antagonist (propranolol HCl, 1.0 mg/kg, i.v.) followed, at least 5 min later, by a cholinergic muscarinic receptor antagonist (atropine sulfate, 50 μg/kg, i.v.). The drug doses had been previously shown to provide an effective inhibition of cardiac autonomic neural receptors (Billman and Dujardin, 1990). One week later, the study was repeated with the drugs given in the reverse order (i.e., atropine followed by propranolol). HRV was monitored continuously 5 min before and for at least 5 min after each drug injection to ensure that peak changes had been achieved.