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NONLINEAR ANALYSIS OF HEART RATE DYNAMICS DURING RECOVERY FROM FLEXIBLE POLE EXERCISE INTERVENTION
Ana M. S. Antonio1, David M. Garner2 , Rodrigo D. Raimundo3,4, Leticia S. de Oliveira1, Luiz Carlos de Abreu3,4, Marcelo T. Navega5, Vitor E. Valenti
Aim. Evaluated the acute effects of exercise with flexible pole on complex behavior of heart rate variability (HRV).
Material and methods. We investigated 32 healthy female volunteers aged between 18 and 25 years who executed a session of exercise with flexible pole. HRV was analyzed 10 minutes before and, 10 minutes' post-exercise. We then applied five entropic measures and Poincare plot directly to the RR-intervals of the electrocardiographic signal.
Results. Sample entropy was significantly decreased during recovery from exercise with flexible pole (0,8329±0,1111 vs. 0,6568±0,1959; p<0,0001). The Poincare plot indicated reduced dispersion of RR intervals after exercise, indicating reduced HRV Conclusion. Exercise with flexible pole was able to acutely reduce chaotic behavior of heart rate dynamics measured by Sample Entropy alone. Care should be practiced when applying this exercise protocol to patients with cardiac diseases and/or abnormalities.
Russ J Cardiol 2016, 4 (132), Engl.: 160-164
http://dx.doi.org/10.15829/1560-4071-2016-4-eng-160-164
Key words: cardiovascular system, autonomic nervous system, cardiovascular physiological phenomena, rehabilitation, physical and rehabilitation medicine, exercise therapy.
1Centro de Estudos do Sistema Nervoso Autónomo (CESNA), Programa de Pós-Graduajao em Fisioterapia, Faculdade de Ciéncias e Tecnología, UNESP Presidente Prudente, SP, Brasil; Cardiorespiratory Research Group, Department of Biological and Medical Sciences, Faculty of Health and Life Sciences, Oxford Brookes University, Gipsy Lane, Oxford OX3 0BP, United Kingdom; 3Department of Environmental Health, Harvard School of Public Health, Boston, MA, United States of America; 4Laboratório de Delineamento em Pesquisas e Escrita Científica, Faculdade de Medicina do ABC, Santo André, SP, Brasil; 5Departamento de Fisioterapia e Terapia Ocupacional, Faculdade de Filosofia e Ciéncias, UNESP Marilia, SP, Brasil.
Corresponding author. Vitor E. Valenti. Departamento de Fonoaudiologia, Av. Hygino Muzzi Filho, 737. 17.525-000 - Marilia, SP, Phone: +55 (14) 3402-1300. E-mail: vitor.valenti@marilia.unesp.br
HRV — heart rate variability, ANS — autonomic nervous system.
Received March 11, 2016. Revision received March 14, 2016. Accepted March 21, 2016.
НЕЛИНЕЙНЫЙ АНАЛИЗ ДИНАМИКИ ЧАСТОТЫ СЕРДЕЧНЫХ СОКРАЩЕНИЙ ВО ВРЕМЯ ВОССТАНОВЛЕНИЯ ПОСЛЕ УПРАЖНЕНИЙ С ГИБКИМ ШЕСТОМ
Ana M. S. Antonio1, David M. Garner2 , Rodrigo D. Raimundo3,4, Leticia S. de Oliveira1, Luiz Carlos de Abreu3,4, Marcelo T. Navega5, Vitor E. Valenti
Цель. Оценить непосредственное действие упражнений с гибким шестом на сложное поведение вариабельности сердечного ритма (ВСР). Материал и методы. Мы исследовали 32 здоровых женщин-добровольцев в возрасте от 18 до 25 лет, которые исполняли сеанс тренировки с гибким шестом. ВСР анализировали за 10 минут до и через 10 минут после тренировки. Затем мы применили пять измерений энтропии и гипотезу Пуанкаре напрямую к РР-интервалам ЭКГ сигнала.
Результаты. Энтропия образца была значительно снижена во время восстановления от физических упражнений с гибким шестом (±0,8329 0,1111 и 0,6568±0,1959; р<0,0001). Гипотеза Пуанкаре указывает на снижение дисперсии интервалов RR после тренировки, указывающих на снижение ВСР. Заключение. Упражнение с гибким шестом может уменьшить хаотическое поведение в динамике сердечного ритма и измеряется энтропией образца. Использование протокола тренировок для пациентов с заболеваниями сердца и/или иными отклонениями должно получить практическое применение.
Российский кардиологический журнал 2016, 4 (132), Англ.: 160-164
http://dx.doi.org/10.15829/1560-4071-2016-4-eng-160-164
Ключевые слова: сердечно-сосудистая система, вегетативная нервная система, сердечно-сосудистые физиологические феномены, реабилитация, физическая и реабилитационная медицина, ЛФК.
1Centro de Estudos do Sistema Nervoso Autónomo (CESNA), Programa de Pós-Graduajao em Fisioterapia, Faculdade de Ciéncias e Tecnología, UNESP Presidente Prudente, SP, Бразилия; Cardiorespiratory Research Group, Department of Biological and Medical Sciences, Faculty of Health and Life Sciences, Oxford Brookes University, Gipsy Lane, Oxford OX3 0BP, Великобритания; 3Department of Environmental Health, Harvard School of Public Health, Boston, MA, United States of America; 4Laboratório de Delineamento em Pesquisas e Escrita Científica, Faculdade de Medicina do ABC, Santo André, SP, Бразилия; 5Departamento de Fisioterapia e Terapia Ocupacional, Faculdade de Filosofia e Ciéncias, UNESP Marília, SP, Бразилия.
Autonomic changes during recovery from exercise provides important information that may not be identified at rest [1]. The physiological response of the autonomic nervous system (ANS) to exercise is characterized by initial parasympathetic withdrawal and subsequent increase in sympathetic activity. Following exercise, the parasympathetic reactivation is involved in recovery of heart rate to the basal level [2, 3].
Heart rate variability (HRV) is a simple, inexpensive, non-invasive and reliable measurement of heart rate auto-nomic regulation. Here we apply the algorithms directly to the RR-intervals derived from the PQRST-waveform of the signal [4].
Exercise with a flexible pole is a useful physiotherapy intervention for upper limb rehabilitation. Hitherto, its acute effects on cardiac autonomic regulation are unclear.
A very recent study revealed that an acute session of flexible pole exercise induced significant changes in heart rate autonomic modulation [5], however, others found no significant change [6, 7]. Investigation of autonomic responses to exercise with flexible pole has relevant information for planning rehabilitation protocols in patients with cardiovascular disorders, since heart rate responses during recovery from exercise provides information regarding risk for cardiovascular events and sudden death [8].
In this sense, non-linear analysis of heart rate dynamics provides qualitative measurement of cardiovascular physiology and the susceptibility to "dynamical disease" [9] states while linear measurement of HRV is limited [4]. Previously, studies which applied geometric measures and linear indices [10] or chaotic global techniques [11] on HRV during recovery from exercise with flexible pole have so far proven inconclusive. Thus, we evaluated the acute effects of exercise with flexible pole on complex behavior of HRV through five entropic measures. These are enforced directly onto the RR-intervals with no power spectral step as is the case with chaotic global methods [12, 13]. The techniques used here were Approximate [14], Sample [15], Shannon [16], Multi-scale Renyi [17] and Multiscale Tsallis [18] entropies.
Material and methods
Study Population. To determine the sample size a priori knowledge was required, based on Moreno et al. [19], a sample size of 18 participants was stipulated by a test of hypothesis (two-tail), with 5% level of significance and 80% power.
This study consisted of 32 healthy female student subjects, all nonsmokers, aged 19,8±1,6 years old, height 1,62±0,004
m, mass 58,8±10 kg and body mass index (BMI) of 22,2±3,7
2
kg/m . All volunteers were informed about the procedures and objectives of the study and gave written informed consent. All study procedures were approved by the Ethics Committee in Research of the Faculty of Sciences of the UNESP, Campus of Marilia (No. 0554-2012), and were in accordance with Resolution 466/2012 National Health 10/10/1996.
Subjects under the following conditions were excluded:
2
body mass index (BMI) >35 kg/m ; systolic blood pressure (SBP) >140 mmHg or diastolic blood pressure (DBP) >90 mmHg (at rest); reported cardiovascular, respiratory, endocrine and reported neurological disorders or any condition that did not allow the volunteers to perform the procedures. Subjects under medication that influence the ANS were not included. Volunteers were not evaluated on 10-15 days and 20-25 days after the first day of the menstrual cycle [20]. We also excluded physically active subjects according to the International Physical Activity Questionnaire (IPAQ) [21].
Initial Evaluation. Baseline information included: age, gender, mass, height and body mass index (BMI). Mass was determined using a digital scale (W 200/5, Welmy, Brazil) with a precision of 0,1 kg. Height was established using a stadiometer (ES 2020, Sanny, Brazil) with a precision of 0,1 cm and with 220 cm of extension. BMI was calculated as
Figure 1. The exercise protocol was composed by the following exercise with both arms on three positions: I) with shoulders at approximately 180° of flexion with the flexible pole on the frontal plane, parallel to the ground (Figure 1A), II) with the shoulders on 90° of flexion with the flexible pole on the transverse plane (Figure 1B), and III) one shoulder at 90° of flexion with the flexible pole on the sagittal plane, perpendicular to the ground (Figure 1C).
mass / height2, with mass in kilograms and height in metres.
Exercise with flexible pole. The flexible pole is an apparatus with a mass of 0,8 kg and of approximately 150 cm length. The flexible pole provides oscillations induced by movements of the upper limbs. Exercise protocols using the flexible pole have been proven to present positive results in shoulder muscle function training [22].
The flexible pole exercises were undertaken (Figure 1) with volunteers at standing position with feet apart (wide base) and shoulder flexion as the proposed position. To maintain the proper shoulder flexion in each upper limb it was used as a target visual feedback with a metronome to oscillate the pole (5 Hz). All exercises were performed for 15 seconds with 50 to 60 seconds of rest between each exercise. Three repetitions were performed for each exercise [11].
HRV analysis. We enforced procedure from Task Force guidelines [23]. Instantaneous RR intervals (RRi) were recorded with a digital telemetry system (Polar® RS800CX; Polar Electro Oy, Kempele, Finland). This system detected ventricular depolarization, corresponding to the R wave on the electrocardiogram, at a sampling rate of 1000 Hz, providing a temporal resolution of 1 millisecond for each RR interval and was previously validated [24]. The Polar heart rate device consisted of an elastic band and two electrodes worn by the volunteer around the chest, at the level of the xiphoid process just below the pectoralis muscles according to the manufacturer guidelines. To ensure proper signal detection, water was placed on the front two electrodes of the chest strap. RR intervals were then downloaded to the Polar Precision Performance program (v. 3.0, Polar Electro, Finland). The software enabled the visualization of HR and the extraction of a cardiac period (RR interval) file in "txt" format. Following digital filtering complemented with manual filtering for the elimination of artefacts, which were replaced by linear interpolation of adjacent beats, 500 (short-term) stable RR intervals were applied for the data analysis. Only series with more than 95% sinus rhythm were included in the study [23, 25].
Befor exercise
After exercise
1000 800 600 400 200 0
1000 800 600 400 200 0
-1 J* *
200
400
600
800
1000
200
400
600
800
1000
Figure 2. Visual pattern of Poincare plot observed in one subject before exercise and after exercise.
HRV was analysed in the following periods: control protocol — the 10-minute period before the performance of the exercises and the 10-minute period after the performance with flexible pole — the recovery phase.
Protocol. Data collection was undertaken in the same soundproofed room for all volunteers. The temperature was between 21°C and 25°C and, the relative humidity was between 50% and 60%. Volunteers were instructed not to consume alcohol, caffeine or other ANS stimulants for 24 hours before the evaluation. Data was collected on an individual basis, always between 18:00 and 21:00 to standardize circadian influences. All procedures necessary for the data collection were explained to each subject separately, and the subjects were instructed to remain at rest and avoid talking during the collection.
Poincare plot. The plot was qualitatively analysed by HRV analysis software based on the figures formed by its attractor. The expected shapes were described by Tulppo et al [26] as:
1) Figures in which an increase in the dispersion of RR intervals is observed with increased intervals, characteristic of a normal plot.
2) Small figures with beat-to-beat global dispersion without increased long-term dispersion of RR intervals.
Shannon Entropy. Entropy is a benchmark of the disorder in dynamical systems, a statistical complexity measurement derived from information theory. Generally, entropy as a measure of lack of knowledge is useful in many situations. For conditions where the connection with physical temperature is unimportant, the Boltz-mann's constant can be removed. This normalisation gives us the Shannon entropy [27].
Entropy-based techniques are routinely employed in analysis of medical data especially cardiovascular [29, 30], respiratory [31, 32] and neurological signals [33, 34]. A low entropy dataset is highly predictable — whereas a high
entropy dataset is less predictable. Accordingly, high entropy is more disordered. All entropies are numerically expressed between zero and unity with zero being the lowest disorder.
In contrast to Tsallis and Renyi entropies (see below); Shannon entropy is additive. Consequently, if the probabilities can be factorised into independent factors, the entropy of the joint process is the sum of the entropies of the separate processes.
Multiscale Renyi Entropy. The order-q Renyi entropies are a series of entropy like quantities. Here we set the value, entropic order, a to 0.25, 0.35, 0.45, 0.55, 0.65, 0.75. Where a=1 the function is the Shannon entropy and when a=2 it is the squared entropy. When a is varied this provides the multi-scale measure; a=0 is simply the logarithm of n. As a is increasing the measures become more sensitive to the values occurring at a higher probability and less to those at a lower probability.
Multiscale Tsallis Entropy. Tsallis entropy is a generalization of the standard Shannon-Boltzmann-Gibbs entropy. It was introduced as a basis for generalizing the standard statistical mechanics. Here we set entropic index, q to 0.25, 0.35, 0.45, 0.55, 0.65, 0.75. Where q=1 it is the Shannon-Boltzmann-Gibbs entropy.
Approximate Entropy. Approximate entropy is the logarithmic ratio of component wise matching sequences from the signal length, N. Other relevant parameters involve r which we set to 0.2 of the standard deviation based on factors of the signal that is being analysed and compared to. The factor m, is the length of sequences compared which we set to window of 2. It is measured as an integer count of discrete time bins. A minimum value of zero for Approximate entropy would indicate a fully predictable series. Approximate entropy is described algorith-mically in Hornero et al [35].
Sample Entropy. It is important to consider Approximate entropy and Sample entropy together as similar mathematical functions. Comparisions with fixed m, r, and N. N is the length of the time series and m is the length of the sequences to be compared whereas r is the tolerance for accepting matches. As with Approximate entropy in this study we set r to 0.2 of the standard deviation. The factor m, is the length of sequences compared which we set to window of 2. Again the algorithm for Sample entropy is discussed in Hornero et al [35].
Results
The visual analysis through the Poincare plot illustrated that RR intervals dispersion reduced immediately after exercise with flexible pole (Figure 2).
Normalization of the data is required to decide the necessary statistical test of significance to apply. Here, we applied the Anderson-Darling [36] and Lilliefors tests [37]. The Anderson—Darling test for normality applies an empirical cumulative distribution function. The Lilliefors test is suitable when the number of subjects is low. Here, there are only 32 subjects in each cohort. The results from both tests
0
0
Table 1
Mean values, standard deviation and p-value of significance for the five entropic measures
Entropic Parameter Mean ± SD Mean ± SD ANOVA1 Kruskal-Wallis
Pre (n=32) Recovery (n=32) (p-value) (p-value)
Approximate 0,9091±0,0779 0,8346±0,2294 0,0871 0,2243
Sample 0,8329±0,1111 0,6568±0,1959 <0,0001* <0,0001*
Shannon 0,7612±0,1089 0,7286±0,1200 0,2595 0,1379
Renyi a=0,25 0,9922±0,0039 0,9910±0,0043 0,2215 0,1452
a=0,35 0,9900±0,0050 0,9884±0,0056 0,2209 0,1415
a=0,45 0,9881±0,0059 0,9861±0,0067 0,2204 0,1415
a=0,55 0,9864±0,0068 0,9842±0,0075 0,2199 0,1415
a=0,65 0,9850±0,0075 0,9825±0,0084 0,2195 0,1415
a=0,75 0,9837±0,0081 0,9810±0,0091 0,2190 0,1379
tsallis q=0,25 0,7870±0,0980 0,7574±0,1082 0,2551 0,1379
q=0,35 0,7864±0,0983 0,7567±0,1085 0,2551 0,1379
q=0,45 0,7853±0,0987 0,7555±0,1090 0,2552 0,1379
q=0,55 0,7836±0,0995 0,7536±0,1098 0,2553 0,1379
q=0,65 0,7811±0,1006 0,7507±0,1110 0,2557 0,1379
q=0,75 0,7774±0,1021 0,7466±0,1127 0,2563 0,1379
Annotation: the table below shows the mean values, standard deviation and p-value of significance for the five entropic measures for normal subjects and subjects recovering from flexible pole exercises related to RR-intervals. the number of RR-intervals was 500; and after tests of normality ANOVA1 and Kruskal-Wallis tests of significance were applied. For Multiscale Renyi and Multiscale tsallis entropy the values we calculated were for six values of entropic order and entropic index. For Approximate entropy and Sample entropy (m=2 and r=0,2). Both statistical tests are significant for Sample Entropy at (p<0,0001); where * is highly significant.
were inconclusive. Accordingly, both the parametric oneway analysis of variance; (ANOVA1) and the non-parametric Kruskal-Wallis [38] tests of significance must be applied. Dissimilarities would be considered weakly significant when the probability of a type I error was less than 5% (p<0,05). Further significance is achieved at the level of the probability of a type I error was less than 1% (p<0.01) (Table 1).
Discussion
For cardiovascular and, HRV responses in particular, flexible pole exercise performance has been explored previously in the time and frequency domain indices [10, 11]. Traditional linear analysis of HRV did not find statistically significant responses induced by flexible pole exercise in women, suggesting well-being and safety when performing this exercise [7, 10].
Here, we investigated HRV through entropic analysis and reported the presence of levels chaotic behavior of HRV before and immediately after a 10 minute session of exercise with the flexible pole. Statistical significance was achieved with the Sample entropy algorithm. Since there is only one significant parameter the multivariate techniques [39] applied in similar studies [40] is not required. In this context, our findings do not support the cardiac autonomic safety of flexible pole exercise as previously recommended based on linear HRV indices [8, 10].
This measurement is useful for assessments of the intensity of physiotherapy and rehabilitative treatment required in such patients; and their future susceptibility to cardiovascular irregularities and "dynamical diseases" when undergoing the protocol.
Sample entropy presented in our study is proposed to quantify the entropy rate of short- to mid-length RR inter-
vals, it indicates the complexity of HRV. Small values of Sample entropy are associated to more regular, predictable, processes. Conversely, the greater the complexity, the more physiologically adapted the organism [41].
According to our results, the Sample entropy has decreased when recovering from flexible pole exercises, indicating reduced chaotic behavior during this phase.
This technique which can assess the level of chaotic response to exercise is useful in determining the intensity of the physiotherapy intervention and the risk from cardiovascular pathology and "dynamical diseases" generally in subjects undergoing the protocol.
Contrariwise, Shannon, Multiscale Renyi, Multiscales Tsallis and Approximate entropies measurements were not significantly changed by flexible pole exercise.
Previous investigations also examined HRV responses to the same exercise protocol with flexible pole. De Oliveira et al [7], investigated HRV 30 minutes after exercise with flexible pole. Time and frequency domain and geometric indices of HRV were not significantly changed compared to control rest before exercise.
Dos Santos et al [6], failed to find significant responses of time and frequency domain indices of HRV in women submitted to a single session of exercise with flexible pole. The authors analyzed the initial 60 minutes during recovery from exercise.
However, Ogata et al [5], reported significant heart rate dynamics change induced by flexible pole exercise in healthy men. A decline was reported in parasympathetic heart rate modulation immediately after the exercise protocol, which continued for the initial 10-15 minutes during recovery from exercise.
We may postulate that the difference between studies in men [5] and women [6, 7] is due to increased muscle mass
in men, which may be involved in more intense mechan-oreflex responses.
Our data suggests a diminished complex response of heart rate dynamics followed flexible pole exercise, which were not previously detected by linear indices of HRV [6, 7, 10]. In this way, nonlinear HRV evaluated through global chaotic analysis displayed reduction in chaotic behavior immediately after exercise with flexible pole [11], which reinforces our outcomes. Further studies are necessary to investigate the chronic effects of this exercise protocol.
Application of flexible pole exercises has been extensively applied in rehabilitation and physiotherapy clinics. Despite that, there are few scientific studies supporting its effectiveness. Knowledge of the feasibility of its use can provide greater safety in patients with neurological, cardiac and metabolic disorders undergoing the treatment.
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The application of the protocol in young adults and the lack of research in this area could be a opinion addressed in this study. However, we evaluated only females in order to avoid gender related effects.
Conclusion
An acute session of exercise with a flexible pole reduced the chaotic behavior of heart rate dynamics assessed through the technique Sample Entropy. We suggest that clinicians take care when using this treatment protocol in patients presenting with cardiovascular disorders.
Acknowledgements. The authors declare that there is no conflict of interests regarding the publication of this article. The study received financial support from FAPESP (number 2012/09043-1).
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