Effect of stress on cardiorespiratory synchronization of Ironmen athletes
Maia Angelova, Philip M. Holloway, Sergiy Shelyag, Sutharshan Rajasegarar, H.G. Laurie Rauch
aa r X i v : . [ q - b i o . Q M ] F e b Effect of stress on cardiorespiratory synchronization ofIronmen athletes
Maia Angelova ∗ , Philip M. Holloway , Sergiy Shelyag , Sutharshan Rajasegarar ,and H.G. Laurie Rauch D2I Research Centre, School of IT, Deakin University, Geelong, Victoria, Australia Northumbria University, Newcastle Upon Tyne, UK Department of Human Biology, Faculty of Health Sciences, University of CapeTown, Cape Town, South AfricaFebruary 4, 2021
Abstract
The aim of this paper is to investigate the cardiorespiratory synchronization in athletes sub-jected to extreme physical stress combined with a cognitive stress tasks. ECG and respirationwere measured in 14 athletes before and after the Ironmen competition. Stroop test was ap-plied between the measurements before and after the Ironmen competition to induce cognitivestress. Synchrogram and empirical mode decomposition analysis were used for the first timeto investigate the effects of physical stress, induced by the Ironmen competition, on the phasesynchronization of the cardiac and respiratory systems of Ironmen athletes before and after thecompetition. A cognitive stress task (Stroop test) was performed both pre- and post-Ironmanevent in order to prevent the athletes from cognitively controlling their breathing rates. Ouranalysis showed that cardiorespiratory synchronization increased post-Ironman race comparedto pre-Ironman. The results suggest that the amount of stress the athletes are recovering frompost-competition is greater than the effects of the Stroop test. This indicates that the recoveryphase after the competition is more important for restoring and maintaining homeostasis, whichcould be another reason for stronger synchronization.
An Ironman race is a long-distance triathlon consisting of a 2.4-mile swim, a 112-mile bicycle rideand a 26.2-mile marathon run raced in that order with no break in between sections. It takesa participant a long time to recover from the physiological stress of completing an Ironman race.Such heavy exertion undoubtedly has a negative effect on the body’s immune system with sustainedinflammatory response to muscle fatigue, increased risk of respiratory tract infections, weight loss,and other medical conditions (Ren and Zhang, 2019). In this study we measured the effect of extremephysical stress on the cardiorespiratory system of athletes, while they performed a cognitive test toprevent them from cognitively controlling their breathing rates. ∗ Corresponding author. e-mail: [email protected]
1e studied the performance of the cardiorespiratory system by investigating the effects of theIronmen competition on cardiorespiratory synchronization. The cardiovascular and respiratory sys-tems are coupled by several mechanisms two di(Berne et al., 1998; Ren and Zhang, 2019), wherethe interactions between these two systems involve a large number of feedback and feed-forwardmechanisms. In healthy subjects, the heart rate increases during inspirations and decreases withexpiration, which is the well-known, and well-studied phenomena (Anrep et al., 1936) respiratorysinus arrhythmia (RSA). However, for cardiorespiratory system, it is unlikely to find continuous syn-chronization, as the respiration is neither the governing mechanism, nor is the only system affectingthe heart rate dynamics. In fact, synchronizations is only expected to be observed either when oneof the systems is forced (i.e., by controlled breathing) or when synchronizations are necessary forthe regulation of homeostasis (i.e. after events that induce stress in one or both of the systems).Earlier studies of cardiorespiratory synchronization support its existence (Pokrovskii et al., 1985;Wu and Hu, 2006; Bartsch et al., 2007; Rosenblum et al., 2004; Angelova et al., 2017) and as shownin (Bartsch et al., 2012) cardiorespiratory synchronization and RSA represent different aspectsof the interaction between the cardiac and respiratory systems. Cardiorespiratory synchroniza-tions were shown to exist in humans during rest (Schafer et al., 1998; Lotric and Stefanovska, 2000;Stefanovska et al., 2001; Ren and Zhang, 2019), Zen meditation (Cysarz and Bussing, 2005), Dharma-Chan meditation (Chang and Lo, 2013). Desynchronizations were reported following myocardialinfarctions (Leder et al., 2000; Hoyer et al., 2002), as well as reduced cardiorespiratory coordinationwith obstructive sleep apnoea (Kabir et al., 2010) and acute insomnia (Angelova et al., 2020).As with most physiological time series, when investigating the coupling of the cardiorespiratorysystems, noise will occur. This noise originates not only from measurements and external distur-bances, but also from the fact that there are other subsystems that take part in the cardiovascularcontrol (Stefanovska and Bracic, 1999; Angelova et al., 2020). These influences, when consideringcardiorespiratory synchronizations, are also considered as noise.Cognitive stress is known to affect the physiological functioning of the cardiovascular systemsuppressing heart rate variability (HRV) (Hansen et al., 2003; Wood et al., 2002; Ren and Zhang,2019). In physiology, HRV is the variation in time interval between heartbeats, measured by thevariation in the beat-to-beat interval (Hon and Lee, 1965). Raschke et al. suggested that coordina-tion between the cardiac and respiratory systems would be at its strongest during states of relaxationand stated that this coordination was easily disturbed under conditions of stress or disease (Raschke,1987). However, there is little knowledge of the effect that cognitive stress exerts on cardiorespira-tory synchronizations and no study thus far has investigated neither the effect of extreme physicalstress on cardiorespiration, nor of cognitive stress during or after an extreme physical stress suchas the Ironmen competition. In this study, the participants were asked to complete a Stroop testin order to impose stress and draw attention away from consciously controlling one’s breathing andinstead focus on completing the task. Our hypothesis is that we will see a decrease in the amountof synchronization during the Stroop test, due to the physical stress of the Ironman event.After the first Stroop test, the participants completed the Ironmen competition, after which asecond Stroop test was administered. Thus, we observed the effect of the extreme physical taskon the concentration and cognitive abilities. Coordination between the cardiorespiratory systemshas been reported in healthy adults (Lotric and Stefanovska, 2000; Kotani et al., 2002), athletes(Schafer et al., 1998, 1999) as well as in sleeping humans (Cysarz et al., 2004a; Bartsch et al., 2007).A high degree of synchronization was reported for subjects during meditation with very little coor-dination seen during spontaneous breathing (Cysarz and Bussing, 2005). Raschke et al. (Raschke,1987) suggested that coordination between the cardiac and respiratory systems would be at itsstrongest during states of relaxation and reported strong coordination between the cardiorespira-tory subsystems during sleep, (Sweeney-Reed and Nasuto, 2007), also stating that this coordination2as easily disturbed under conditions of stress or disease. Kabir et al. showed a reduction inphase coupling in patients with severe obstructive sleep apnoea (OSA) compared with mild OSA,synchronization levels also seemed to correlate with sleep stages (Kabir et al., 2010).Although neither the underlying mechanisms governing the coordination nor the physiologi-cal significance of such results is understood, its quantification could prove to have clinical merit,e.g. estimating the prognosis of cardiac diseases in patients having suffered myocardial infarctions(Hoyer et al., 2002; Leder et al., 2000).In this study, we nvestigate the effects of extreme physical stress on cardiorespiratory synchro-nizations using the concept of phase locking with synchrogram and Empirical Mode Decomposition(EMD) analysis.We apply synchrogram analysis and EMD to respiration (RR) and electrocardiogram (ECG) timeseries in order to find a mode that encapsulates the key features of the original signal. The phase ofthis mode is calculated via the Hilbert transform and is compared with the phases from all modes ofthe corresponding ECG signal, after which the synchronization analysis is carried out. Specifically,we analyse the RR and ECG signals of Ironman athletes before and after the Ironmen race, when aStroop test is administered. The ECG and RR data are taken before and after the athletes performa Stroop test. Our results consistently illustrated a rise in synchronizations after the competition.Furthermore, we evaluate the control effect on synchronizations, we expect to see an increase insynchronizations between the cardiorespiratory systems after the race due to an increased breathingrate, to which the heart adjusts its rhythm to beat at an equal rate (Pokrovskii et al., 1985). Inboth scenarios cardiorespiratory systems are trying to maintain homeostasis.The paper is organised as follows. Section 2 introduces the experimental settings and data collec-tion methods. Section 3 considers the techniques applied for analysing the cardio- and respiratorytime series data , followed by the results and discussion in Section 4 and final conclusions in Section5.
The study investigated 14 Ironmen athletes before and after the race. The physical performance ofthe athletes was judged by the synchronization of the cardio- and respiratory systems. This wasmeasured by taking the ECG and RR signals and studying their synchronization using time seriesanalysis. Stroop test was administered before and after the race to induce cognitive stress. Stroopmistakes were counted as a measure of cognitive performance.Simultaneous ECG and RR signals were recorded from all participants for two settings: oneduring a Stroop test before and one after the Ironman race.To remove the noise, we applied a zero-phase filter to the time series signals using an IIR filter.The IIR filter has an 4th order and the cutoff frequency is 0.4.
Cognitive stress is known to affect the physiological functioning of the cardiovascular system sup-pressing heart rate variability (HRV) (Hansen et al., 2003; Wood et al., 2002). In physiology, HRVis the variation in time interval between heartbeats, measured by the variation in the beat-to-beatinterval (Hon and Lee, 1965). Raschke et al. suggested that coordination between the cardiac andrespiratory systems would be at its strongest during states of relaxation and stated that this coor-dination was easily disturbed under conditions of stress or disease (Raschke, 1987).3he Stroop effect is a demonstration of interference in the reaction time of a test (Stroop, 1935).Essentially, the name of a colour, e.g. “red”, is printed in a colour not denoted by the name. Theexample in Fig. 1 shows the word “blue” printed in the colour red, and the word ”red” printedin colour ”green”. Naming the colour of the word takes longer and is more prone to errors thanwhen the colour of the word and the name of the colour match. The Stroop test was appliedin this study in order to turn the participants’ attention away from consciously controlling theirrespiration depth and rate and instead to focus on completing the task at hand. In doing this, theunconscious, homeostatic mechanisms can be investigated and their influence on cardiorespiratorysynchronizations found. !"
Figure 1: Example of a Stroop test. Participants were required to state the colour of the wordinstead of reading the word. For example the first line would be red, blue, red.In this study, the participants were asked in the first stage to complete a Stroop test - in orderto impose stress and draw attention away from consciously controlling one’s breathing and insteadfocus on completing the task – pre Ironman event; and in the second stage they completed a Strooptest post Ironman event.We expect to see a decrease in the amount of synchronization during the Stroop test, due to theextreme physical stress.
ECG was measured with three electrodes, positioned in Einthoven’s triangle configuration, andrecorded at 1000Hz. ECG and RR time series were recorded continuously using AcqKnowledgesoftware (version 2.1). The signals were pre-processed using Matlab in order to extract the R-peaks.As the time series were noisy and strongly non-stationary, EMD was implemented to decomposeand reconstruct the respiration signal free of noise. The respiratory signal was recorded via a forcetransducer fixed to a belt around the chest. Subjects were asked to expel air from their lungs as thetransducer was first fit, and then were instructed to breathe normally. ECG and RR signals wererecorded simultaneously for 6 minutes - 1 minute prior to a Stroop test and five minutes during thetest. The descriptive statistics of each individual in the study is given in Table 1. All individuals4ad to perform two Stroop tests, one before and one after the Ironman event.Table 1: Descriptive statistics of 14 Ironman athletes. Means and standard deviations are shown inthe bottom rows.
ID Age Gender Height(m) Weight(kg) BMI Fit (h/wk) Race Time(mins) Recovery(mins)1 36 M 1.85 74 21.6 14 621 722 43 M 1.79 71 22.2 17 638 753 M 675 974 35 M 1.72 66 22.3 20 695 595 30 M 1.83 69 20.6 15 712 1066 40 M 1.68 72 25.5 19 738 937 44 M 1.92 92 25 20 759 708 35 M 1.74 68 22.5 8 769 989 43 M 1.7 72 24.9 13 775 11210 21 M 1.91 79 21.7 9 784 20011 31 F 1.65 58 21.3 15 803 10912 30 M 1.84 75 22.2 15 809 12013 39 M 1.77 73 23.3 15 870 13214 21 M 1.82 67 20.2 25 948 142Mean 34.5 1.79 72 22.55 15.77 756.85 106.04STD 7.67 0.09 7.88 1.67 4.59 88.12 36.19Med 35 1.81 72 22.16 15 764 102
Statistical analysis was performed on the data for 14 Ironmen using the package R. The arithmeticmean (mean), standard deviation (STD) and the median (Med) of the eight variables: Age in years,Gender, Height in meters, Weight in kilograms, Body Mass Index (BMI), Fitness(Fit) in hours perweek, Race Time in minutes and Recovery time in minutes are given in Table 1.Pearson correlation coefficients were computed for all pairs of variables. There was no linearcorrelation between variables, except some correlation between Race time and Recovery (0.6198),Age and Recovery (-0.6998), and Age and BMI (0.7949). The distribution of Recovery shows thatonly two athletes have a Recovery time significantly higher than the average - (A)
Race time in minutes F r equen cy
600 700 800 900 1000 (B)
Recovery in minutes F r equen cy
50 100 150 200 (C)
Fitness in hours per week F r equen cy (D) BMI F r equen cy
20 21 22 23 24 25 26
Figure 2: Histograms of (A) Race and (B) Recovery time, (C) Fitness and (D) BMI for the athletes.
The synchronization is a basic phenomenon in nature (Rosenblum et al., 1996; Pikovsky et al., 1997;Cysarz et al., 2004b; Rosenblum et al., 2004). Through the detection of synchronous states we maybe able to achieve a better understanding of physiological functioning. In the classical sense ofperiodic, self-sustained oscillators, synchronization is usually defined as the locking (entrainment) ofthe phases with a near constant phase difference that persists over time: φ n,m = n Φ − m Φ = const (1)where n and m are integers, Φ , Φ are phases of the two oscillators and φ n,m is the generalizedphase difference (Tass et al., 1998). In such cases, the n : m phase locking demonstrates itself as avariation of φ n,m around a horizontal plateau. We will use the length of this plateau as a measureof synchronization.The phase φ ( t ) is easily estimated from any scalar time series. A problem arises if the signalcontains multiple component or time-varying spectra, thus making phase estimation difficult. The6MD method overcomes this as it breaks a signal down into a finite set of components for whichthe instantaneous phase can be defined.As with most physiological time series, when investigating the coupling of the cardiorespiratorysystems, noise can occur. This noise originates not only from measurements and external distur-bances, but also from the fact that there are other subsystems that take part in the cardiovascularcontrol (Stefanovska and Bracic, 1999). These influences, when doing synchronization analysis, arealso considered as noise. To study the phase synchronization of the cardiorespiratory system we use Hilbert-Huang Transform(HHT) (Huang et al., 1998; Huang and Attoh-Okine, 2005; Huang and Wu, 2008). It is superior tothe Fourier-based methods, which are the simplest and most popular methods of decomposing a sig-nal into energy-frequency distributions. The Fourier methods lose track of time-localised events andare proven ineffective when analysing physiological systems with non-stationary processes. A popu-lar alternative to Fourier methods is wavelet analysis. It overcomes problems with non–stationarity,however, due to the use of single, basic wavelet is non-adaptive and therefore needs to be appliedwith care to non-linear data. HHT is used in order to analyse non-linear and noisy signals as itdescribes them more locally in time. HHT is also capable of measuring instantaneous frequency andphase, which makes it particularly suitable for physiological time series. Hilbert-Huang transformapplies Hilbert transform to intrinsic mode functions obtained from the EMD decomposed signals.In the standard Hilbert Transform (HT), y i , can be written for any function x i as follows, y i = 1 π P Z ∞−∞ x i ( t ′ ) t − t ′ dt ′ , (2)where P indicates the Cauchy principal value. Gabor et al. determined that an analytical functioncan be formed with the HT pair (Gabor, 1946), z i ( t ) = x i ( t ) + i y i ( t ) ≡ A i ( t ) e i φ i ( t ) , (3)with amplitude A i ( t ) and instantaneous phase φ i ( t ), A i ( t ) = q x i ( t ) + y i ( t ) , (4) φ i ( t ) = tan − (cid:16) y i ( t ) x i ( t ) (cid:17) . (5)The instantaneous frequency can be presented as the time derivative of the phase, ω = dφ i ( t ) dt . (6)When determining the instantaneous phase, an assumption is made that the system studied canbe modelled as weakly-coupled oscillators (Stefanovska and Bracic, 1999). We also assume thattheir interactions can be investigated by analysing such phases (Kuramoto, 1984). We shouldnote that the Hilbert transform is not the only method to estimate phase relationships, this canalso be done by using wavelet transform or marked events methods (Stefanovska and Bracic, 1999;Le Van Quyen et al., 2001; Clemson and Stefanovska, 2014). Another main advantage of the HT isthat it can find the phase of a single oscillation directly.7 .4 Synchrograms In 1998, Schafer et al. developed the cardiorespiratory synchrogram in order to analyse n : m synchronizations in the cardiorespiratory systems, where the heart beats n times in m respira-tory cycles (Schafer et al., 1998, 1999). The synchrogram analysis is very effective to study phasesynchronization between a point process (heartbeat) and a continuous signal (respiration). Thistechnique has been used to look at synchronizations in infants (Mrowka et al., 2000), in adults dur-ing poetry recitation (Cysarz et al., 2004b) and desynchronizations following myocardial infarction(Leder et al., 2000). A high degree of synchronization was reported for subjects during medita-tion with very little coordination seen during spontaneous breathing (Cysarz and Bussing, 2005).Bartsch et al (Bartsch et al., 2012) used the synchrogram method to investigate the response ofcardiorespiratory synchronization to changes in physiological states through sleep.After cleaning the signal with a low pass filter, Matlab code was employed to the respiratorysignal, to detect R-peaks from the ECG time-series. The Hilbert transform was used to calculatethe instantaneous phase of the respiration signal Φ nr from (14). We then considered the respiratoryphase at times t k - the r -peak of the k th heartbeat. The cardiorespiratory synchrogram can beconstructed by observing the phase of the respiration at each t k , and wrapping the phase into a[0 , πm ] interval. In the simplest case of n : 1 synchronization, there are n heartbeats in eachrespiratory cycle. Plotting these relative phases Ψ n, as a function of time against t k , we observe n horizontal lines (representing the number of heartbeats) in one respiratory cycle. This is illustratedin Fig. 3. The relative phase is given by,Ψ n,m ( t k ) = 12 π [Φ nr ( t k ) mod 2 πm ] . (7) As it has been noted above, HHT consists of two stages in order yo analyse a time series. The firststage, EMD, decomposes a time series into a set of simple oscillatory functions, defined as intrinsicmode functions (IMFs). Typically, an IMF is a function that fulfills the following: • In the entire dataset, the number of extrema and the number of zero-crossings must be eitherequal or differ by at most one • At any point, have a mean value of zero between its local maxima and minima envelopes.The IMF components are obtained by applying an iterative technique known as ’sifting’, this processis as follows:1. Localize all the local maxima in the time series ( x ( t )) and connect them with a cubic spline,this is the upper envelope. Repeat the procedure with the local minima defining the lowerenvelope.2. Calculate the mean of the upper and lower envelopes m ( t ) and determine the first componentby subtracting the mean from the original time series x ( t ). c ( t ) = x ( t ) − m ( t ) (8) • if the condition for an IMF are met then the component c ( t ) is an IMF.8igure 3: An example of how the cardiorespiratory synchrogram works. On the top is the respiration,in the middle the corresponding ECG signal and at the bottom, the formation of the synchrogram.The position of each heartbeat in relation to its appearance in the phase of the respiratory cyclescan be clearly seen. Red broken vertical lines indicate picks in the heart beats and black verticalbroken lines indicate one second interval. This example illustrates n : 1 synchronization.9 if the conditions are not met, repeat the process from step 1 until an IMF is found.3. Finally, subtract the IMF component from the original time series to find the residue, r ( t ). r ( t ) = x ( t ) − c ( t ) (9)4. Repeat the sifting process using r ( t ) as the new time series.5. Continue this process until all of the intrinsic modes ( c i ) are found. This process can beterminated when the n th residue is a monotonic function that doesn’t present any extremaand no more IMFs can be extracted. This last residue is called the trend of the data. It isimportant to note that any residue constitutes a trend for the previously extracted oscillation.i.e. r i is the trend followed by the c i oscillation.After this procedure it is possible to express the original data in terms of the obtained IMFs, x ( t ) = N X i =1 c i ( t ) + r n ( t ) (10)Orthogonality of the EMD is not guaranteed theoretically, but is satisfied in a practical sense asthe IMFs are orthogonal within a certain period of time. In this sense the process only ensures timelocalized orthogonality.The instantaneous phase can be calculated by applying the Hilbert transform to each IMF, c i ( t ).The procedures of the Hilbert transform consist of calculation of the conjugate pair of c i ( t ), i.e., y i = 1 π P Z ∞−∞ c i ( t ′ ) t − t ′ dt ′ , (11)where P , as in equation (2), indicates the Cauchy principal value. With this definition, two functions c i ( t ) and y i ( t ) forming a complex conjugate pair, define an analytic signal z i ( t ): z i ( t ) = c i ( t ) + i y i ( t ) ≡ A i ( t ) e i φ i ( t ) , (12)with amplitude A i ( t ) and the instantaneous phase φ i ( t ): A i ( t ) = q c i ( t ) + y i ( t ) , (13) φ i ( t ) = tan − (cid:16) y i ( t ) c i ( t ) (cid:17) . (14)An illustration of EMD decomposition into EMFs is given on Fig. 4. EMD was applied tocorresponding ECG and RR time series in order to find the synchronized modes. The underlyingtheory is that in the decomposition of the ECG, lies an IMF (or set of IMFs) that describe theinfluence that respiration has on the dynamics of the heart. Once the IMFs for both time serieshave been found, a particular IMF is found from the decomposition of the respiration signal whichcontains the key features of the original signal, while neglecting the faster oscillations as noise. Thedecomposition in IMFs is illustrated on Fig. 4. The Hilbert transform is applied to this mode as wellas all the other modes from the ECG signal and the instantaneous phases, φ i ( t ) , are calculated with(14). A vector matrix is constructed showing the phase differences between the respiration IMF and10 c -0.0500.05 c -0.100.1 c -0.100.1 c -0.100.1 c -0.0500.05 c -0.100.1 c -0.100.1 c -0.0500.050.1 c -0.200.1 c -0.300.1 c -0.100.1 c Time (sec) -0.0400.04 c SignalTrendIMFs
Figure 4: Illustration of EMD decomposition into IMFs for athlete
The steps of data processing were done with as follows:1. EMD was applied to corresponding ECG and RR signals taken from athletes performing aStroop test both before and after the Ironman competition. The number of sifting timesdepends on the data quality, and it varies case by case. Since the ECG and RR signals werepre-processed to a sampling rate of 100Hz, we set the sifting time as 100.2. Visually the resulting IMFs decomposed by the EMD were inspected. If the amplitude of acertain model is dominant and the wave form is well distributed, the data are said to be welldecomposed and the decomposition is successfully completed. Otherwise, the decompositionmay be inappropriate, and we have to repeat step (1) with different parameters.
The cardiorespiratory synchrograms were calculated for each athlete pre- and post-Ironman race forone and two respiration cycles, m = 1 ,
2. Exemplary synchrograms for one athlete n : m ratio has increased to 4:1.The synchrograms for athlete Here we compute the phase difference with n : m as n : 1 and n : 2 for all athletes and using HHTto determine the instantaneous phase difference (5). The lengths of the plateaus, determined by12igure 5: Phase differences between one IMF (IMF5) from respiration decomposition and severalIMFs from the ECG decomposition (athlete n : m where plateaus were observed for n = 2 , , ..., m = 1 ,
2. Out of the 14 participants,only one ( p -value for Spearman rank correlation are presented in Table 3.As is evident from the data presented in the table, there are no strong linear correlations betweenthe parameters of the athletes and their synchronization times. The strongest Spearman correlationof 0.53 with the p -value of 0.06 is observed in weight - post-competition synchronization time param-eter pair. Therefore, it can be concluded that physiological properties of each tested individual donot play a significant role in synchronization levels. It should be, however, noted that participatingin Ironman competition already includes some implicit pre-selection.Figure 11 displays box plots for the cardiorespiratory synchronization times both before andafter the Ironman race. The figure shows a clear difference with the synchronization times beingsignificantly higher post-race with a p -value of 0.009.These results led to the conclusion that the synchronization is stronger post-competition.15igure 8: The cardiorespiratory synchrogram for an athlete A new method for visualising the synchronizations between the cardiorespiratory system was pro-posed through the implementation of EMD and HHT. The moving variance also allows quantificationof the stability of these synchronized regions.Strong synchronizations were observed in the Ironman athletes post-competition, these periodswere significantly longer and more pronounced than the synchronized regions witnessed prior to thecompetition for 13 out of the 14 athletes. Although the Stroop test was impeding any consciousefforts to regulate the cardiorespiratory systems, unconsciously the body’s need to recover home-ostasis after the race meant that the control mechanisms are still working to regulate the heart andbreathing rates - in order to restore them to a normal rate.The Ironman competitors displayed the highest levels of synchronization during periods whentheir body’s were recovering from a state of stress. This is contrary to our hypothesis because theathletes showed longer, more stable periods of synchronization, presumably partly due to a superiorlevel of fitness and respiratory control.Another factor to consider is the amount of stress the individuals are recovering from, for examplethe effects of an Ironman competition are far greater than those of a single Stroop test. Therefore therecovery phase after competition is much more important for restoring and maintaining homeostasis.This heightened importance, we believe, is another reason for stronger synchronizations.Finally, seeing such high levels of synchronization in the Ironman athletes after competition -16igure 9: The cardiorespiratory synchrogram for an athlete
MA, PH and LR thank the FP7 research Project Models for Ageing and Technological Solutionsfor Improving and Enhancing the Quality of Life (MATSIQEL), under Grant FP7-PEOPLE-IRSES-247541 and the Medical Research Council of South Africa, the University of Cape Town HarryCrossley and Nellie Atkinson Staff Research Funds for the partial support of this work.
References
Angelova, M., Holloway, P., and Rauch, L. (2017). Investigating the effect of cognitive stress oncardiorespiratory synchronization. In Duarte, S., Gazeau, J.-P., Faci, S., Micklitz, T., Scherer, R.,and Toppan, F., editors,
Physical and Mathematical Aspects of Symmetries , pages 85–91, Cham.Springer International Publishing. 17igure 10: EMD decomposition with data for athlete
ID pre-Ironman post-Ironman Totals2:1 3:1 4:1 5:1 5:2 7:2 9:2 3:1 4:1 5:1 5:2 7:2 9:2 pre post1 - - - - - - 50 - 60 - - - 250 50 3102 45 60 - - - - - - 60 - - 120 - 105 1803 - 160 - - - - - - 250 - - 30 - 160 2804 - 90 20 - - - - 30 150 - - - - 110 1805 - - 45 - - 50 - 45 90 - - 25 - 95 1606 - 30 40 - - - 70 30 120 - - - - 140 1507 - - 180 - - - - - 220 - - - - 180 2208 - 50 60 - - 40 - 75 - - - 30 - 150 1059 - - - 60 - - 20 - - 90 - 130 - 80 22010 - 60 - - - - - 250 - - 80 - - 60 33011 - 90 - - 60 - - - 80 - - 90 - 150 17012 - - 90 - - - - - 80 - 60 - - 90 14013 - 120 40 - - - - 200 - - 30 - - 160 23014 - 40 - - - - 70 45 90 - - - - 110 135Tot 45 700 475 60 60 90 210 675 1200 90 170 425 250 1640 2810
Angelova, M., Karmakar, O., Zhu, Y., Drummond, S., and Ellis, J. (2020). Automated method fordetecting acute insomnia using multi-night actigraphy data.
IEEE Access , 8:74413–74422.Anrep, G., Pascual, W., and Rossler, R. (1936). Respiratory variations of the heart rate. i.–thereflex mechanism of the respiratory arrhythmia.
Proceedings of Royal Society B .Bartsch, R., Kantelhardt, J., Penzel, T., and Havlin, S. (2007). Experimental evidence for phasesynchronization transitions in the human cardiorespiratory system.
Phys. Rev. Lett. , 98.Bartsch, R., Schumann, A., Kantelhardt, J., Penzel, T., and Ivanov, P. (2012). Phase transitions inphysiological coupling.
PNAS , 109:10181.Berne, R. M., Levy, M. N., Koeppen, B., and Stanton, B. (1998).
Physiology . Mosby St, St. Louis.Chang, C. and Lo, P. (2013). Effects of long-term dharma-chan meditation on cardiorespiratorysynchronization and heart rate variability behaviour.
Rejuvenation Research , 16:115–123.Clemson, P. and Stefanovska, A. (2014). Discerning non-autonomous dynamics.
Phys. Reports ,542:297.Cysarz, D., Bettermann, H., Lange, S., Geue, D., and van Leeuwen, P. (2004a). A quantitativecomparison of different methods to detect cardiorespiratory coordination during night-time sleep.
Biomed. Eng. Online , 3.Cysarz, D. and Bussing, A. (2005). Cardiorespiratory synchronization during zen meditation.
Eur.J. App. Physiol. , 95:88–95. 19 arameter Pearson r Spearman ρ p -valuePre Post Pre Post Pre PostAge 0.38 -0.03 0.31 0.29 0.30 0.33Height -0.27 0.46 -0.30 0.34 0.32 0.26Weight 0.07 0.44 -0.23 0.53 0.46 0.06BMI 0.39 0.05 0.39 0.13 0.18 0.67Fit 0.26 -0.30 0.41 -0.24 0.17 0.44Race time 0.33 -0.27 0.26 -0.22 0.39 0.48Recovery -0.27 0.28 -0.19 -0.03 0.53 0.91
Table 3: Pearson and Spearman rank correlation coefficients between the duration of synchronizationperiods pre- and post-competition (Table 2) and the individual parameters of the athletes (Table 1). p -values for Spearman correlations are also shown. Small p -values indicate significant correlations. Pre-Ironman Post-Ironman50100150200250 T i m e s pen t sy n c h r on i s ed ( s e c ) Figure 11: Box plots displaying the times the cardiorespiratory systems spent synchronized pre- andpost-Ironmen race. Outliers are presented by star (*). The length of synchronizations, given by thelength of the plateaus is larger in the post-race indicating that the athlete is more relaxed after thecompetition and shows a better synchronization between cardiac and respiratory systems.20ysarz, D., von Bonin, D., Lackner, H., Heusser, P., Moser, M., and Bettermann, H. (2004b).Oscillations of heart rate and respiration synchronize during poetry recitation.
Am J PhysiolHeart Circ Physiol , 287:579–587.Gabor, D. (1946). Theory of communication.
Proc IEEE Part III , 93:429–457.Hansen, A., Johnsen, B., and Thayer, J. (2003). Vagal influence on working memory and attention.
Int J Psychophysiol , 48:263–274.Hon, E. and Lee, S. (1965). Electronic evaluations of the fetal heart rate patterns preceding fetaldeath: further observations.
Am J Obstet Gynecol. , 87:814–826.Hoyer, D., Leder, U., Hoyer, H., Pompe, B., Sommer, M., and Zwiener, U. (2002). Mutual informa-tion and phase dependencies: measures of reduced nonlinear cardiorespiratory interactions aftermyocardial infarction.
Med. Eng. Phys. , 24:33–43.Huang, N. E. and Attoh-Okine, N. O. (2005).
The Hilbert-Huang transform in engineering . CRCPress.Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C.,and Liu, H. H. (1998). The empirical mode decomposition and the hilbert spectrum for nonlinearand nonstationary time series analysis.
Proc. R. Soc. London. Ser. , 454:903–995.Huang, N. E. and Wu, Z. (2008). A review on hilbert-huang transform: Method and its applicationsto geophysical studies.
Reviews of Geophysics , 46(2).Kabir, M., Dimitri, H., Sanders, P., Antic, R., Nalivaiko, E., Abbott, D., and Baumert, M. (2010).Cardiorespiratory phase-coupling is reduced in patients with obstructive sleep apnea.
PLoS ONE ,5.Kotani, K., Takamasu, K., Ashkenazy, Y., Stanley, H., and Yamamoto, Y. (2002). Model forcardiorespiratory synchronization in humans.
Phys. Rev. E , 65.Kuramoto, Y. (1984).
Chemical Oscillations, Waves, and Turbulence . Springer-Verlag, Berlin,Germany.Le Van Quyen, M., Foucher, J., Lachaux, J., Rodriguez, E., Lutz, A., Martinerie, J., and Varela,F. (2001). Comparison of hilbert transform and wavelet methods for the analysis of neuronalsynchrony.
J. Neurosci. Methods , 111:83–98.Leder, U., Hoyer, D., Sommer, M., Baier, V., Haueisen, J., Zwiener, U., and Figulla, H. (2000).Cardiorespiratory desynchronization after acute myocardial infarct.
Z. Kardiol , 89:630–637.Lotric, M. and Stefanovska, A. (2000). Synchronization and modulation in the human cardiorespi-ratory system.
Physica A , 283:451–461.Mrowka, R., Patzak, A., and Rosenblum, M. (2000). Quantitative analysis of cardiorespiratorysynchronization in infants.
Int J Bifurcat Chaos , 10:2479–2488.Pikovsky, A., Rosenblum, M., Osipov, G., and Kurths, J. (1997). Phase synchronization of chaoticoscillators by external driving.
Physica D , 104:219–238.21okrovskii, V., Abushkevich, V., Dashkovskii, A., and Shapiro, S. (1985). A possibility to controlthe heart rhythm through the voluntary changing the respiratory rate.
Dokl. Akad. Nauk SSSR ,283:738.Raschke, F. (1987).
Temporal Disorder in Human Oscillatory Systems , volume 36. Springer Seriesin Synergetics, Springer-Verlag, Berlin.Ren, Y. and Zhang, J. (2019). Increased cardiorespiratory synchronization evoked by a breathcontroller based on heartbeat detection.
BiomedEng OnLine , 18, 61.Rosenblum, M., Pikovsky, A., and Kurths, J. (1996). Phase synchronization of chaotic oscillators.
Phys. Rev. Lett. , 76:1804–1807.Rosenblum, M., Pikovsky, A., and Kurths, J. (2004). Synchronization approach to analysis ofbiological systems.
Fluct Noise Lett , 4:L53–L62.Schafer, C., Rosenblum, M., Abel, H.-H., and Kurths, J. (1999). Synchronization in the humancardiorespiratory system.
Phys Rev E , 60:857–870.Schafer, C., Rosenblum, M., Kurths, J., and Abel, H. (1998). Heartbeat synchronized with ventila-tion.
Nature , 392:239–240.Stefanovska, A. and Bracic, M. (1999). Physics of the human cardiovascular system.
Contemp Phys ,40:31–55.Stefanovska, A., Lotric, M., Strle, S., and Haken, H. (2001). The cardiovascular system as coupledoscillators?
Physiol. Meas. , 22:535.Stroop, J. (1935). Studies of interference in serial verbal reactions.
J Exp Psychol , 18:643–662.Sweeney-Reed, C. and Nasuto, S. (2007). A novel approach to the detection of synchronisation ineeg based on empirical mode decomposition.
J Comput Neurosci. , 23:79–111.Tass, P., Rosenblum, M., Weule, J., Kurths, J., Pikovski, A., Volkmann, J., Schnitzler, A., andFreund, H.-J. (1998). Detection of n:m phase locking from noisy data: Application to magnetoen-cephalography.
Phys. Rev. Lett. , 81:3291–3294.Wood, R., Maraj, B., Lee, C., and Reyes, R. (2002). Short-term heart rate variability during acognitive challenge in young and older adults.
Age Ageing , 31:131–135.Wu, M. and Hu, C. (2006). Empirical mode decomposition and synchrogram approach to cardiores-piratory synchronizations.