heart rate variability during

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J Syst Sci Complex (2013) 26: 104116

HEART RATE VARIABILITY DURINGHIGH-INTENSITY EXERCISE

SARMIENTO Samuel GARC IA-MANSO Juan Manuel MART IN-GONZ ALEZ Juan Manuel VAAMONDE Diana CALDER ON Javier DA SILVA-GRIGOLETTO Marzo Edir

DOI: 10.1007/s11424-013-2287-yReceived: 10 January 2012c The Editorial Office of JSSC & Springer-Verlag Berlin Heidelberg 2013

Abstract The aim of this paper is to describe and analyse the behaviour of heart rate variability (HRV)during constant-load, high-intensity exercise using a time frequency analysis (Wavelet Transform).Eleven elite cyclists took part in the study (age: 18.6 3.0 years; VO 2 max : 4.88 0.61 litres min 1 ). Ini-tially, all subjects performed an incremental cycloergometer test to determine load power in a constantload-test (379.55 36.02 W; 89.0%). HRV declined dramatically from the start of testing ( p < 0.05).The behaviour of power spectral density within the LF band mirrored that of total energy, recordinga signicant decrease from the outset LF peaks fell rapidly thereafter, remaining stable until the endof the test. HF-VHF fell sharply in the rst 20 to 30 seconds. The relative weighting (%) of HF-VHFwas inverted with the onset of fatigue, [1.6% at the start, 7.1 ( p < 0.05) at the end of the rst phase,and 43.1% ( p < 0.05) at the end of the test]. HF-VHF peak displayed three phases: a moderate initialincrease, followed by a slight fall, thereafter increasing to the end of the test. The LF/HF-VHF ratioincreased at the start, later falling progressively until the end of the rst phase and remaining aroundminimal values until the end of the test.Key words Cycling, heart rate variability, wavelet.

1 Introduction

The cardiovascular system at rest is mostly controlled by higher brain centres and cardio-vascular control areas in the brain through the sympathetic and parasympathetic branches of

the autonomic nervous system (ANS). Efferent sympathetic and vagal activities directed to thesinus node are characterised by discharges largely synchronous with each cardiac cycle, whichcan be modulated by central and peripheral oscillators. These mechanisms generate rhythmic

SARMIENTO Samuel GARC IA-MANSO Juan ManuelDepartment of Physical Education, University of Las Palmas de Gran Canaria, Spain.MART IN-GONZ ALEZ Juan ManuelPhysics Department, University of Las Palmas de Gran Canaria, Spain.VAAMONDE DianaDepartment of Morphological Sciences, School of Medicine, University of C ordoba, Spain.CALDER ON JavierPhysical Activity Sciences Faculty, Polytechnic University of Madrid, Spain.DA SILVA-GRIGOLETTO Marzo EdirAndalusian Centre of Sports Medicine, C ordoba, Spain . Email: pit [email protected].

This paper was recommended for publication by Editors FENG Dexing and HAN Jing.


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HEART RATE VARIABILITY DURING CYCLING 105

uctuations in efferent neural discharge which manifest as short and long-term oscillations inthe heart period.

It is commonly accepted that heart rate variability (HRV) analysis is a non-invasive tech-nique for assessing autonomic inuences on the heart [1 4] . Evaluation criteria have been estab-lished by the Task Force and accepted by the Board of the European Society of Cardiology andthe North American Society of Pacing, and Electrophysiology [5]. However, the criteria used indifferent HRV series obtained during exercise vary slightly.

With physical activity, cardiovascular responses depend on the type, intensity, and volumeof exercise. The behaviour of HRV has been analysed in different types of endurance activity:steady-state intensity [6 9] and incremental exercise [10 12] .

During moderate exercise, HR is regulated initially by the ANS, with increased sympatheticmodulation and withdrawal of parasympathetic activity [4,13 15] . These changes are associ-ated with local chemical factors and hemodynamic changes that are dependent on exerciseintensity [7,16 ,17] and accelerated breathing rates. Changes in breathing affect HR control indifferent ways. Breathing and heart rate respond to a process involving both systems, knownas respiratory sinus arrhythmia (RSA). The effect of respiratory oscillation on blood pressurecan be ascribed to the cyclic variation in intrathoracic pressure, with breathing mechanicallyperturbing venous return, cardiac output, and blood pressure. Such changes are detected bybaroreceptors, which cause changes in autonomic HR control [10 ,18 ,19] .

During heavy exercise, the responses described above are exacerbated. Some studies indicatethat the ANS is no longer effective with these workloads [7,20 22] and that there are othermechanisms, either mechanical [9,23 ,24] or functional [25] , which govern the cardiac response. Suchmechanisms imply the reex baroreceptor action and the reex neuronal feedback provoked bymuscle contraction [26] .

We hypothesize that the nature of these mechanisms is observed when the subject reaches

a high level of fatigue and demands the organism an important effort in order to perform anexercise. HRV is an especially sensitive indicator for minimal changes in the organisms responseto workloads and, consequently, is an accurate indicator of how the organism reacts to fatiguein exercises of different intensities giving us information on the response capacity, fatigue level,and performance capacity. Thus, the aim of the present study was to describe and analysethe behaviour of heart rate variability (HRV) during constant-load, high-intensity (HI) exerciseusing a time frequency analysis (Wavelet Transform) of the time intervals between beats.

2 Methods

2.1 ParticipantsEleven elite cyclists (descriptive anthropometric and physiological characteristics for the

sample are shown in Table 1) took part in this study. All subjects were informed of the natureof the study, which complied with the ethical guidelines of the Declaration of Helsinki, and allgave their informed written consent to be included. The study protocol was approved by theEthics Committee of the University of Las Palmas de Gran Canaria.

2.2 Protocol

All subjects performed a preliminary incremental test to characterise the sample and de-termine the workload for the HI exercise. Tests were performed using a cycloergometer withan electromagnetic braking system (Jaeger ER800, Erich Jaeger, Germany). Before startingthe incremental load test, subjects spent 2 min at rest on the cycloergometer to determine


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106 SARMIENTO SAMUEL, et al.

baseline values. After a warm-up period of 10 min (5 min at 50 W and 5 min at 100 W), allsubjects pedalled at between 8090 rpm with load increases of 5 W every 12 s (25 min 1 ) untilexhaustion. One week later, the subjects performed a six-minute constant load-test (89.0% of the maximum intensity obtained in the incremental test). This load was higher than the VT2(second ventilatory threshold) load and lower than the maximum aerobic speed (379.55 36.02W; 5.61 0.73 WKg 1).

Table 1 Subjects characteristics ( n = 11). Mean and standard deviationAge (years) 18.6 3.0Height (centimetres) 174.1 6.2Body mass (kg) 68.3 7.4Percentage fat (%) 8.7 1.5Relative workload (W Kg 1 ) 5.61 0.73Percentage workload 88.95 4.79 %Rest Heart Rate (beats min 1 ) 52.55 8.34

Maximal Heart Rate (beats min 1

) 185.09 6.06Maximal Oxygen Uptake (litres min 1 ) 4.88 0.61

2.3 Measurement of Ventilatory Parameters and O 2 Consumption

Subjects breathed normal air through a low-resistance valve using a mask of known deadvolume. The composition and volume of the expired air ware determined using a Jaeger OxiconPro analyser (Erich Jaeger, Germany); this adheres to the standards of the American ThoracicSociety and the European Communities Chemistry Council. Gaseous exchange data were pro-cessed breath-by-breath using LabManager v.4.53 software (Erich Jaeger, Germany). Prior toeach test, the equipment was calibrated using a gas with the following composition: 16% O 2 ,

5% CO2 , and 79% N2 .2.4 Measurement of R-R Intervals

HR was monitored beat-by-beat using a Polar S810i RR cardiotachometer (Polar Electro,Oy, Finland) and a Jaeger ECG surface electrocardiograph (Viasys Healthcare, Erich Jaeger,Germany). The cardiotachometric recording of HR is a validated method [27 ,28] . Cardiac datawere processed using Polar Precision Performance SW software v.3.00 (Polar Electro, Oy, Fin-land). The obtained data were analysed in terms of two phases determined by the inectionpoint in a double-logarithmic (log-log) plot of HR values vs. time. Values were analysed at thefollowing points: start, end of the rst phase and minutes 23, 34, 45, and 56 after the rstphase, until completion of four one-minute intervals (second phase).

2.5 Time-Frequency Analysis of Heart Rate Variability

The cardiac signal was examined by analysing the intervals between beats; this provided ameasurement of HRV Wavelet transform (WT) provides a general signal processing techniquethat can be used in HRV analysis [29 ,30] . WT utilises short windows at high frequencies andlong windows at low frequencies and can be successfully applied to non-stationary signals foranalysis and processing. It indicates which frequencies occur at what time, showing good timeresolution at high frequencies and good frequency resolution at low frequencies.

The wavelets are families of functions, dened both spatially and temporally, which aregenerated by the scaling and translation of a function called mother wavelet or base function [31] .

a,b (t ) = |a | 1/ 2 t ba , (1)


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where is the mother wavelet, and the scaling and translation parameters a and b varycontinuously with regard to the group of real numbers ( R ) as long as a does not equal 0(a = 0). The value of the scale enables dilatation and compression of the mother wavelet, whilethe parameter of scale a corresponds to frequency information. The translation parameter blocates the wavelet function in time along the length of the signal. Further mathematical detailson this procedure can be found in [32].

A discrete wavelet transform (DWT), with a Daubechies (Db8) type base function, wasinitially used to eliminate the trend of the signal over time. A linear interpolation was thenapplied to the remaining signal in order to obtain a uniform sampling. Finally, the digital RRsignal was subjected to continuous wavelet transform (CWT) using a Morlet-type base functionwith = 6 and =20, which provided good quality time and frequency resolution, where is a dimensionless frequency which denes the number of cycles of the Morlet wavelet. Largevalues for are associated with improved frequency resolution, though at the expense of poorertime resolution. For this reason, several values of the parameter were used, and it was foundthat = 20 best tted our purposes.

The value of power spectral density at each moment of the test was based on the sum of thecoefficient wavelets at each moment. The properties of a time series at different scales can besummarized by discrete wavelet variance, which breaks down the variance of a time series on ascale-by-scale basis. However, the number of wavelet coefficients at each scale, obtained by theDWT, decreases by a factor of 2 for each increasing level of the transform, limiting the abilityto carry out statistical analyses on the coefficients. This limitation can be overcome if thedownsampling in the DWT is avoided by using the maximal overlap discrete wavelet transform.The analysis was performed using Matlab software (Mathworks Inc., Natick, MA, USA). Thefrequency ranges for the bands were: VLF < 0.04 Hz; LF 0.04 Hz to 0.15 Hz; HF 0.15 Hz to0.4 Hz; VHF > 0.4 Hz.

2.6 Statistical Analysis

Standard statistical methods were used for the calculation of means and standard devia-tions. Normal Gaussian distribution of the data was veried by the Shapiro-Wilk test. Allvariables were compared by repeated-measures ANOVA followed by a Bonferroni post-hoc test.Statistical signicance was set at p < 0.05. The statistical package SPSS 12.0.1 for Windowswas used for all calculations.

3 Results

HR and VO 2 increased from the outset until the end of HI (Table 2). Their behaviourdisplayed two clearly-differentiated phases: an initial phase (92.3 41.7seconds) with a rapidincrease in HR ( =35.9%; p < 0.001) and VO 2 ( =121.5%; p < 0.001); and a second phase of slow increase in HR ( =10.7%; p < 0.001) and VO 2 ( =14.1%; p < 0.006), which lasted untilthe end of the test (6 min). After the third minute of the second phase, increases in HR weresignicant with regards the initial measurement (start) but not with regards the immediatelyprior measurement.

Changes in cardiorespiratory response were accompanied by a signicant drop in HRV (Ta-ble3) in both frequency bands (LF and HF), and in VO 2 (Table 2).


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Table 2 Means and standard deviation of HR and VO 2 max during both phases of the protocolPhases First phase ( n = 11)

Variables Start End

HR(beat min 1

) 127.2 6.9 169.9 8.6 #

VO 2 (1 min 1 ) 1.95 0.1 4.32 0.5 #

Phases Second phase ( n = 11)

Variables Phase 1 + 1min Phase 1 + 2min Phase 1 + 3min Phase 1 + 4minHR(beat min 1 ) 176.2 6.0 # 181.2 4.3 # 184.7 4.7 188.1 7.0

VO 2 (1 min 1 ) 4.75 0.4 # 4.90 0.5 # 4.90 0.6 4.92 0.6

Denotes signicant difference ( p < 0.05) using repeated-measures ANOVA with respect tothe start measurement. # Denotes signicant difference ( p < 0.05) using repeated- measuresANOVA with respect to the immediately previous measurement.

Table 3 Average values and standard deviation (%) of changes in Total Power (TP),Low Frequency (LF), and High Frequency (HF) in the two phases of the test

TP(%) a 100 24.4 21.4 10.7 12.1 4.2 9.1 4.2 9.1 3.3 8.0LF(%) b 98.4 1.4 92.9 17.9 95.7 7.0 90.3 8.6 73.8 26.4 56.9 20.2HF(%) b 1.6 1.4 7.1 17.9 4.3 7.0 9.7 8.6 26.2 26.4 43.1 20.2

a Represents the relative value (%) with respect to PT at the start of the test. b Representsthe relative value (%) with respect to PT at each moment of the test. Denotes signicantdifference ( p < 0.05) using repeated-measures ANOVA.

The behaviour of the HRV frequency spectrum over the course of the test can be seenin Figure 1, where the spectrogram (CWT) is shown with the weighting of frequencies (ingreyscale) of one of the subjects during the test. As well as changes in power spectral densityfor each frequency, the graph also shows the pattern displayed by the peaks of the two bands

(HF > 0.15 Hz; LF< 0.15 Hz), represented by the line corresponding to the DWT (Daubechiesbase function = 8) applied to the HF-VHF band. However, the DWT did not detect theappreciable changes recorded for HF-VHF peaks at the start of the test.

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Figure 1 Frequency spectrum (CWT Morlet base function with = 6 in greyscale andoutline mode of four levels) of HRV. Shows the changes in frequency compo-nents (Hz) plotted against time (s). Superimposed is the DWT (Daubechiesbase function = 8) with peaks of maximum energy in the HF-VHF bandshown in Hz by the solid black line. The X axis shows the time of the test

(Start: 720 s) and the Y axis shows the frequencies (Hz)


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Figure 2 shows the typical pattern, represented by one of the sample subjects, of values forPT (upper left), HF-VHF (upper right), LF (lower left), and the LF/HF ratio (lower right).This pattern, with individual idiosyncrasies, was observed in all subjects under evaluation.

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Figure 2 Typical pattern observed for PT values (upper left), HF-VHF values(upper right), LF values (lower left), and the LF/HF ratio values (lowerright). This pattern, with individual idiosyncrasies, was observed in allsubjects under evaluation

Figure 3 shows the relationship between the frequency spectrum of the HF-VHF band,expressed by the total of the wavelet coefficients, and heart rate (top) and respiratory frequency(bottom) for one of the sample subjects. It should be noted how there is a drastic change inspectral density with regards to functional parameters; that is, from a certain cardiorespiratorylevel (HR > 178 and over 40 breaths/minute), drastic changes (fall in spectral density) in thehigh frequency band are observed.

4 Discussion

The data obtained conrmed that WTs are an accurate and highly-sensitive method of detecting small changes in the HRV signal due to exercise, enabling a more detailed examination

of certain specic aspects of the acute response to intense, medium-duration endurance exercise(3 to 19 min) which are not perceptible using other evaluation instruments.


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4.1 Heart Rate Response

At the outset of abrupt high-intensity exercise, HR increases rapidly in proportion with workrate. The initial increases were rapid and intense (Start: 127.2 6.9beat min 1 ; Phase-I end :

169.9 8.6 beat min

1 ; p < 0.001), reaching values greater than 90% of HR max at the end of therst phase. The subjects started the test with a relatively high HR after warm-up (Table 2).During the second phase, HR continued to show a slight, but statistically-signicant increaseuntil the end of the test (Table 2).

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Figure 3 The upper gure shows the spectral density of the HF-VHF band ( Y axis) andheart rate ( X axis). The lower gure shows spectral density for the HF-VHFband ( Y axis) and breathing frequency ( X axis). Both gures relate to the samesubject from the sample


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4.2 Effect of High-Intensity Exercise on HRV

HRV, expressed in terms of total power spectral density (PT) and determined on the basisof wavelet coefficients, decreased dramatically after the rst few seconds after starting tests. At

the end of the rst phase PT fell by 75.6 21.4% ( p < 0.05) compared to baseline values. At theend of the rst phase (90.6 30.5 seconds), parasympathetic control may have disappeared, orfallen to minimal levels, whilst sympathetic activity may also have been seriously compromised;the changes observed in HRV would depend very directly on other mechanical and functionalvariables.

With strenuous exercise, ANS initially increases mainly due to the muscle chemoreex,whereas central command increases HR and cardiac output through vagal withdrawal [13] . Con-sequently, HRV decreases in total power and in the two major frequency bands: low frequency(LF) and high frequency (HF) [4,13 15] . This response initially involves parasympathetic with-drawal and augmented sympathetic activity [4,13 15] .

The reason for the increased sympathetic activity at the start of exercise is not fully un-

derstood at present. While a number of authors, including Vctor, et al. [33] , Rotto, et al. [34] ,and Sinoway, et al. [35] link metabolic acidosis to increased sympathetic activity, Vissinget, etal. [36] suggested that no parallel exists between the functional responses. Nevertheless, plasmaepinephrine levels increase sharply at around 60% VO 2max [37 39] . Some researchers believethat sympathetic activity remains unchanged up to 100% of ventilatory threshold and increasesabruptly at 110% [40] and that total parasympathetic withdrawal does not occur even duringhigh-intensity exercise [21 ,41] .

Retention of parasympathetic tone during exercise could be a benecial response for theorganism, allowing it to respond rapidly to changes in blood pressure [42] . However, Rowell &OLeary [21] suggest that, during progressive exercise, sympathetic activity does not increaseuntil parasympathetic restraint is exhausted. These changes in autonomic tone have beenassociated with changes in central command baroreex and activation of muscle afferents [43 ,44] .Other authors suggest that parasympathetic activity only decreases signicantly at 50% VO 2maxwhile sympathetic activity increases slightly at lower intensities (50%60% VO 2max ) and moremarkedly when the intensity is moderate [45 ,46] .

Here, intensity was pronounced, causing signicant changes in the athletes organisms. Dur-ing heavy exercise, eliciting increases in sympathetic activity, marked functional parasympa-thetic tone exists [42 ,45] .

4.3 Effect of High-Intensity Exercise in LF

From the start of the test, the absolute values of LF fell rapidly (77.8 22.6%), in a mannersimilar to that of PT. However, in the rst few moments, LF continued to account for mostof the total HRV power spectral density (98.3 2.3%). This fall in LF was accompanied bya progressive reduction in peaks in this frequency band. During the second phase, the powerspectral density continued to fall throughout the test, reaching nal values of < 4% of totalbaseline variability.

The reduction in LF (ms 2 ) values with medium- and high-intensity loads has been reportedin other studies [18 ,47] . Only one published study recorded increases in LF values during a ramptest [48] , but it should be noted that VLF (0.000.004 Hz) values were included within the LFband.

4.4 Effect of High Intensity-Exercise in HF-VHF

HF-VHF data are valuable in this type of test, in that they are closely linked to respiratoryresponse and to the mechanical characteristics of the activity. In all the assessed subjects, the


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pattern of spectral density divided into three phases: an initial sharp fall (20 to 30 seconds);a period of minimal values lasting for part of the second phase of the test; and a progressiveincrease when levels of fatigue become pronounced ( 170 beat min 1). This nal increase inspectral density was almost three times the minimum value observed throughout the durationof the test (second minute) and was accompanied by greater instability in values.

The relative weight (%) of the two frequency bands (LF and HF-VHF) varied with exercisein proportion to the intensity of effort and the duration of the test. Despite the continuousdecrease in total power spectral density, the greater weight of variability was always observedin the LF band. With fatigue, however, the relative weight (%) of HF-VHF increased, whilethat of LF fell, thus modifying the LF/HF ratio. Detection of the intensity or timing HF-VHFweighting increases could provide a means of controlling the training load and assessing athletes.

Fatigue and the accompanying mechanisms exert a mechanical effect on the sinus nodethrough forced hyperventilation, prompting a progressive increase in HF peak . The HF-VHFpeaks tend to behave in a similar manner to respiratory frequency [11 ,12 ,49] . In the HI tests, theHF

peak behaved idiosyncratically in the rst phase (Figure 1), exhibiting two distinct patterns

in this period. Over the course of the rst seconds, ( 30 ) peaks tended to increase slightly,continuing to increase until the end of the phase. Later, during the second phase, HF-VHFpeaks increased progressively until effort ceased. It should be recalled that the HF peak is stronglyassociated with breathing. However, it must be noted that there is no consensus, up-to-date,on the mechanisms affecting at these intensities the observed changes, especially with regardsto low-frequency band when the subject reaches a level of cardiorespiratory response that isclose to the anaerobic threshold.

The increase in HF in high-intensity physical effort has also been reported in [7, 9, 1012, 49,50], and is almost always linked to increases in respiratory frequency and amplitude. Increasedhyperventilation can exert mechanical effects on the sinus node that manifest themselves in in-

creased values for HF-VHF (peak and absolute values).This increase in HF may be attributableto the critical situation undergone by the cardiac system in extreme states of fatigue. Thisseems to indicate that, at extremely intense levels of effort, HR is not modulated by the vege-tative system, but rather that it responds to non-neural effects such as muscular mechanismsand the intense forced respiratory dynamic.

It is also worth noting that during the second part of the test, VE and breathing frequency(BF) maintained consistently high values, and the ventilatory parameter that took longest toreach its maximum values was VE (247.36 79.91 seconds). The tidal volume (TV) very soonreached its maximum value (157.73 62.65 seconds), while BF continued rising until virtuallythe end of the test (272.36 91.10 seconds).

Figure 3 shows that the increase in the power spectral density of HF-VHF occurred at 133

seconds into the test. The HR at this point was 176177 beats min 1

while BF was 4748resp min 1 . VC had already reached its maximum level and VO 2 was around its maximumresponse level. In this particular subject, however, BF was still at 60% of its maximum value,while VE was at 70%. This conrms that the increase in respiratory rhythm is especiallyimportant for the HRV response in the HF-VHF band.

The pattern conrms the close relationship existing between cardiac response and ventilatoryresponse (RSA). The number of beats per breath at this point was signicantly lower thanbaseline. The gure of ve to eight beats normally found at the start of the test fell to threebeats per breath, or fewer, in the moments leading up to the conclusion of the test. Cottin,et al. [7] also found a lower number of beats per breath during intense exercise, adding thatworking at an intensity of > VT2 did not alter cardiorespiratory synchronisation.

It should also be mentioned that, at the end of the test, the subjects frequently tried tokeep up the work rate using anomalous movements in mechanical response (force applied to the


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pedal and cadence) and forced functional responses (cardiorespiratory and muscular responses).Sumi, et al. [9], Blain, et al. [51] , and Lunt, et al. [52] linked oscillations at the end of the test withthe subjects pedal cadence during this phase. The authors emphasise that there is a highcorrelation coefficient between the peak of HF and the pedalling frequency, demonstrating thatthe work rate manifests itself in HRV and specically with the development of the HF spectrum.

4.5 LF/HF Ratio Analysis

The LF/HF ratio increased sharply during the rst minute of the test (20 40 , but subse-quently declined, reaching minimum values when the value of VO 2 stabilised. Similar ndingsare reported in studies using an intensity > VT2 [40 ,46 ,53] .

In summary, this means a proportional increase in the weight of VHF and HF bands withregards to the total variability of the signal which, as explained in previous paragraphs, coincidewith a signicant decrease of vegetative control and a coupling of the signals in the high-frequency bands with the mechanical workload that the athlete performs during the pedaling

action.

5 Conclusion

A basic problem in HRV analysis is non-stationarity of the heart rate signal, which holdsparticularly true for exercise conditions. Standard spectral HRV analysis (i.e., FFT) shouldnot be applied to exercise conditions. The use of WTs analyses shows much promise in thisarea. The use of WT allows for the detailed assessment of the evolution of the cardiac responseenabling us to individually establish the moments in which the organism establishes functionalmodications in order to respond to the impact of the intensity load.

With wavelet transforms, changes in HRV signal of energy (total, LF, and HF-VHF) andthe evolution of peaks of the two assessed bands (LF peak and HF-VHF peak ) may be used forinstantaneous and continuous control of the organisms functional response, enabling us todetect minimal adaptive changes in the organism as a response to exercises of different intensityand duration. It can be stated, overall, that the relative weight (%) of the two frequency bands(LF and HF-VHF) varied with regards to exercise in proportion to the intensity of the effortand the duration of the test. Despite the continuous decrease in total power spectral density,the greater weight of variability was always observed in the LF band. With fatigue, however,the relative weight (%) of HF-VHF increased, while that of LF fell, thus modifying the LF/HFratio.

Most of the studies on HRV and exercise justify these changes from the modication tothe vegetative response as effect of fatigue and its inuence on the cardiac rhythm. Specialattention is given to the marked decrease of HRV with workload increase and to the decreasein vagal withdrawal. Yet, part of the changes that take place in HRV, specially when fatigue iselevated, are directly related with the type of activity and the effect the mechanical work hason respiratory rhythm, ventilation, hemodynamic changes, and cardiac response.

In our study, the use of TW allows us to observe how HRV indices are very directly inuencedby both cycling cadence and power output at the end of exercise. This cardiolocomotor couplinghas been proposed to optimize blood ow to contracting muscles and minimize the energy costof cardiac muscle contraction [54] .

6 Practical Applications

Being able to assess the HRV prole for each moment of an exercise, independently from


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work intensity, enables the sports technician to establish the individual evolution of fatigueduring the execution of an effort. From a functional standpoint, this methodology enables us toperform a very precise analysis as to how the cardiorespiratory system instantaneously adjuststo the exercise demands showing specic responses of the frequency spectrum when fatiguestarts to compromise the mechanical response.

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