assessment of ventilatory thresholds from heart rate variability in well-trained subjects during...

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Abbreviations

V̇E ventilatory flow

V̇ O2 oxygen uptake

V̇ CO2 carbon dioxide output

Vt tidal volume

V̇E/V̇ O2,

V̇E/V̇ CO2 ventilatory equivalents

BF breathing frequency

HRV heart rate variability

HF high frequency spectral energy

f HF frequency peak of HF-HRV

VT1 first ventilatory threshold detected from

ventilatory equivalents

VT2 second ventilatory threshold detected from

ventilatory equivalents

HFT1 first ventilatory threshold detected from HF · f HFHFT2 second ventilatory threshold detected from HF· f HFTRSA1 first ventilatory threshold detected from f HFTRSA2 second ventilatory threshold detected from f HF

Abstract

The purpose of this study was to implement a new method for

assessing the ventilatory thresholds from heart rate variability

(HRV) analysis. ECG, V̇ O2, V̇ CO2, and V̇E were collected from elev-

en well-trained subjects during an incremental exhaustive test

performed on a cycle ergometer. The “Short-Term Fourier Trans-

form” analysis was applied to RR time series to compute the highfrequency HRV energy (HF, frequency range: 0.15 – 2 Hz) and HF

frequency peak ( f HF) vs. power stages. For all subjects, visual ex-

amination of ventilatory equivalents, f HF, and instantaneous HF

energy multiplied by f HF (HF· f HF) showed two nonlinear in-

creases. The first nonlinear increase corresponded to the first

ventilatory threshold (VT1) and was associated with the first HF

threshold (TRSA1 from f HF and HFT1 from HF· f HF detection). The

second nonlinear increase represented the second ventilatory

threshold (VT2) and was associated with the second HF threshold

(TRSA2 from f HF and HFT2 from HF· f HF detection). HFT1 , TRSA1, HFT2,

and TRSA2 were, respectively, not significantly different from VT1(VT1 = 219 ± 45 vs. HFT1 =220±48W, p=0.975; VT1 vs. TRSA1 =

213±56W, p=0.662) and VT2 (VT2 =293±45 vs. HFT2 =294

± – 48W, p= 0.956; vs. TRSA2 = 300± 58 W, p = 0.445). In addition,

when expressed as a function of power, HFT1, TRSA1, HFT2, and

TRSA2 were respectively correlated with VT1 (with HFT1 r2 = 0.94,

p < 0.001; with TRSA1 r2 = 0.48, p < 0.05) and VT2 (with HFT2 r2 =0.97, p < 0.001; with TRSA2 r

2 = 0.79, p < 0.001). This study con-

firms that ventilatory thresholds can be determined from RR

time series using HRV time-frequency analysis in healthy well-

trained subjects. In addition it shows that HF· f HF provides a more

reliable and accurate index than f HF alone for this assessment.

Key words

Exercise · respiratory components · time frequency analysis ·

short-term fourier transform

Ph y si ol o g y &Bi o ch

emi s tr y

Affiliation1 Laboratory of Exercise Physiology (LEPH), University of Evry, E.A. 3872 Genopole, Evry Cedex, France

2 French National Institute for Research in Computer Science and Control (INRIA), Le Chesnay, France3 Laboratory of Physiology, Medicine Faculty, University of Paris XI, E.F. R., Hôpital Antoine Béclère,

Clamart Cedex, France

Correspondence

François Cottin, PhD · Department of Sport a nd Exercise Science · University of Evry ·Boulevard F. Mitterrand · 91025 Evry Cedex · France · Phone: + 330169 64 4881 · Fax: +33 016964 4895 ·

E-mail: [email protected]

Accepted after revision: December 5, 2005

Bibliography

Int J Sports Med © Georg Thieme Verlag KG · Stuttgart · New York ·DOI 10.1055/s-2006-923849 · Published online 2006 ·

ISSN 0172-4622

F. Cottin1

P.-M. Leprêtre1

P. Lopes1

Y. Papelier3

C. Médigue2

V. Billat1

Assessment of Ventilatory Thresholds

from Heart Rate Variability inWell-Trained Subjects during Cycling


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Introduction

Nowadays, the assessment of ventilatory thresholdsin athletes is

used by some coaches in order to build their specific training

programs [1,19,25]. The measurement of the breathing compo-

nents during exhaustive incremental tests allows the assessment

of two ventilatory thresholds [4,35]. According to Wasserman et

al. [35, 36], the ventilatory thresholds are indicated by the obser-

vation of the ventilatory equivalents (V̇E/V̇ O2 and V̇E/V̇ CO2)

curves vs. power during an incremental exercise test on a cycleergometer. The first ventilatory threshold (VT1) is called “adapta-

tion ventilatory threshold” resulting from the hyperpnea elicited

by the increase in the CO2 metabolic production linked to the ex-

ercise intensity above anaerobic threshold. As a result, V̇ E/V̇ O2nonlinearly increases while V̇E/V̇ CO2 remains constant. The sec-

ond ventilatory threshold (VT2) is called: “maladjustment venti-

latory threshold” or “respiratory compensation point”. Since the

hyperpnea is not sufficient to eliminate the CO2 metabolic pro-

duction, V̇E increases whereas V̇ CO2 remains constant leading to

a drastic increase in V̇E/V̇ CO2 until exhaustion.

Furthermore, heart rate variability (HRV) has been broadly inves-

tigated during exercise [3, 6, 8, 9,12,13,33, 38]. During short-termrecordings of exercise (5–10 minutes duration), spectral energy

is divided into two frequency bands [3,31]:

1. A low frequency band ranges from 0.04 to 0.15 Hz (LF-HRV). It

is now admitted that this LF variability is induced by both

sympathetic and vagal cardiovascular control [31].

2. A high frequency band (HF-HRV) reflecting the amplitude of

the respiratory sinus arrhythmia (RSA), linked to the breath-

ing rate and vagal cardiovascular control. It ranges from 0.15

to f max Hz during exercise. f max is the maximal frequency in-

duced by the sampling scale of the RR signal.

It is well known that total spectral energy decreases when exer-cise intensity increases [9,15,26,33]. Recently, in healthy hu-

mans [6,12] and trotting horses [11], it has been shown that,

when the exercise intensity is lower than the intensity at ventila-

tory threshold (moderate exercise), the energy of LF-HRV (LF) is

prevalent compared to the energy of HF-HRV (HF). In contrast,

when the exercise intensity exceeds the intensity at ventilatory

threshold (heavy exercise) HF is prevalent compared to LF. Fur-

thermore, it seems that the hyperpnea observed during heavy

exercise induces a mechanical electric feedback on the sinus

node [6,12,22,23] with the stretch increasing the spontaneous

depolarization of the cardiac myocytes [24,28]. Both phenomena

result in an increase in HF above VT1 [6].

In addition, it has been reported that the spectral frequency peak

in the HF band ( f HF) corresponds closely to the breathing fre-

quency [6,12]. Since breathing frequency (BF) increases when

VT1 is exceeded and abruptly increases when VT2 is exceeded,

f HF could follow a behavior similar to that of the breathing fre-

quency. Recent studies have shown that V T1 could be assessed

from BF [21] and then from f HF [2]. Furthermore, Blain et al. [5]

have demonstrated that it was possible to detect both VTs from

f HF (TRSA) when TRSA1 was corresponding to VT1 and TRSA2 was cor-

responding to VT2. However, in 20% of their data it was not pos-

sible to detect VTs [5]. In addition, among the numerous papers

and methods about the ventilatory threshold assessment, only a

few studies have shown the reliability of the VTs assessment

from BF. The present study aims to find an alternative method

of VTs assessment from HRV analysis and to compare it with the

detection method developed by Blain et al. [5].

Therefore, it is hypothesized that during an incremental protocol,

the product of the HF energy by the HF frequency peak (HF· f HF)

vs. work rate could show three identifiable phases allowing the

ventilatory thresholds assessment:

1. Since the autonomic control of heart rate decreases [6,15] and

breathing frequency remains nearly constant when work rate

increases [10,18], HF· f HF could attain a minimum just before

VT1 was reached [6].

2. When work rate exceeds VT1, the resulting hyperpnea could

entail an increase in f HF and in HF energy by mechanical effect

[6,11,12]. The resulting steeper increase in HF· f HF would give

the first HF threshold (HFT1).3. When work rate exceeds VT2, the subsequent increase in

breathing frequency could result in a further increase in f HFand in HF energy by mechanical effect [6,11, 29]. This second

abrupt increase in HF · f HF would give the second HF threshold

(HFT2).

Methods

Subjects

Eleven competitive male cyclists and triathletes (20 ± 6.3 years)

participated in this study. All subjects were free of cardiac and

pulmonary disease. The anthropometric and physiological char-

acteristics of the subjects are summarized in Table 1. Prior topar-

ticipating, each subject was familiarized with the experimental

procedure and informed of the risks associated withthe protocol.

All subjects gave their written voluntary informed consent in

accordance with the guidelines of the University of Evry.

Experimental design

Two to three hours after a light breakfast, all subjects performed

an incremental exercise test in the upright position on an elec-

tronically braked cycle ergometer (Ergoline 900, Marquette-Hel-

lige, Fribourg, Germany) in an air-conditioned room. Since the

cycle ergometer performance level was different between cy-

Table 1 Characteristics of the subjects (n = 11)

Mean SD

Age (years) 20.0 6.3

Height (cm) 77.5 5.0

Weight (kg) 65.8 8.7

Body mass index (%) 12.3 6.7

PV ̇O2max (W) 353.0 53.0

MAP (W) 371.0 60.0

V ̇O2max (l· min–1 ) 4.5 0.4

V ̇O2max (ml·min–1 · kg–1 ) 68.2 6.3

HRV ̇O2max (beats ·min–1 ) 194.0 8.0

Cottin F et al. Ventilatory Thresholds Assessment from HRV … Int J Sports Med

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clists and triathletes, the chosen incremental protocol was differ-

ent. After a two-minute warm-up at 60 watts for triathletes or 75

watts for cyclists, each subject performed a 15 watts·min–1 in-

cremental test for triathletes or 25 watts · min–1 for cyclists. Seat

and handlebar heights were set for each subject and kept con-

stant for all the tests. The pedalling frequency selected by each

subject was between 70 and 100 revolutions·min–1.

Data collection procedures

Time series

ECG recordings were performed with a Power lab device (ADIn-struments Ltd, Chalgrove Oxfordshire, UK) with a sampling fre-

quency of 1000 Hz. The R wave peak occurrencewas assessed us-

ing a threshold technique provided with the Chart5 program

(Chart5, v5.0.2 for Power Lab, ADInstruments Ltd, Chalgrove Ox-

fordshire, UK). Beat-to-beat RR intervals were then extracted

from the ECG signal. The ECG sampling frequency provided an

accuracy of 1 ms for each RR period. Artifacts, cumulative RR

periods and extrasystoles were manually processedby computa-

tion of interpolated or extrapolated values.

Gas measurements

V̇ O2, V̇CO2, and V̇E were measured throughout the test using a

Quark device (Quark Pft, Cosmed, Rome, Italy), [27]. Prior to each

test, the O2 analysis system was calibrated using ambient air

(20.9% O2 and 0.04% CO2) and calibration gas (12.01% O2 and 5%

CO2). The calibration of the turbine flow-meter of the analyzer

was performed with a 3-L syringe (Quinton Instruments, Seat-

tle).

Anthropometric measurements

Height and weight were measured before each test. Four skin-

fold measurements were taken (triceps, biceps, suprailiac, sub-

scapular) with % body fat computed using the Durnin and Wo-

mersley’s formula [16].

Time frequency analysis

Since, RR series were not stationary during the incremental pro-

tocol, classical spectral analysis was not suitable to analyze HRV.

Therefore, the Short Term Fourier Transform (STFT) was used for

computing HRV (Matlab software, 6.5.1, Mathworks Inc., Natick,

MA, USA). This method was already well described in previous

studies [13,14]. The main principle of STFT consists in choosing

a small enough window for analysis in which the signal can be

considered stationary [17]. Classical FFT can therefore be per-

formed on this windowed signal. The same analysis window is

applied to the next signal block, and so on until the end of theprocessed series. STFT is constituted by all the FFT performed on

the successive signal blocks that are determined by regular

translation of the chosen window. Therefore STFT yields a 3-D

figure made of all the spectra vs. time called spectrogram

(Fig. 1), which is a time-frequency representation of HRV

[13,17]. The three axes of the computed spectrogram are the fol-

lowing:

x-axis: time (s)

y-axis: frequency (Hz)

z-axis: power spectral density (ms2 · Hz–1).

Before the STFT processing, all RR series were resampled at 4 Hz

using a cubic spline function (Matlab software, 6.5.1, Mathworks

Inc., Natick, MA, USA). Each spectrogram window was made of

256 successive RR periods of 64 seconds since the time between

each RR period after resampling was 0.25 seconds. The succes-

sive spectrogram windows were spaced by 12 successive RR in-

tervals corresponding to 3 seconds duration.

In order to remove the low frequency trend of the time series, a

time-varying finite impulse response (FIR) high-pass was ap-

plied on each STFT window [11]. Then a Hamming window was

applied on each STFT segment before computing FFT [20].

Fig. 1 Typical example of a periodogramfrom a subject (top) and the associatedcomputed spectrogram (bottom) usingShort Term Fourier Transform. The cycleergometer power increased from 110 at thebeginning of the periodogram (t = 360 s) to450 watts at the end of both graphs. Spec-trogram axis: X-axis: time (seconds). Y-axis:frequency (Hz). Z-axis, Power SpectralDensity (P.S. D., ms2 · Hz–1). The spectrogramshows both frequency bands changes versus

time. The LF frequency remains constantwhereas its power decreases and almostdisappears as soon as 800 s were reached.The HF frequency (f HF) remains constantfrom 420 to 600 s, and then it begins toincrease. Immediately after 800 s, f HF showsa second steeper increase while the HFenergy increases.

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The spectral energy was computed in HF ranges by integrating

the power spectral density (PSD) for each spectrum of the spec-

trogram as following:

HF ¼X f max

f ¼0:15

PSD f ðms2Þ

f max is given by Shannon sampling theorem. The rule that “All the

information in a signal, band-limited to a frequency of f max, can

be captured in its samples taken a rate of greater than 2 · f max” is

known as Shannon’s sampling theorem. The critical frequency

f max is known as Nyquist rate. f max =1/(2· ∆t), ∆t being the sam-

pling scale of the RR signal. In the present study the signal sam-

pling was ∆t = 0.25 s, thus f max =2Hz.

In addition, the assessment of the instantaneous HF peak ( f HF)

was computed from the spectrograms (Matlab software, 6.5.1,

Mathworks Inc., Natick, MA, USA).

Since the STFT provided one spectrum every 3 seconds, it was

then possible to get the HF energy and instantaneous f HF values

every 3 seconds. However, for an optimal assessment of the VTs

[19] and to synchronize HRV and ventilatory data, the HRV com-

ponents were averaged every 20 seconds.

Ventilatory thresholds assessment

Breath by breath data were averaged to provide a data point for

each 20-s period. It was therefore possible to synchronize HRV

and ventilatory data on the same graph ([19], Fig. 2). Since the

Wasserman method appears to be a consistent predictor for cy-

cling performance in well-trained subjects with regard to reli-ability and validity [1,19], it was used to determine VT 1 and VT2[25,36]. Therefore, V̇E/V̇ O2 and V̇E/V̇ CO2 were plotted vs. work

rate during the incremental exercise test (Fig. 2). VT1 corre-

sponds to a first nonlinear increase in the V̇ E/V̇ O2 curve while

the V̇E/V̇ CO2slope remains constant [35, 36]. In addition, VT2 is in-

dicated by the nonlinear increase in the V̇E/V̇ CO2 curve concomi-

tant to a second strong increase in V̇E/V̇ O2 with further increase

in exercise intensity [35, 36]. Based on the above criteria, two ex-

perienced researchers have independently assessed the ventila-

tory thresholds. When there was a disagreement, a third experi-

enced investigator was involved in the process. When he agreed

with one investigator, the corresponding threshold was kept.

When all the investigators found different thresholds the subject

threshold could not be determined.

HF thresholds assessment

As it was indicated in the introduction, the present study com-

pared two VTs assessment methods:

1. From f HF: f HF successive values were averaged to provide a data

point for each 20-s period synchronous with the ventilatory

data. HF thresholds were detected from the curve of f HF plot-

ted vs. the work rate by an independent investigator. The first

HF threshold (TRSA1) corresponded to the last point before a

first increase in f HF. The second HF threshold (TRSA2) corre-

sponded to a second nonlinear increase in f HF (Fig. 3).

Fig. 2 Typical example of both ventilatory thresholds assessment from ventilatory components(V̇E/V̇O2, V̇E/V̇CO2), and from HRV components (HF · f HF). Each gas-exchange and HRV data pointcorresponds to a 20-s interval. X-axis: time (seconds). Left Y-axis, ventilatory equivalents (V̇E/V̇O2and V̇E/V̇CO2, solid line). Right Y-axis, HF· f HF (dotted line, ms

2 ·Hz) and HF·BF (dashed line,

ms

2

·Hz). Since BF and f HF are similar, HF · f HF and HF · BF are obviously similar. The first ventilatorythreshold (VT1) corresponds tothe first substantial increase in V̇E/V̇O2 while V̇E/V̇CO2 remainscon-stant. The second ventilatory threshold (VT2) corresponds to the steeper increase in both V̇E/V̇O2andV̇E/V̇CO2. ThefirstHF-HRVthreshold (HFT1) corresponds tothe first increase inHF · f HF,thesec-ond HF-HRV threshold (HFT2) corresponds to the second abrupt increase in HF· f HF. Since eachthreshold is given as a power stage and there is one power stage by minute, each threshold wasgiven at the nearest power stage of the corresponding abrupt increase. However, a short doublearrow was added on the graph, corresponding to eachaccurate threshold: VT1 on V̇E/V̇O2 curve at–20sandHFT1 ontheHF· f HF curveat + 20s of the powerstage threshold. Also for VT2 ontheV̇E/V̇CO2 curveat+20sandontheHF·f HF curvealso at + 20s of the powerstage threshold.


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2. From HF· f HF. The hyperpnea elicited by the overstepping of

VTs could induce a double effect on HF-HRV: an increase in

HF energy (HF) combined with an increase in f HF. Thus, the

VTs could be assessed from the product of HF by f HF (HF · f HF).

As it was mentioned above, HF· f HF successive values were

then averaged to provide a data point for each 20-s period

synchronous with the ventilatory data. Therefore, HF thresh-

olds were detected from the curve of HF· f HF plotted vs. the

work rate by an independent investigator. The first HF thresh-

old (HFT1) corresponded to the first nonlinear increase in

HF · f HF after it has reached a minimum. The second HF thresh-old (HFT2) corresponded to a second nonlinear increase in

HF · f HF (Fig. 2).

Statistical analysis

The Student’s t -test (Sigmastat 2.03, Jandel Scientifics, San Ra-

fael, CA, USA, 1997) was used to compare the respective ventila-

tory and HF-HRV thresholds (VT1 vs. HFT1, VT2 vs. HFT2, VT1 vs.

TRSA1, and VT2 vs. TRSA2). Linear regression and the Pearson Prod-

uct Moment Correlation (Sigmastat 2.03, Jandel Scientifics, San

Rafael, CA, USA, 1997) were used to test the correlation between

VT1 vs. HFT1, V T2 vs. HFT2, VT1 vs. TRSA1, and VT2 vs. TRSA2. Bland-

Altman [7] plots were conducted to illustrate the relationship

between ventilatory thresholds (VT1 and VT2) and their respec-

tive HRV thresholds (HFT1, HFT2, TRSA1, and TRSA2).

Results

Visual assessment of ventilatory and HF-HRV thresholds

Fig. 2 gives a typical example of ventilatory thresholds assess-

ment in one subject from ventilatory components (V̇E/V̇ O2, V̇E/

V̇ CO2) and from HF· f HF.

VTs assessment: For VT2 assessment, both investigators agreed

for all subjects, whereas VT1 assessment yielded conflicting re-

sults on three occasions. The third investigator, who was then in-

volved in the assessment, always agreed with at least one of the

initial investigators.

f HF assessment: One other independent investigator assessed

TRSAs. For 18% of the subjects (2/11 subjects), TRSA1 matched VT1andfor 36% of the subjects (4/11 subjects) TRSA2 matched VT2 (Ta-

ble 2). In the other cases TRSA1 and TRSA2, respectively, had one,

two, or more stage lags (Table 2). For one subject TRSA1 could not

be assessed (Fig. 3).

HF · f HF assessment: One other independent investigator assessed

HFTs. For 81.8% of the subjects (9/11 subjects), HFT1 matched VT1and HFT2 matched VT2 (Table 2). HFT1 did not match VT1 for two

subjects, the difference corresponded to one stage lag for one

subject and two stage lags for the other subject (Table 2). HFT2did not match VT2 for two subjects, the difference always corre-

sponded to one stage lag (Table 2).

Comparison and relationships between ventilatory and

HF · f HF thresholds

There were no significant differences between the absolute

power at VT1, nor at HFT1 (219 ± 45 vs. 220 ± 48 W, p = 0.975, Ta-

ble 3) nor between the absolute power at VT2 and at HFT2 (293 ±

45 vs. 294± 48 W, p = 0.956, Table 3). When the different thresh-

olds were expressed as a percentage of PV̇ O2max, there were no

significant differences between the relative power at VT1 neither

at HFT1 (63±7 vs. 64±8% PV̇ O2max, p = 0.789, Table 3) nor be-

tween the relative power at VT2 and at HFT2 (80± 6 vs. 81 ± 7%

PV̇ O2max, Table 3). Linear regression analysis showed a strong cor-

relation in absolute and relative (% PV̇ O2max) terms between VT1vs. HFT1 (absolute: r = 0.97, r

2 = 0.94; relative r = 0.92, r2 = 0.85,

p < 0.001, Table 3) and VT2 vs. HFT2 (absolute; r = 0.98, r2 = 0.97;

relative r = 0.93, r2 = 0.87, p < 0.001, Table 3). The results of the

Bland-Altman plots are illustrated in Fig. 4. The standard devia-

tion for the difference between VT1 vs. HFT1 and VT2 vs. HFT2

Fig. 3 Typical example of a non-detection of TRSA1 from the same subject used in Fig. 2. Each HRV data pointcorresponds to a 20-s interval. X-axis: time (seconds). Left Y-axis, f HF (solid line, Hz). Right Y-axis: HF· f HF (dashedline, ms2 ·Hz). Since f HF vs. time is quasi linear (r

2 = 0.931) TRSA1 could not be assessed whereas TRSA2, HFT1, andHFT2 could be assessed. Since each threshold is given as a power stage and there is one power stage by minute,each threshold was given at the nearest power stage of the corresponding abrupt increase. However, a shortdouble arrow was added on the graph, corresponding to each accurate threshold: HFT1 and HFT2 on the HF· f HFcurve at + 20 s and for TRSA2 on the f HF curve at – 20 s of the power stage threshold.



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Table 3 Comparison between ventilatory vs. HF · f HF thresholds and ventilatory vs. f HF thresholds by Student’s t -test and linear regression an-alysis. Significance (p) for t -test, linear regression significance (p), and correlation coefficients (r) between: VT1 vs. HFT1, VT2 vs. HFT2,VT1 vs. TRSA1, and VT2 vs. TRSA2

Ventilatory thresholds HRV thresholds

from HF · f HF

Student’s

t-test

Linear regression

analysis

HRV thresholds

from f HF

Student’s

t-test

Linear regression

analysis

p r p p r p

VT 1 (W) 219 ± 45 HFT1 (W) 220 ± 48 0.975 0.97 < 0.001 TRSA1 (W) 213 ± 56 0.662 0.69 < 0.05

VT 2 (W) 293 ± 45 HFT2 (W) 294 ± 48 0.956 0.98 < 0.001 TRSA2 (W) 300 ± 58 0.445 0.89 < 0.001

VT 1 (% PV ̇O2max ) 64 ± 7 HFT1 % 64 ± 8 0.789 0.92 < 0.001 TRSA1 % 60 ± 11 0.321 0.26 0.47

VT 2 (% PV ̇O2max ) 80 ± 6 HFT2 % 81 ± 7 0.703 0.93 < 0.001 TRSA2 % 85 ± 7 0.008 0.40 0.22

Fig. 4 Bland-Altman plots: difference against average of threshold power output. Top: first and bottom: second ventilatory thresholds. Left:HFTs detection from HF· f HF index and right: TRSAs detection from f HF alone. Center dashed line equals mean difference between ventilatory andHRV threshold power output. The upper and lower dashed lines represent mean difference ± 1.96 times the standard deviation of the difference.

Table 2 VT 1 vs. HFT1, VT2 vs. HFT2, VT 1 vs. TRSA1, and VT2 vs. TRSA2 matching

N = 11 Matching 1 stage difference 2 stages difference 3 or more No detection

VT 1 vs. HFT 1 81.82%

9 subjects

9.09%

1 subject

9.09%

1 subject

0.00%

0 subject

0.00%

0 subject

VT 2 vs. HFT 2 81.82%

9 subjects

18.18%

2 subjects

0.00%

0 subject

0.00%

0 subject

0.00%

0 subject

VT 1 vs. T RSA1 18.18%2 subjects 18.18%2 subjects 27.27%3 subjects 18.18%3 subjects 9.09%1 subject

VT 2 vs. T RSA2 36.36%

4 subjects

36.36%

4 subjects

18.18%

2 subjects

9.09%

1 subject

0.00%

0 subject


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were respectively 12.3 and 9.2 watts. No hysteresis effect of the

average output on the differences between VTs and HFTs thresh-

olds was observed.

Comparison and relationships between ventilatory and f HFthresholds

There were no significant differences between the absolute

power at V T1 and at TRSA1 (236 ± 46 vs. 226 ± 56 W, p = 0.662, Ta-

ble 3) nor between the absolute power at VT2 and at TRSA2(217 ± 51 vs. 315 ± 58 W, p = 0.445, Table 3). When the different

thresholds are expressed as a percentage of PV̇ O2max, there were

no significant differences between the relative power at VT 1 and

at HFT1 (63±7 vs. 60±11% PV̇ O2max,, Table 3) nor between the

relative power at VT2 and at HFT2 (80 ± 6 vs. 85± 7% PV̇ O2max,

p = 0.100, Table 3). Linear regression analysis showed a correla-

tion between VT1 vs. HFT1 (r= 0.69, r2 = 0.48, p < 0.05, Table 3)

and VT2 vs. HFT2 in absolute terms (r = 0.89, r2

= 0.79, p < 0.001,Table 3), but when expressed as a percentage of PV̇ O2max, neither

VT1 nor VT2 could be predicted by respectively TRSA1 and TRSA2(VT1% vs. TRSA1%: r = 0.26, r

2 = 0.07 and VT2% vs. TRSA2%: r = 0.40,

r2 = 0.16, n. s., Table 3). The results of the Bland-Altman plots are

illustrated in Fig. 4. It reveals that TRSA1 underestimates VT1whereas TRSA2 overestimates VT2. The standard deviations for

the difference between VT1 vs. TRSA1 and VT2 vs. TRSA2 were re-

spectively 41.4 and 26.6 watts.

Discussion

The present study shows no significant difference between the

ventilatory thresholds assessed from ventilatory signals and

from the ECG signal (VT1 vs. HFT1 and TRSA1; VT2 vs. HFT2 and

TRSA2, n.s.). Thus, the HRV thresholds can be detected from f HF[2,5] but also from HF· f HF. The discussion will now focus on two

main parts. The first part will compare the two HRV assessment

methods developed in this paper ( f HF vs. HF · f HF assessment). The

second part will discuss the advantage of using HF· f HF for detect-

ing HFT1 and HFT2 (HF· f HF) and the physiological mechanisms

linked to its behavior during the incremental exhaustive test.

HRV assessments: f HF vs. HF· f HF in relation to VT1 detection and

in absolute terms, VT1 can be predicted by HFT1 more accurately

than TRSA1 (r = 0.97 vs. r = 0.69, Table 3). When expressed as a per-

centage of PV̇ O2max, if VT1 can be predicted by HFT1 (r = 0.92, Ta-

ble 3), VT1 cannot be predicted by TRSA1 (r = 0.26, Table 3). Sec-

ondly, since the difference with VT1 is lower for HFT1 than TRSA1

(HFT1–VT1: 0.45±12.3W vs. TRSA1–VT1: – 13.5 ± 41.4 W, Fig. 4),the Bland-Altman analysis reveals a better accuracy in the HF · f HFdetection than f HF alone. Thirdly, VT1 matches HFT1 in 81.8% of

the subjects (9/11 subjects) whereas VT1 match TRSA1 in 18.0% of

the subjects (2/11 subjects). In addition, TRSA1 could not be de-

tected for one subject (Fig. 3). Therefore, from these results, the

HRV assessment of VT1 is more accurate when using the HF· f HFindex than the f HF alone.

In relation to VT2 detection and in absolute terms, VT2 can be de-

tected by HFT2 moreaccurately thanTRSA2 (r= 0.98vs. r = 0.45, Ta-

ble 3). Firstly, in terms of absolute value VT 2 can be predicted by

HFT2 more accurately than TRSA2 (r = 0.98 vs. r = 0.45, Table 3).When expressed as a percentage of PV̇ O2max, if VT2 can be pre-

dicted by HFT2 (r = 0.93, Table 3), VT2 cannot be predicted TRSA2(r = 0.4, Table 3). Secondly, the Bland-Altman analysis reveals a

better accuracy in the HF· f HF detection than the f HF alone because

the difference with VT2 is lower for HFT2 than TRSA2 (HFT2 – VT2:

0.91 ± 9.2 W vs. TRSA2 – VT2 = 18.18 ± 26.6 W, Fig. 4). Thirdly, VT2matches HFT2 in 81.8% of the subjects (9/11 subjects) whereas

VT2 matches TRSA2 in 36.4 % of the subjects (4/11 subjects). There-

fore, regarding these results, as for VT1, the HRV assessment of

VT2 is more accurate when using HF· f HF index than f HF alone.

The choice of HF· f HF index for the ventilatory thresholds detec-

tion: The second point of the discussion considers the choice of

HF · f HF index which enabled the assessment of the ventilatory

thresholds. For a better understanding, the discussion about the

assessment of V T1 will be dissociated from the VT2 assessment.

What could be the physiological mechanisms involved in the first

increase in HF· f HF allowing HFT1 detection? And why this index

is more adequate than f HF alone to detect VT1? The detection of

HFT1 is linked to the two concomitant increases in HF and f HF.

The former considers the HF energy changes. It has been shown

that during an incremental exercise, when RR decreases the

overall HRV decreases [15,26,32,34,37]. As a result, just before

VT1 was reached, HF energy is minimal [6]. In contrast, when

VT1 is exceeded, HF increases progressively (Fig.1). During heavy

Fig. 5 Typical example of the non-detection of thefirst ventilatory thresholdfrom HF index alone. EachHRV data point correspondsto a 20-s interval; X-axis:time (seconds); left Y-axis:HF · f HF (dotted line, ms

2·Hz);right Y-axis: (HF, solid line,ms2). The determination of VTs from the HF curve could

not be assessed whereasHFT1 and HFT2 could be de-tected from the HF · f HFcurve.



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exercise cardiac vagal control is no longer effective [30]. While

vagal withdrawal is conflicting with an increase in HF energy,

the hyperpnea (concomitant increase in Vt and BF) [10,18] in-

duced by the overstepping of VT1 could be the result of a me-

chanical effect on the sinus node, inducing an increase in HF syn-

chronous with VT1 [6,9,11,12]. Blain et al. [6] found that, when

expressed as a percentage of V̇ O2peak, HF energy is minimal

around 61– 62%. This result is consistent with the present study

results: HF is minimal around 60% PV̇ O2max. However, beyond

64% PV˙ O2max (beyond the detected HFT1 and VT1 of this study)the hyperpnea induces a double effect on HF· f HF: on the one

hand, an increase in HF by mechanical effect and on the other

hand an increase in f HF linked to BF increase. There is no physio-

logical argument that the minimal pointof HF would be concom-

itant with VT1. However, the first increase after HF had reached a

minimum is probably linked to the overstepping of VT1. Unfortu-

nately, this increase in HF is sometimes not very marked and the

VT1 detection is then difficult (Fig. 5). However, when VT1 is ex-

ceeded, f HF increases, but as for the HF concomitant increase, the

expected increase in f HF is sometimes linear (Fig. 3) and the VT1detection is then difficult or even impossible. In contrast, when

multiplying HF by f HF the concomitant increase in both HF and

f HF is amplified just after the overstepping of VT1. Consequently,the first ventilatory threshold assessment is easier and more ac-

curate from HF · f HF than from f HF or HF alone.

Similar questions can be addressed for the VT2 assessment. What

could be the physiological mechanisms involved in the second

nonlinear increase in HF· f HF allowing HFT2 detection? And why

this index is more adequate than f HF alone to detect VT2? Regard-

ing f HF, at this exercise intensity, the further increase in minute

ventilation (V̇E) is a consequence of an increasing breathing fre-

quency while Vt remains maximal and constant [10,18]. Conse-

quently, when VT2 is exceeded f HF increases abruptly (Fig. 3). It

is then possible to detect VT2 from f HF alone [5]. Regarding HF,Perlini et al. [29] have shown an increase in HF energy when

breathing frequency increases and tidal volume remained con-

stant, in anesthetized, vagotomized, and mechanically ventilated

rabbits. This effect was enhanced at higher tidal volume. Thus,

the increase in breathing frequency during heavy exercise could

induce an increase in the RSA amplitude and consequently an in-

crease in HF energy synchronous with BF. According to the above

mentioned studies [6,11,12,29], it seems possible that the in-

crease in HF energy observed when VT2 is exceeded could be

the result of a mechanical effect of the increasing breathing fre-

quency on the heart. Briefly, the overstepping of VT2 entails a

combined increase in both HF and f HF. Thus, the curve of the

product of HF by f HF plotted vs. the work rate provides a more

pronouncedincrease at VT2 than the curve of f HF alone. Therefore,

the detection of VT2 fromHF· f HF is easier and more accurate than

from f HF alone (Fig. 4, Tables 2, 3).

Tosum up, during an incremental protocol, the increase in HF en-

ergy and f HF frequency, together with the increasing exercise in-

tensity could induce two successive nonlinear increases corre-

sponding to VT1 (HFT1) and VT2 (HFT2) (Figs. 1,2). However, at

high work loads, neither the expected increase in HF, nor the

two nonlinear increases in f HF were clearly observed in a few sub-

jects ([5], Figs. 3, 5), whereas the product of HF · f HF vs. work load

showed the two expected nonlinear increases. Therefore, the use

of the product of HF · f HF accentuates the expected two successive

nonlinear increases when the exercise intensity oversteps the

ventilatory thresholds. Consequently, HF· f HF is considered to be

a more effective index to assess the ventilatory thresholds from

HRV than HF or f HF alone.

Conclusion

This study has confirmed that the ventilatory thresholds can bedetected from the cardiac RR series using HRV time frequency

analysis during an incremental exercise test in athletes. In addi-

tion, it has been shown that HF· f HF provides a reliable index for

this assessment. Therefore, this study proposed a noninvasive

method of VTs detection from a simple ECG without any use of

expensive ventilatory device. Thus, the assessment of both HFT1and HFT2 from HRV could provide a substitute for the respiratory

methods when the breathing analysis is not available. Although,

HRV thresholds have been detected by different independent ex-

perts, further studies could be conducted to implement algo-

rithms for the automated detection of the HRV thresholds.

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