Assessment of Ventilatory Thresholds from Heart Rate Variability in ...

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Dec 5, 2005 - tion ventilatory threshold” resulting from the hyperpnea elicited by the increase in the CO2 metabolic production linked to the ex- ercise intensity ...
F. Cottin1 P.-M. Leprêtre1 P. Lopes1

Assessment of Ventilatory Thresholds from Heart Rate Variability in Well-Trained Subjects during Cycling

Y. Papelier3 C. Médigue2 V. Billat1

The purpose of this study was to implement a new method for assessing the ventilatory thresholds from heart rate variability (HRV) analysis. ECG, V˙O2, V˙CO2, and V˙E were collected from eleven well-trained subjects during an incremental exhaustive test performed on a cycle ergometer. The “Short-Term Fourier Transform” analysis was applied to RR time series to compute the high frequency HRV energy (HF, frequency range: 0.15 – 2 Hz) and HF frequency peak (fHF) vs. power stages. For all subjects, visual examination of ventilatory equivalents, fHF, and instantaneous HF energy multiplied by fHF (HF · fHF) showed two nonlinear increases. The first nonlinear increase corresponded to the first ventilatory threshold (VT1) and was associated with the first HF threshold (TRSA1 from fHF and HFT1 from HF · fHF detection). The second nonlinear increase represented the second ventilatory threshold (VT2) and was associated with the second HF threshold

(TRSA2 from fHF and HFT2 from HF · fHF detection). HFT1 , TRSA1, HFT2, and TRSA2 were, respectively, not significantly different from VT1 (VT1 = 219 ± 45 vs. HFT1 = 220 ± 48 W, p = 0.975; VT1 vs. TRSA1 = 213 ± 56 W, p = 0.662) and VT2 (VT2 = 293 ± 45 vs. HFT2 = 294 ± – 48 W, 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 r2 = 0.79, p < 0.001). This study confirms that ventilatory thresholds can be determined from RR time series using HRV time-frequency analysis in healthy welltrained subjects. In addition it shows that HF · fHF provides a more reliable and accurate index than fHF alone for this assessment.

Physiology & Biochemistry

Abstract

Key words Exercise · respiratory components · time frequency analysis · short-term fourier transform 1

Abbreviations V˙E V˙O2 V˙CO2 Vt V˙E/V˙O2, V˙E/V˙CO2 BF HRV

1

ventilatory flow oxygen uptake carbon dioxide output tidal volume ventilatory equivalents breathing frequency heart rate variability

HF fHF VT1 VT2 HFT1 HFT2 TRSA1 TRSA2

high frequency spectral energy frequency peak of HF-HRV first ventilatory threshold detected from ventilatory equivalents second ventilatory threshold detected from ventilatory equivalents first ventilatory threshold detected from HF · fHF second ventilatory threshold detected from HF · fHF first ventilatory threshold detected from fHF second ventilatory threshold detected from fHF

Affiliation 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, France 3 Laboratory of Physiology, Medicine Faculty, University of Paris XI, E. F. R., Hôpital Antoine Béclère, Clamart Cedex, France

Correspondence Franc¸ois Cottin, PhD · Department of Sport and Exercise Science · University of Evry · Boulevard F. Mitterrand · 91025 Evry Cedex · France · Phone: + 33 0169 64 48 81 · Fax: + 33 0169 64 48 95 · 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

Introduction Table 1 Characteristics of the subjects (n = 11)

Physiology & Biochemistry 2

Nowadays, the assessment of ventilatory thresholds in athletes is used by some coaches in order to build their specific training programs [1,19, 25]. The measurement of the breathing components 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 observation of the ventilatory equivalents (V˙E/V˙O2 and V˙E/V˙CO2) curves vs. power during an incremental exercise test on a cycle ergometer. The first ventilatory threshold (VT1) is called “adaptation ventilatory threshold” resulting from the hyperpnea elicited by the increase in the CO2 metabolic production linked to the exercise intensity above anaerobic threshold. As a result, V˙E/V˙O2 nonlinearly increases while V˙E/V˙CO2 remains constant. The second ventilatory threshold (VT2) is called: “maladjustment ventilatory threshold” or “respiratory compensation point”. Since the hyperpnea is not sufficient to eliminate the CO2 metabolic production, V˙E increases whereas V˙CO2 remains constant leading to a drastic increase in V˙E/V˙CO2 until exhaustion. Furthermore, heart rate variability (HRV) has been broadly investigated during exercise [3, 6, 8, 9,12,13, 33, 38]. During short-term recordings 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 breathing rate and vagal cardiovascular control. It ranges from 0.15 to fmax Hz during exercise. fmax is the maximal frequency induced by the sampling scale of the RR signal. It is well known that total spectral energy decreases when exercise intensity increases [9,15, 26, 33]. Recently, in healthy humans [6,12] and trotting horses [11], it has been shown that, when the exercise intensity is lower than the intensity at ventilatory 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. Furthermore, 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 (fHF) corresponds closely to the breathing frequency [6,12]. Since breathing frequency (BF) increases when VT1 is exceeded and abruptly increases when VT2 is exceeded, fHF could follow a behavior similar to that of the breathing frequency. Recent studies have shown that VT1 could be assessed from BF [21] and then from fHF [2]. Furthermore, Blain et al. [5] have demonstrated that it was possible to detect both VTs from fHF (TRSA) when TRSA1 was corresponding to VT1 and TRSA2 was corresponding to VT2. However, in 20 % of their data it was not possible to detect VTs [5]. In addition, among the numerous papers and methods about the ventilatory threshold assessment, only a Cottin F et al. Ventilatory Thresholds Assessment from HRV … Int J Sports Med

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

4.5

0.4

V˙O2max (ml · min–1 · kg–1)

68.2

6.3

HRV˙O2max (beats · min–1)

194.0

8.0

V˙O2max (l · min–1)

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 · fHF) 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 · fHF could attain a minimum just before VT1 was reached [6]. 2. When work rate exceeds VT1, the resulting hyperpnea could entail an increase in fHF and in HF energy by mechanical effect [6,11,12]. The resulting steeper increase in HF · fHF 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 fHF and in HF energy by mechanical effect [6,11, 29]. This second abrupt increase in HF · fHF 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 characteristics of the subjects are summarized in Table 1. Prior to participating, each subject was familiarized with the experimental procedure and informed of the risks associated with the 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 electronically braked cycle ergometer (Ergoline 900, Marquette-Hellige, Fribourg, Germany) in an air-conditioned room. Since the cycle ergometer performance level was different between cy-

clists and triathletes, the chosen incremental protocol was different. After a two-minute warm-up at 60 watts for triathletes or 75 watts for cyclists, each subject performed a 15 watts · min–1 incremental test for triathletes or 25 watts · min–1 for cyclists. Seat and handlebar heights were set for each subject and kept constant 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 (ADInstruments Ltd, Chalgrove Oxfordshire, UK) with a sampling frequency of 1000 Hz. The R wave peak occurrence was assessed using a threshold technique provided with the Chart5 program (Chart5, v5.0.2 for Power Lab, ADInstruments Ltd, Chalgrove Oxfordshire, 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 processed by computation of interpolated or extrapolated values. Gas measurements V˙O2, V˙CO2, and V˙E 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, Seattle). Anthropometric measurements Height and weight were measured before each test. Four skinfold measurements were taken (triceps, biceps, suprailiac, subscapular) with % body fat computed using the Durnin and Womersley’s formula [16].

Time frequency analysis Since, RR series were not stationary during the incremental protocol, 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 performed on this windowed signal. The same analysis window is applied to the next signal block, and so on until the end of the processed 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 following: – 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 successive spectrogram windows were spaced by 12 successive RR intervals 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 applied on each STFT window [11]. Then a Hamming window was applied on each STFT segment before computing FFT [20].

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

Physiology & Biochemistry

Fig. 1 Typical example of a periodogram from a subject (top) and the associated computed spectrogram (bottom) using Short Term Fourier Transform. The cycle ergometer power increased from 110 at the beginning of the periodogram (t = 360 s) to 450 watts at the end of both graphs. Spectrogram axis: X-axis: time (seconds). Y-axis: frequency (Hz). Z-axis, Power Spectral Density (P. S. D., ms2 · Hz–1). The spectrogram shows both frequency bands changes versus time. The LF frequency remains constant whereas its power decreases and almost disappears as soon as 800 s were reached. The HF frequency (fHF) remains constant from 420 to 600 s, and then it begins to increase. Immediately after 800 s, fHF shows a second steeper increase while the HF energy increases.

3

Physiology & Biochemistry 4

Fig. 2 Typical example of both ventilatory thresholds assessment from ventilatory components ˙O2, V ˙ E/V ˙CO2), and from HRV components (HF · fHF). Each gas-exchange and HRV data point ˙ E/V (V ˙ E/V ˙ O2 corresponds to a 20-s interval. X-axis: time (seconds). Left Y-axis, ventilatory equivalents (V ˙ E/V ˙CO2, solid line). Right Y-axis, HF · fHF (dotted line, ms2 · Hz) and HF · BF (dashed line, and V ms2 · Hz). Since BF and fHF are similar, HF · fHF and HF · BF are obviously similar. The first ventilatory ˙ E/V ˙O2 while V ˙ E/V ˙CO2 remains conthreshold (VT1) corresponds to the first substantial increase in V ˙ E/V ˙ O2 stant. The second ventilatory threshold (VT2) corresponds to the steeper increase in both V ˙ E/V ˙CO2. The first HF-HRV threshold (HFT1) corresponds to the first increase in HF · fHF, the secand V ond HF-HRV threshold (HFT2) corresponds to the second abrupt increase in HF · fHF. 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 short double ˙ E/V ˙O2 curve at arrow was added on the graph, corresponding to each accurate threshold: VT1 on V ˙ E/ – 20 s and HFT1 on the HF · fHF curve at + 20 s of the power stage threshold. Also for VT2 on the V ˙CO2 curve at + 20 s and on the HF · fHF curve also at + 20 s of the power stage threshold. V

The spectral energy was computed in HF ranges by integrating the power spectral density (PSD) for each spectrum of the spectrogram as following:

HF ¼

fX max

PSD  f ðms2 Þ

f ¼0:15

fmax is given by Shannon sampling theorem. The rule that “All the information in a signal, band-limited to a frequency of fmax, can be captured in its samples taken a rate of greater than 2 · fmax” is known as Shannon’s sampling theorem. The critical frequency fmax is known as Nyquist rate. fmax = 1/(2 · Δt), Δt being the sampling scale of the RR signal. In the present study the signal sampling was Δt = 0.25 s, thus fmax = 2 Hz. In addition, the assessment of the instantaneous HF peak (fHF) 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 fHF values every 3 seconds. However, for an optimal assessment of the VTs [19] and to synchronize HRV and ventilatory data, the HRV components 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 Cottin F et al. Ventilatory Thresholds Assessment from HRV … Int J Sports Med

and ventilatory data on the same graph ([19], Fig. 2). Since the Wasserman method appears to be a consistent predictor for cycling performance in well-trained subjects with regard to reliability and validity [1,19], it was used to determine VT1 and VT2 [25, 36]. Therefore, V˙E/V˙O2 and V˙E/V˙CO2 were plotted vs. work rate during the incremental exercise test (Fig. 2). VT1 corresponds to a first nonlinear increase in the V˙E/V˙O2 curve while the V˙E/V˙CO2slope remains constant [35, 36]. In addition, VT2 is indicated by the nonlinear increase in the V˙E/V˙CO2 curve concomitant to a second strong increase in V˙E/V˙O2 with further increase in exercise intensity [35, 36]. Based on the above criteria, two experienced researchers have independently assessed the ventilatory thresholds. When there was a disagreement, a third experienced 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 compared two VTs assessment methods: 1. From fHF: fHF 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 fHF plotted vs. the work rate by an independent investigator. The first HF threshold (TRSA1) corresponded to the last point before a first increase in fHF. The second HF threshold (TRSA2) corresponded to a second nonlinear increase in fHF (Fig. 3).

2. From HF · fHF. 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 fHF. Thus, the VTs could be assessed from the product of HF by fHF (HF · fHF). As it was mentioned above, HF · fHF successive values were then averaged to provide a data point for each 20-s period synchronous with the ventilatory data. Therefore, HF thresholds were detected from the curve of HF · fHF plotted vs. the work rate by an independent investigator. The first HF threshold (HFT1) corresponded to the first nonlinear increase in HF · fHF after it has reached a minimum. The second HF threshold (HFT2) corresponded to a second nonlinear increase in HF · fHF (Fig. 2). Statistical analysis The Student’s t-test (Sigmastat 2.03, Jandel Scientifics, San Rafael, CA, USA, 1997) was used to compare the respective ventilatory and HF-HRV thresholds (VT1 vs. HFT1, VT2 vs. HFT2, VT1 vs. TRSA1, and VT2 vs. TRSA2). Linear regression and the Pearson Product Moment Correlation (Sigmastat 2.03, Jandel Scientifics, San Rafael, CA, USA, 1997) were used to test the correlation between VT1 vs. HFT1, VT2 vs. HFT2, VT1 vs. TRSA1, and VT2 vs. TRSA2. BlandAltman [7] plots were conducted to illustrate the relationship between ventilatory thresholds (VT1 and VT2) and their respective 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 assessment in one subject from ventilatory components (V˙E/V˙O2, V˙E/ V˙CO2) and from HF · fHF. 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 involved in the assessment, always agreed with at least one of the initial investigators. fHF assessment: One other independent investigator assessed TRSAs. For 18 % of the subjects (2/11 subjects), TRSA1 matched VT1 and for 36 % of the subjects (4/11 subjects) TRSA2 matched VT2 (Table 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 · fHF assessment: One other independent investigator assessed HFTs. For 81.8 % of the subjects (9/11 subjects), HFT1 matched VT1 and 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). HFT2 did not match VT2 for two subjects, the difference always corresponded to one stage lag (Table 2). Comparison and relationships between ventilatory and HF · fHF thresholds There were no significant differences between the absolute power at VT1, nor at HFT1 (219 ± 45 vs. 220 ± 48 W, p = 0.975, Table 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 thresholds were expressed as a percentage of PV˙O2max, there were no significant differences between the relative power at VT1 neither at HFT1 (63 ± 7 vs. 64 ± 8 % PV˙O2max, p = 0.789, Table 3) nor between the relative power at VT2 and at HFT2 (80 ± 6 vs. 81 ± 7 % PV˙O2max, Table 3). Linear regression analysis showed a strong correlation in absolute and relative (% PV˙O2max) terms between VT1 vs. HFT1 (absolute: r = 0.97, r2 = 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 deviation for the difference between VT1 vs. HFT1 and VT2 vs. HFT2 Cottin F et al. Ventilatory Thresholds Assessment from HRV … Int J Sports Med

Physiology & Biochemistry

Fig. 3 Typical example of a non-detection of TRSA1 from the same subject used in Fig. 2. Each HRV data point corresponds to a 20-s interval. X-axis: time (seconds). Left Y-axis, fHF (solid line, Hz). Right Y-axis: HF · fHF (dashed line, ms2 · Hz). Since fHF vs. time is quasi linear (r2 = 0.931) TRSA1 could not be assessed whereas TRSA2, HFT1, and HFT2 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 short double arrow was added on the graph, corresponding to each accurate threshold: HFT1 and HFT2 on the HF · fHF curve at + 20 s and for TRSA2 on the fHF curve at – 20 s of the power stage threshold.

5

Table 2 VT1 vs. HFT1, VT2 vs. HFT2, VT1 vs. TRSA1, and VT2 vs. TRSA2 matching N = 11

Matching

1 stage difference

2 stages difference

3 or more

No detection

VT1 vs. HFT1

81.82% 9 subjects

9.09% 1 subject

9.09% 1 subject

0.00% 0 subject

0.00% 0 subject

VT2 vs. HFT2

81.82% 9 subjects

18.18% 2 subjects

0.00% 0 subject

0.00% 0 subject

0.00% 0 subject

VT1 vs. TRSA1

18.18% 2 subjects

18.18% 2 subjects

27.27% 3 subjects

18.18% 3 subjects

9.09% 1 subject

VT2 vs. TRSA2

36.36% 4 subjects

36.36% 4 subjects

18.18% 2 subjects

9.09% 1 subject

0.00% 0 subject

Physiology & Biochemistry

Table 3 Comparison between ventilatory vs. HF · fHF thresholds and ventilatory vs. fHF thresholds by Student’s t-test and linear regression analysis. 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 · fHF

Student’s t-test

Linear regression analysis

p

r

VT1 (W)

219 ± 45

HFT1 (W)

220 ± 48

0.975

0.97

VT2 (W)

293 ± 45

HFT2 (W)

HRV thresholds from fHF

p < 0.001

TRSA1 (W)

213 ± 56

Student’s t-test

Linear regression analysis

p

r

0.662

0.69

< 0.05

p

294 ± 48

0.956

0.98

< 0.001

TRSA2 (W)

300 ± 58

0.445

0.89

< 0.001

VT1 (% PV˙O2max)

64 ± 7

HFT1 %

64 ± 8

0.789

0.92

< 0.001

TRSA1 %

60 ± 11

0.321

0.26

0.47

VT2 (% PV˙O2max)

80 ± 6

HFT2 %

81 ± 7

0.703

0.93

< 0.001

TRSA2 %

85 ± 7

0.008

0.40

0.22

6

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

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

Fig. 5 Typical example of the non-detection of the first ventilatory threshold from HF index alone. Each HRV data point corresponds to a 20-s interval; X-axis: time (seconds); left Y-axis: HF · fHF (dotted line, ms2·Hz); right Y-axis: (HF, solid line, ms2). The determination of VTs from the HF curve could not be assessed whereas HFT1 and HFT2 could be detected from the HF · fHF curve.

Comparison and relationships between ventilatory and fHF thresholds There were no significant differences between the absolute power at VT1 and at TRSA1 (236 ± 46 vs. 226 ± 56 W, p = 0.662, Table 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 PV˙O2max, there were no significant differences between the relative power at VT1 and at HFT1 (63 ± 7 vs. 60 ± 11 % PV˙O2max,, Table 3) nor between the relative power at VT2 and at HFT2 (80 ± 6 vs. 85 ± 7 % PV˙O2max, p = 0.100, Table 3). Linear regression analysis showed a correlation 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 PV˙O2max, neither VT1 nor VT2 could be predicted by respectively TRSA1 and TRSA2 (VT1% vs. TRSA1%: r = 0.26, r2 = 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 VT1 whereas TRSA2 overestimates VT2. The standard deviations for the difference between VT1 vs. TRSA1 and VT2 vs. TRSA2 were respectively 41.4 and 26.6 watts.

than TRSA1 (r = 0.97 vs. r = 0.69, Table 3). When expressed as a percentage of PV˙O2max, if VT1 can be predicted by HFT1 (r = 0.92, Table 3), VT1 cannot be predicted by TRSA1 (r = 0.26, Table 3). Secondly, since the difference with VT1 is lower for HFT1 than TRSA1 (HFT1–VT1: 0.45 ± 12.3 W vs. TRSA1–VT1: – 13.5 ± 41.4 W, Fig. 4), the Bland-Altman analysis reveals a better accuracy in the HF · fHF detection than fHF 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 detected for one subject (Fig. 3). Therefore, from these results, the HRV assessment of VT1 is more accurate when using the HF · fHF index than the fHF alone. In relation to VT2 detection and in absolute terms, VT2 can be detected by HFT2 more accurately than TRSA2 (r = 0.98 vs. r = 0.45, Table 3). Firstly, in terms of absolute value VT2 can be predicted by HFT2 more accurately than TRSA2 (r = 0.98 vs. r = 0.45, Table 3). When expressed as a percentage of PV˙O2max, if VT2 can be predicted 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 · fHF detection than the fHF 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, VT2 matches HFT2 in 81.8 % of the subjects (9/11 subjects) whereas VT2 matches TRSA2 in 36.4 % of the subjects (4/11 subjects). Therefore, regarding these results, as for VT1, the HRV assessment of VT2 is more accurate when using HF · fHF index than fHF alone.

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 fHF [2, 5] but also from HF · fHF. The discussion will now focus on two main parts. The first part will compare the two HRV assessment methods developed in this paper (fHF vs. HF · fHF assessment). The second part will discuss the advantage of using HF · fHF for detecting HFT1 and HFT2 (HF · fHF) and the physiological mechanisms linked to its behavior during the incremental exhaustive test. HRV assessments: fHF vs. HF · fHF in relation to VT1 detection and in absolute terms, VT1 can be predicted by HFT1 more accurately

The choice of HF · fHF index for the ventilatory thresholds detection: The second point of the discussion considers the choice of HF · fHF index which enabled the assessment of the ventilatory thresholds. For a better understanding, the discussion about the assessment of VT1 will be dissociated from the VT2 assessment. What could be the physiological mechanisms involved in the first increase in HF · fHF allowing HFT1 detection? And why this index is more adequate than fHF alone to detect VT1? The detection of HFT1 is linked to the two concomitant increases in HF and fHF. 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 Cottin F et al. Ventilatory Thresholds Assessment from HRV … Int J Sports Med

Physiology & Biochemistry

were respectively 12.3 and 9.2 watts. No hysteresis effect of the average output on the differences between VTs and HFTs thresholds was observed.

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Physiology & Biochemistry 8

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] induced by the overstepping of VT1 could be the result of a mechanical effect on the sinus node, inducing an increase in HF synchronous with VT1 [6, 9,11,12]. Blain et al. [6] found that, when expressed as a percentage of V˙O2peak, HF energy is minimal around 61 – 62 %. This result is consistent with the present study results: HF is minimal around 60 % PV˙O2max. However, beyond 64 % PV˙O2max (beyond the detected HFT1 and VT1 of this study) the hyperpnea induces a double effect on HF · fHF: on the one hand, an increase in HF by mechanical effect and on the other hand an increase in fHF linked to BF increase. There is no physiological argument that the minimal point of HF would be concomitant with VT1. However, the first increase after HF had reached a minimum is probably linked to the overstepping of VT1. Unfortunately, this increase in HF is sometimes not very marked and the VT1 detection is then difficult (Fig. 5). However, when VT1 is exceeded, fHF increases, but as for the HF concomitant increase, the expected increase in fHF is sometimes linear (Fig. 3) and the VT1 detection is then difficult or even impossible. In contrast, when multiplying HF by fHF the concomitant increase in both HF and fHF is amplified just after the overstepping of VT1. Consequently, the first ventilatory threshold assessment is easier and more accurate from HF · fHF than from fHF or HF alone.

of the product of HF · fHF accentuates the expected two successive nonlinear increases when the exercise intensity oversteps the ventilatory thresholds. Consequently, HF · fHF is considered to be a more effective index to assess the ventilatory thresholds from HRV than HF or fHF alone.

Conclusion This study has confirmed that the ventilatory thresholds can be detected from the cardiac RR series using HRV time frequency analysis during an incremental exercise test in athletes. In addition, it has been shown that HF · fHF 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 HFT1 and 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 experts, further studies could be conducted to implement algorithms for the automated detection of the HRV thresholds.

References 1

Similar questions can be addressed for the VT2 assessment. What could be the physiological mechanisms involved in the second nonlinear increase in HF · fHF allowing HFT2 detection? And why this index is more adequate than fHF alone to detect VT2? Regarding fHF, at this exercise intensity, the further increase in minute ventilation (V˙E) is a consequence of an increasing breathing frequency while Vt remains maximal and constant [10,18]. Consequently, when VT2 is exceeded fHF increases abruptly (Fig. 3). It is then possible to detect VT2 from fHF alone [5]. Regarding HF, Perlini et al. [29] have shown an increase in HF energy when breathing frequency increases and tidal volume remained constant, 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 increase in HF energy synchronous with BF. According to the above mentioned studies [6,11,12, 29], it seems possible that the increase in HF energy observed when VT2 is exceeded could be the result of a mechanical effect of the increasing breathing frequency on the heart. Briefly, the overstepping of VT2 entails a combined increase in both HF and fHF. Thus, the curve of the product of HF by fHF plotted vs. the work rate provides a more pronounced increase at VT2 than the curve of fHF alone. Therefore, the detection of VT2 from HF · fHF is easier and more accurate than from fHF alone (Fig. 4, Tables 2, 3).

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To sum up, during an incremental protocol, the increase in HF energy and fHF frequency, together with the increasing exercise intensity could induce two successive nonlinear increases corresponding 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 fHF were clearly observed in a few subjects ([5], Figs. 3, 5), whereas the product of HF · fHF vs. work load showed the two expected nonlinear increases. Therefore, the use Cottin F et al. Ventilatory Thresholds Assessment from HRV … Int J Sports Med

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