Ventilatory thresholds (VT1 and VT2) are useful in
many fields of medicine and sports. Nevertheless, their
measurement is cumbersome and needs trained
personnel. This work proposes an alternative method to
predict VT1, VT2 and maximum loads in incremental
maximal tests based on heart rate variability (HRV)
analysis. Twelve competitive male cyclists executed an
incremental exhaustive test. During the test, RR time
series and gas concentrations were recorded. After
artifact correction the power spectrum was estimated in a
sliding window, and central frequency (CF) and
bandwidth that contains half the total power (BW) were
computed. An automatic algorithm recognized the loads
where CF and BW undergo a significant change. These
loads were used as inputs in linear regression models to
predict VT1, VT2 and maximum loads. The errors of the
predictions are similar to the load resolution.Postprint (published version

Angulo, R.

Benítez Herrera, Adolfo

Bescós, R.

García González, Miguel Ángel

Iglesias, X.

Marina, M.

Padullés, M.

Rodríguez, F.

English

Ventilatory thresholds (VT1 and VT2) are useful in

many fields of medicine and sports. Nevertheless, their

measurement is cumbersome and needs trained

personnel. This work proposes an alternative method to

predict VT1, VT2 and maximum loads in incremental

maximal tests based on heart rate variability (HRV)

analysis. Twelve competitive male cyclists executed an

incremental exhaustive test. During the test, RR time

series and gas concentrations were recorded. After

artifact correction the power spectrum was estimated in a

sliding window, and central frequency (CF) and

bandwidth that contains half the total power (BW) were

computed. An automatic algorithm recognized the loads

where CF and BW undergo a significant change. These

loads were used as inputs in linear regression models to

predict VT1, VT2 and maximum loads. The errors of the

predictions are similar to the load resolution

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Ventilatory threshold prediction by spectral analysis of heart rate variability in incremental maximal tests

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Ventilatory Threshold Prediction by Spectral Analysis of Heart Rate Variability in Incremental Maximal Tests A Benítez1,2, M A García-González1, R Angulo2, F Rodríguez2, X Iglesias2, R Bescós2, M Marina2, J M Padullés2 1Group of Biomedical and Electronic Instrumentation. Department of Electronic Engineering, Technical University of Catalonia (UPC), Barcelona, Spain 2National Physical Education Institute of Catalonia, University of Barcelona (UB), Barcelona, Spain Abstract Ventilatory thresholds (VT1 and VT2) are useful in many fields of medicine and sports. Nevertheless, their measurement is cumbersome and needs trained personnel. This work proposes an alternative method to predict VT1, VT2 and maximum loads in incremental maximal tests based on heart rate variability (HRV) analysis. Twelve competitive male cyclists executed an incremental exhaustive test. During the test, RR time series and gas concentrations were recorded. After artifact correction the power spectrum was estimated in a sliding window, and central frequency (CF) and bandwidth that contains half the total power (BW) were computed. An automatic algorithm recognized the loads where CF and BW undergo a significant change. These loads were used as inputs in linear regression models to predict VT1, VT2 and maximum loads. The errors of the predictions are similar to the load resolution.  1. Introduction The estimation of the First and Second Ventilatory Thresholds (VT1 and VT2) is useful in many fields in medicine and sport, like cardiac or pulmonary therapy or enables to program an endurance sport season [1]. VT1 and VT2 are assessed during a maximal incremental test with a gas analyzer [2]. VT1 is the ventilatory threshold and VT2 is the threshold of decompensated metabolic acidosis. VT1 is determined visually by an expert as the point where VE’/VO2’ increases nonlinearly while VE’/VCO2’ remains constant; and VT2 is the point where VCO2’ increases nonlinearly with respect to VO2’ (being VE’ the respiratory flow, VO2’ oxygen consumption and VCO2’ the carbon dioxide production). VT1 and VT2 assessment is cumbersome because a gas analyzer and trained personnel are needed. During an incremental test it is very common to measure, simultaneously with gas concentrations, the heart rate or the electrocardiogram. These measurements enable us to obtain the RR time series for further analysis of heart rate variability (HRV).  VT2 has been estimated through analysis of HRV [3],[4],[5]. Nevertheless, all these studies need the complete test and an expert observer in order to ascertain the threshold location. One drawback in the traditional spectral analysis of HRV [6] is the partition of the power spectrum in bands with constant limits. In incremental tests, the breathing frequency increases with the load so it is presumable to lie above the maximum limit of the high frequency band (typically 0.4 Hz) being the result of the power of the high frequency band completely meaningless.  The aim of this work is to propose a HRV processing methodology that predicts the load at which VT1 and VT2 occur. The signal processing is based on spectral indices that don’t require a band definition as opposed to the classical spectral indices.  2. Materials and methods Twelve competitive male cyclist (34.1±5.7 years old) participated in this study. Prior to the test, each subject was familiarized with the experimental procedure and was informed of the risks associated with the protocol. All subjects gave their written voluntary informed consent. The measurement protocol was an incremental exercise test in the upright position on an electronically braked cycle ergometer (Excalibur Sport, Lode, The Netherlands) modified with clip-in pedals. Each subject performed a 25W/min incremental test [2]. The pedaling frequency was chosen at will between 70 and 100 rpm. During the test, the RR time series was collected with a Polar RS800 (Polar Electro, Finland, OY) with a 1 kHz sampling frequency, and the gas measurement was performed with a Cosmed Quark PFT-Ergo gas analyzer (Cosmed, Italy). Before each test, ambient conditions were measured and the gas analyzers and inspiratory flowmeter were calibrated. Gas measurements were used by an expert doctor in order to estimate VT1 and VT2 loads.  ISSN 0276−6574 939 Computing in Cardiology 2010;37:939−942.Prior to processing, the artifacts of the RR time series were corrected with the software Kubios HRV 2.0 (University of Kuopio). Then, the analysis was done using a sliding window with a length of 300 beats and an incremental shift along the RR time series of one beat. Inside the window, the RR time series were first detrended with a smooth priors detrending method with そ= 100 [7] and then resampled at 10 Hz. The power spectrum was estimated using the FFT and a Hann window. To characterize the power spectrum without a band definition, we have computed the accumulated power spectrum as: ¦¦   N1iRRn1iRRRR(i)P(i)P(n)AP    (1)  where PRR(i) is the power spectrum estimation at frequency f(i) and f(N) is the Nyquist frequency (5 Hz in our case). The central frequency (CF) was defined as the frequency where APRR is equal to 0.5. The bandwidth at 50% (BW) was defined as the difference between the frequency where APRR is equal to 0.75 and the frequency where APRR is equal to 0.25. Figure 1 shows an example of the computation of both indices. For each power spectrum, the CF and BW were computed. So, for each test, time series of CF and BW indices were obtained. The central frequency threshold (CFT) was defined as the first sample since the start of the exercise where   CF(CFT)-CF(CFT-100)>0.15 Hz             (2)  so it corresponds to a drastic change in CF. The bandwidth threshold (BWT) was defined as the sample where, starting at CFT and looking backwards, the bandwidth falls below BW(CFT)/2. Finally, the loads at which CFT and BWT occur were obtained (PCFT and PBWT, respectively). PCFT and PBWT were considered as the independent variables in linear regression models to estimate the values of the maximum load (PMax), VT2 load (PVT2) and VT1 load (PVT1) estimated from the gas analyzer measurements by trained personnel. Figure 2 shows an example of all this procedure.  3. Results Table 1 shows the mean, maximum and minimum values of the loads where the different threshold occur. It is worth noting that PCFT and PBWT always occur for lower loads than the other thresholds.   Results for the linear regressions are shown in table 2. Each threshold can be estimated as:  CFTBWToi PPPP ·· 21 DD    (3)  0 1 2 3 4 500.10.20.30.40.50.60.70.80.91Frequency (Hz)Accumulated Power Spectral DensityCentral frequency: 0.32 HzBandwidth 50%:1.26 Hz Fmin 50%: 0.13 HzFmax 50%: 1.39 Hz Figure 1. Example of the computation of CF and BW corresponding to the accumulated power spectral density of a subject exercising with a mild load.  Time (min)18 20 22 24 26 28 30 32 34(s) or (Hz)0.00.20.40.60.81.0Load (W)0100200300400500VT1 VT2BWT CFT Figure 2. Example of one incremental maximal test. The thick solid line corresponds to the RR time series, the thin stepwise line corresponds to the incremental load used during the test, the dotted line shows the evolution of BW and the dashed line shows CF. VT1 and VT2 are the expert’s annotations while BWT and CFT are, as described in the text, the automatically located instants were sudden changes in CF and BW occur.  Figure 3 show the Bland-Altman plots comparing the estimated loads from equation (3) and the measured loads using the gas analyzer and the expert annotations. The plots also show two horizontal lines indicating the size of the load increments (± 25 W which is the resolution of our measurements). As seen, in most of the cases the 940difference between the annotation and the predicted load is below the load resolution.  Table 1. Mean, maximum and minimum loads measured in the twelve subjects           Table 2. Linear regression models to estimate maximum, VT2 and VT1 loads. The Standard Error of Estimate is also shown.  Load Po (W) D D SEE (W) PMax 278.2 0.299 0.384 24.3 PVT2 196.0 0.313 0.395 23.6 PVT1 203.4 -0.150 0.523 22.6   4. Discussion and conclusion Results show that BW and CF can be used to predict the load events on a maximal incremental test. For the whole twelve participants, the abrupt changes in BW and CF happened always in advance of VT1 so this methodology can be applied in real time and stop the test once CFT is detected. Nevertheless, all participants were competitive male cyclist and further studies must be done in order to ascertain if this methodology is also valid for other groups. Spectral indices without band definition could prove useful in the analysis of RR time series during exercise because the drastic physiological changes associated with physical effort cause a radical modification of the heart rate variability power spectrum. In conclusion, BW and CF can predict VT1, VT2 and maximum loads in competitive male cyclists while performing a maximal incremental test with an error comparable to the load resolution.  Acknowledgments This work was supported by MEC project PSI2008-06417-C03    Figure 3. Bland-Altman plots for the agreement of expert annotated loads (reference measurement) and predicted loads from BW and CF measurements. Horizontal lines show the resolution of the load. Top panel: maximum load. Middle panel: VT2. Bottom panel: VT1.  References [1] Barst RJ, Langleben D, Frost A, Horn EM, Oudiz R, Shapiro S, et al Sitaxsentan Therapy for Pulmonary Arterial Hypertension. Am J Respir Crit Care Med 2003;169:441-447.   [2] Wasserman K, Hansen JE, Sue DY, Whipp BJ, Casaburi R Load Max Mean Min PMax (W) 425 394.2 350 PVT2 (W) 350 329.5 300 PVT1 (W) 300 274.5 250 PBWT (W) 200 168.0 125 PCFT(W) 225 198.7 175 941Principles of exercise testing and interpretation. Second ed. Malvern, Pennsylvania: Lea &  Febiger; 1994.  [3] Anosov O, Patzak A, Kononovich Y, Persson PB High-frequency oscillations of the heart rate during ramp lead reflect the human anaerobic threshold. Eur J Appl Physiol 2000;83:388-394. [4]  Cottin F, Leprêtre PM, Lopes P, Papelier Y, Médigue C, Billat V Assessment of Ventilatory Thresholds from Heart Rate Variability in Well-Trained Subjects during Cycling. Int J Sports Med 2006;27:959–967. [5]  Cottin F, Médigue C, Lopes P, Leprêtre P-M, Heubert R, Billat V Ventilatory Thresholds Assessment from Heart Rate Variability during an Incremental Exhaustive Running Test. Int J Sports Med 2007;28:287-294.                                             [6]  Malik M Heart Rate Variability. Standards of Measurement, Physiological Interpretation, and Clinical Use. Circulation 1996;93:1043-1065. [7] Tarvainen MP, Ranta-aho PO and Karjalainen PA  An advanced detrending method with application to HRV analysis. IEEE Trans Biomed Eng  2002; 49: 172–175   Address for correspondence.  Miguel Ángel García González .  C/ Jordi Girona 1-3, Edifici C-4, Campus Nord UPC, 08034, Barcelona, Spain. magarcia@eel.upc.edu 942

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Ventilatory threshold prediction by spectral analysis of heart rate variability in incremental maximal tests

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