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

The purpose of this study was to implement a new method for assessing the ventilatory thresholds from heart rate variability (HRV) analysis. ECG, VO2, VCO2, and VE 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 (T(RSA1) 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 (T(RSA2) from fHF and HFT2 from HF.fHF detection). HFT1 , T(RSA1), HFT2, and T(RSA2) were, respectively, not significantly different from VT1 (VT1 = 219 +/- 45 vs. HFT1 = 220 +/- 48 W, p = 0.975; VT1 vs. T(RSA1) = 213 +/- 56 W, p = 0.662) and VT2 (VT2 = 293 +/- 45 vs. HFT2 = 294 +/- - 48 W, p = 0.956; vs. T(RSA2) = 300 +/- 58 W, p = 0.445). In addition, when expressed as a function of power, HFT1, T(RSA1), HFT2, and T(RSA2) were respectively correlated with VT1 (with HFT1 r2 = 0.94, p < 0.001; with T(RSA1) r2 = 0.48, p < 0.05) and VT2 (with HFT2 r2 = 0.97, p < 0.001; with T(RSA2 )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 well-trained subjects. In addition it shows that HF.fHF provides a more reliable and accurate index than fHF alone for this assessment