Anthropometric and Spirometric Characteristics of the Subjects

Two-dimensional Poincare plots wereA�also generated by plotting each R-R interval as a function of its.

Reproduction of this article is prohibited without written permission from the American College of Chest Physicians.

Correspondence to: Aurelien Pichon, PhD, Laboratoire a�?RSponses cellulaires et fonctionnelles a la��hypoxie, UFR Sante MedecineA�Biologie Humaine, 74 rue Marcel Cachin, 93017 Bobigny,A�France; previous R-R interval obtained at baseline and after MBC.

Table 1a��Anthropometric and Spirometric Characteristics of the Subjects

Parameters Responders Nonresponders
Age, yr 26 A� 10 31 A� 12
Height, cm 173 A� 10 173 A� 10
Weight, kg 69 A� 11 73 A� 12
PD20 hg 467 A� 351
FVC, L 4.9 A� 1.0 4.9 A� 0.9
FVC, % 108 A� 11 108 A� 13
FEVb L 3.8 A� 0.7 4.1 A� 0.9
FEVj % predicted 101 A� 12 108 A� 15t
Physical activity, h/wk 8 A� 7 4 A� 4t

Data are presented as mean A� SD. PD20 = provocative dose of methacholine causing a 20% fall in FEVPA�tSignificantly different from responder subjects.

A two-dimensional vector analysis was then used to quantify theA�shape of the plots: short-term R-R interval variability (SD1) andA�long-term RR interval variability (SD2) of the plot were separately quantified.

Autoregessive Analysis: Harmonic components of the R-R interval were analyzed by the autoregressive method (HRVA�Analysis Software 1.1 for Windows; Biomedical Signal AnalysisA�Group, Department of Applied Physics, University of Kuopio;A�Kuopio, Finland). Autoregressive coefficients were estimatedA�using the forward-backward linear least-squares algorithm with aA�16th-order autoregressive model. The R-R interval time seriesA�were interpolated at a rate of 2 Hz and detrend prior to theA�analysis. The power density of LF and HF components wasA�calculated and expressed in absolute units (ms) and normalizedA�units (n.u.), which were obtained as follows: HF n.u. = (HFA�ms)/(LF ms + HF ms) X 100). The LF/HF ratio was alsoA�calculated to assess sympathetic/parasympathetic modulation.

Short-Time Fourier Transform: The short-time Fourier transform (STFT) of R-R intervals corresponds to a sliding fast Fourier transform analysis. The STFT processing yields anA�analysis in time-frequency domain that can be exemplified with aA�three-dimensional figure to exhibit the evolution of HRVA�throughout the observed bouts of exercise. The signal is convolved with some constant-duration time window, and the spectral components are calculated for each windowed segment.

The STFT analyses were performed using specific software after Hamming windowing (MATLAB 5.3; The MathWorks; Natick,A�MA). After loading the American Standard Code for InformationA�Interchange file, an R-R periodogram was performed and displayed in order to pick out the more relevant stretch for STFTA�analysis. This stretch needs to be > 320 values to perform aA�STFT on a block of 256 values.

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