Objective. To compare the results on short-term heart rate variability, analyzed in tlie frequency domain by use of autoregressive modeling (ARM) and by fast Fourier transform (FFT).

Methods. RR interval and respiratory activity were recorded in a population-based sample of 614 subjects, during 15 minutes in the supine position and 15 minutes in the free Standing position, under standardized laboratory conditions. The low - (LF) and high -frequency (HF) components of heart rate variability were identified by power spectral analysis, by use of FFT, witli application of two sets of frequency ranges, and by ARM according to a newly developed algoritlun; LF and HF power were expressed in both normalized (%) and absolute units (ms²).

Results. The RR interval, its variance and the HF power decreased from the supine to the Standing position (P < 0.001).The LF power increased on standing when expressed in normalized units, but decreased in absolute units, whereas the LF-to-HF ratio increased (P < 0.001). On the low side of the spectrmn, FFT slightly overestimated the LF component obtained with ARM, when the predefmed frequency range was 0.05 - 0.15 Hz (P < 0.001); the underestimation of LF in the frequency range 0.07 - 0.14 Hz was more pronounced, particularly in tiie erect position (P < 0.001). Both FFT methods overestimated (P < 0.001) the ARM HF component, more so for the 0.15-0.50 Hz range than for the 0.14 - 0.35 Hz range. Finally, we observed considerable within-subject differences between methods, which were estimated by calculation of the limits of agreement.

Conclusions. Different methods for spectral decomposition of short-term heart rate variability yield similar qualitative results, but the quantitative results differ between ARM and FFT, and within the FFT method according to the selected frequency range.