Heart Rate Variability in the Frequency Domain
Report UPTEC F00012
The technique of estimating the autonomic activity from an
recording is explained from the point of data recording to the
calculation of the autonomic indicators. Three different
estimation techniques are discussed: General Spectral
Coarse Graining Spectral Analysis (C.G.S.A.) and recursive
auto-regressive modelling (AR). All methods estimate a
spectrum of the
heart rate variability and calculate the sympathetic (S.N.S.) and
parasympathetic (P.N.S.) indicators from the low-high
The Coarse Graining Spectral Analysis extracts the fractal
the heart rate variability by taking the Fourier transform of the
cross-correlation of the original time series and a rescaled
it. This causes the spectrum of the harmonic components to
while the fractal components, being self-similar, maintain their
spectral properties. The harmonic spectrum can then be
subtracting the fractal spectrum from an original spectrum
both harmonic and fractal components. With recursive
autoregressive (AR) modelling, it
is possible to track time varying parameters providing a much
higher esolution in time.
The methods are first tested on data simulated by the I.P.F.M.
then on data recorded in the Head Up Tilt experiment. All
an increase of the S.N.S. indicator and a decrease of the N.S.
indicator with increased tilt level. The low frequency peak
by the C.G.S.A. method but should be slightly modified for
results. If long recordings of stationary data are available,
modified version of the C.G.S.A. or AR modelling
should be used instead of the G.S.A..
For shorter time periods than 3-5 minutes the AR
modelling method is
recommended. If transient conditions are to be analysed the
recursive A.R. modelling is very promising.
- Centre for Musculoskeletal Research,
National Institute for Working Life, Umeň
- Milos Ljubisavljevic, National Institute for Working Life, Umeň,
Eugene Lyskov National Institute for Working Life, Umeň,
Signal and Systems Group, Uppsala University