Many physiological signals appear fractal, in having self-similarity over a
large range of their power spectral densities. They are analogous to one o
f two classes of discretely sampled pure fractal time signals, fractional G
aussian noise (fGn) or fractional Brownian motion (fBm). The fGn series are
the successive differences between elements of a fBm series; they are stat
ionary and are completely characterized by two parameters, sigma(2), the va
riance, and H, the Hurst coefficient. Such efficient characterization of ph
ysiological signals Is valuable since Il defines the autocorrelation and th
e fractal dimension of the time series. Estimation of H from Fourier analys
is is inaccurate, so more robust methods are needed. Dispersional analysis
(Disp) is good for noise signals while bridge detrended scaled windowed var
iance analysis (bdSWV) is good for motion signals. Signals whose slopes of
their power spectral densities lie near the border between fGn and fBm are
difficult to classify. A new method using signal summation conversion (SSC)
, wherein an fGn is converted to an fBm or an fBm to a summed fBm and bdSWV
then applied, greatly improves the classification and the reliability of (
H) over cap, the estimates of H, for the times series. Applying these metho
ds to laser-Doppler blood cell perfusion signals obtained from the brain co
rtex of anesthetized rats gave A of; 0.24+/-0.02 (SD, n=8) and defined the
signal as a fractional Brownian motion. The implication is that the flow si
gnal is the summation (motion) of a set of local velocities from neighborin
g vessels that are negatively correlated, as if induced by local resistance
fluctuations.