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In-Depth Information
Interbreath intervals
0.8
regular
0.6
0.4
random
0.2
0
0.8
1
1.2
Log average
1.4
1.6
Figure 2.23.
A typical fit to the aggregated variance versus the aggregated mean for BRV time series obtained
by West
et al
.[
99
]. The points are calculated from the data and the solid curve is the best
least-square fit to the data with slope = 0.86. It is evident that the allometric relation (
2.82
) does
indeed fit the BRV data extremely well and lies within the regular and random extremes [
100
].
Reproduced with permission.
BRV has led to a revolutionary way of utilizing mechanical ventilators, which histor-
ically facilitate breathing after an operation and have a built-in periodicity. Mutch
et al
.
[
52
] redesigned the ventilator using an inverse power-law spectrum to vary the ventila-
tor's motion in time. They demonstrated that the variable ventilator-supported breathing
produces an increase in arterial oxygenation over that produced by conventional
control-mode ventilators. This comparison indicates that the fractal variability in breath-
ing is not the result of happenstance, but is an important property of respiration. A reduc-
tion in variability of breathing reduces the overall efficiency of the respiratory network.
Altemeier
et al
.[
2
] measured the fractal characteristics of ventilation and determined
not only that local ventilation and perfusion are highly correlated, but also that they
scale. Finally, Peng
et al
.[
59
] analyzed the BRV time series for forty healthy adults
and found that, under supine, resting and spontaneous breathing conditions, the time
series scale. This result implies that human BRV time series have “long-range (fractal)
correlations across multiple time scales.”
2.4.3
Fractal steps
Humans must learn how to walk and, if other creatures had to consciously do so as well,
the result might be that given by the anonymously written poem:
