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Electrical activity of the heart 121

(tonometer readout) onto the input signal (aortic pressure). Then an inverse of the TF
may be used to deconvolve the readout and recover to input signal. Of course, noise
filtering is provided in this process. The recommended linear ITF of “order 10”
(10 previous successive readouts and 10 previous successive inputs), at 75 bpm (labeled
AI@75) is defined as (Chen et al., 1997):
TtðÞ 52 a 1 Tt 2 1Þ 2 a 1 Tt 2 2Þ 2 2 a 1 Tt 2 mÞ 1 b 1 Tt 2 1Þ 1 b 1 Tt 2 2Þ 1 1 b 1 Tt 2 nÞ;
ð
ð
ð
ð
ð
ð
ð4:22Þ
where T(t) is the present readout, and T(t i), and P(t i), i 5 {1,2, .. . ,m} are previ-
ous, known, outputs (tonometer readouts) and inputs (aortic pressure), “a” and “b” are
the parameters, and m and n are the model order (here, 10). ITF is then convolved
with a low-pass filter with a cut-off frequency such that the ITF gain function
decreases below 1. Its inverse yields the aortic pressure
PtðÞ 52 b 2 =b 1 Pt 2 1Þ 2 2 b n =b 1 Pt 2 nÞ 1 a 1 =b 1 Tt 2 1Þ 1 1 a m =b 1 Tt 2 mÞ:
ð
ð
ð
ð
ð4:23Þ
A GFT may be obtained by averaging the ITF from a population of participating
patients (Chen et al., 1997). It has been asserted (Chen et al., 1997) that GTF is statisti-
cally more stable than other methods and yields dependable spectral estimates from lim-
ited data compared with nonparametric (Fourier transform) approaches (Karamanoglu
et al., 1993; Sharman et al., 2006). The variance of the AI- and Fourier-derived spectra
are similar only when larger data sets are used.
AI is an important indicator as it was associated with essential physiological para-
meters, either through univariate expressions, for example, SBP (nonlinear positive
association), DPB, age (nonlinear positive association), pulse pressure (PP), central sys-
tolic blood pressure (cSBP), but it was negatively related with others such as sex, body
mass index, BMI, and physical activity level, PAL (Sievi et al., 2015).

Using small size data collections to process the arterial flow evaluation
Small size samples (B20 subjects), targeting population of healthy subject without
diagnosed cardiovascular diseases and nonprobability sampling technique, such as the
convenience sampling that is prone to sampling bias, may though unveil the relations that
exist between vital indicators recordable through AAT and related physiological
signals: SBP (SYS), DBP (DIA), PP, central systolic blood pressure (cSBP), AI, and the
pressure pulse values (PULSE) (Baran and Savastru, 2017), obtainable using devices
such as OMRON 9000 AI (Fig. 4.21).
The characteristics of the population that are of interest here are three common
anthropometric variables: the age (AGE), the height (H) and the weight (G) of the
units, and normality is checked using graphic-analytical methods, based on the