The Heart as a Computer .
Andre Willers
18 Oct 2013
This is not medical advice .
Synopsis :
The Heart organ can be described as a computer roughly
equivalent to an old XT (~16kb/sec)
Discussion :
1.See Appendix A.
Information in HRV per heartbeat 1 hz = ((0.15-.07)*1)^-1 =
156 bits
In double “lub-dub” , information = 156^2 = 16384 bits hidden in frequency
interference.
The heart programs a lot of bodily responses .
2.Damage to half of the heart :
Immediate loss of half of system responsiveness .
3.How to compensate :
Programmed Percussion .
If the medical systems did their job , there would be an
exact pattern of percussions to mimic a healthy heart .
Since they didn’t , try heavy rock with big speakers that
cause visceral effects .
4.The body can adapt easier to a strong signal than to a
weak one .
5. Beta blockers are immaterial . At least one heavy
percussion rhythm is needed for the system to retrain using at least periods
between beats in interference mode .
6.You will live longer if you listen to a loud orchestra .
Oompah-Oompah
Andre
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Appendix A
After analysing the variability of the RR intervals, a
number of studies [11] [12]
[19] [22] [25] [26] [29] [30] [32] [35] have shown that an
increase in mental load
causes a decrease in the so-called mid-frequency (MF,
0.07-0.15 Hz) power band
of the Heart Period Variability (HPV) power spectrum.
Focusing on this
frequency band filters other peaks of the power spectrum:
the typical peak in the
0.15-0.45 Hz band corresponds to the respiratory rate
(called respiratory sinus
arrhythmia); the peak in the 0.04-0.07 Hz band is in
connection with the
thermoregulatory fluctuations of the blood vessels [12]
[19]. Heart rate
fluctuations in the MF (0.07-0.15 Hz) power band may also
reflect postural
changes (via the blood pressure control of the so called
baroreflex). To separate
the effect of the mental load from the effect of postural
changes, a ratio of the MF
component around 0.1 Hz and the higher frequency respiratory
component can be
applied [30]. However, it is emphasised that if the
participants work continuously
in a sitting posture (e.g., during computer usage), and
their larger muscle
movements (e.g., stretching, laughing, sneezing, talking,
etc.) eventually are
filtered from the records (e.g., via video analysis), the MF
(0.07-0.15 Hz) power
band itself can characterize the mental effort sensitively
enough, as is shown by
the following results presented in this paper.
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