HFAM model
The HFAM model was demonstrated to provide reliable assessment of the
autonomic balance of the heart rate control in beagle dogs, cynomolgus
monkeys and humans over short 10-second recording sequences (Champéroux
et al., 2018). This model is based on the principle that HF oscillations
are not stationary and is applicable to ambulatory and freely moving
conditions. In the opposite, spectral analysis in the frequency domain
requires stationary rhythms collected at rest in controlled breathing
conditions (Task force, 1996).
HFHR and HFRR oscillations were normalized (N) as follows:
HFHRN=HFHR/HFHRref in normalized units
HFRRN=HFRR/HFRRref in normalized units
HFHRref and HFRRref values are mean
values of HFHR and HFRR oscillations calculated over the entire
circadian period. For the HFAM analysis in humans,
HFHRref and HFRRref values were
determined from population values (HFHRref=10 bpm and
HFRRref=110 ms). In dogs, these reference values were
calculated individually for each animal during a free-treatment 24-hour
recording session preceding each experiment.
The HFAM ratio was calculated from the following ratio:
HFAM=HFHRN/HFRRN.
The HFAM model allows differentiation of three states of the autonomic
control named S1, S2 and S3. Discrete algorithms were designed to
automatically identify each state within each 10-second recording
sequence:
S1: HFAM≤1.
S2: HFAM>1 and HFHRN>1.
S3: HFAM>1 and HFHRN<1.
These states were demonstrated to identify 10-second sequences with
parasympathetic predominance (S1), parasympathetic and sympathetic
coactivation (S2) and parasympathetic withdrawal and/or sympathetic
activation (S3). Proportions of each state were calculated in % per
hour.