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.