  Atrial fibrillation (AF) is the most common arrhythmia, associated with
significant burdens to patients and the healthcare system. The atrioventricular
(AV) node plays a vital role in regulating heart rate during AF, but is often
insufficient in regards to maintaining a healthy heart rate. Thus, the AV node
properties are modified using rate-control drugs. Hence, quantifying individual
differences in diurnal and short-term variability of AV-nodal function could
aid in personalized treatment selection.
  This study presents a novel methodology for estimating the refractory period
(RP) and conduction delay (CD) trends and their uncertainty in the two pathways
of the AV node during 24 hours using non-invasive data. This was achieved using
a network model together with a problem-specific genetic algorithm and an
approximate Bayesian computation algorithm. Diurnal and short-term variability
in the estimated RP and CD was quantified by the difference between the daytime
and nighttime estimates and by the Kolmogorov-Smirnov distance between adjacent
10-minute segments in the 24-hour trends.
  Holter ECGs from 51 patients with permanent AF during baseline were analyzed,
and the predictive power of variations in RP and CD on the resulting heart rate
reduction after treatment with four rate control drugs was investigated.
Diurnal variability yielded no correlation to treatment outcome, and no
prediction of drug outcome was possible using the machine learning tools.
However, a correlation between the short-term variability for the RP and CD in
the fast pathway and resulting heart rate reduction during treatment with
metoprolol ($\rho=0.48, p<0.005$ in RP, $\rho=0.35, p<0.05$ in CD) were found.
  The proposed methodology enables non-invasive estimation of the AV node
properties during 24 hours, which may have the potential to assist in treatment
selection.
