ECG-based estimation of respiratory modulation of AV nodal conduction during atrial fibrillation
arxiv(2023)
摘要
Information about autonomic nervous system (ANS) activity may be valuable for
personalized atrial fibrillation (AF) treatment but is not easily accessible
from the ECG. In this study, we propose a new approach for ECG-based assessment
of respiratory modulation in AV nodal refractory period and conduction delay. A
1-dimensional convolutional neural network (1D-CNN) was trained to estimate
respiratory modulation of AV nodal conduction properties from 1-minute segments
of RR series, respiration signals, and atrial fibrillatory rates (AFR) using
synthetic data that replicates clinical ECG-derived data. The synthetic data
were generated using a network model of the AV node and 4 million unique model
parameter sets. The 1D-CNN was then used to analyze respiratory modulation in
clinical deep breathing test data of 28 patients in AF, where a ECG-derived
respiration signal was extracted using a novel approach based on periodic
component analysis. We demonstrated using synthetic data that the 1D-CNN can
predict the respiratory modulation from RR series alone (ρ = 0.805) and
that the addition of either respiration signal (ρ = 0.830), AFR (ρ =
0.837), or both (ρ = 0.855) improves the prediction. Results from analysis
of clinical ECG data of 20 patients with sufficient signal quality suggest that
respiratory modulation decreased in response to deep breathing for five
patients, increased for five patients, and remained similar for ten patients,
indicating a large inter-patient variability.
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