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Information about autonomic nervous system (ANS) activity may be valuable forpersonalized atrial fibrillation (AF) treatment but is not easily accessiblefrom the ECG. In this study, we propose a new approach for ECG-based assessmentof respiratory modulation in AV nodal refractory period and conduction delay. A1-dimensional convolutional neural network (1D-CNN) was trained to estimaterespiratory modulation of AV nodal conduction properties from 1-minute segmentsof RR series, respiration signals, and atrial fibrillatory rates (AFR) usingsynthetic data that replicates clinical ECG-derived data. The synthetic datawere generated using a network model of the AV node and 4 million unique modelparameter sets. The 1D-CNN was then used to analyze respiratory modulation inclinical deep breathing test data of 28 patients in AF, where a ECG-derivedrespiration signal was extracted using a novel approach based on periodiccomponent analysis. We demonstrated using synthetic data that the 1D-CNN canpredict the respiratory modulation from RR series alone ($\rho$ = 0.805) andthat the addition of either respiration signal ($\rho$ = 0.830), AFR ($\rho$ =0.837), or both ($\rho$ = 0.855) improves the prediction. Results from analysisof clinical ECG data of 20 patients with sufficient signal quality suggest thatrespiratory modulation decreased in response to deep breathing for fivepatients, increased for five patients, and remained similar for ten patients,indicating a large inter-patient variability.

ECG-based estimation of respiratory modulation of AV nodal conduction during atrial fibrillation