ECG-based estimation of respiration-induced autonomic modulation of AV nodal conduction during atrial fibrillation

Front Physiol. 2024 May 8:15:1281343. doi: 10.3389/fphys.2024.1281343. eCollection 2024.

Abstract

Introduction: Information about autonomic nervous system (ANS) activity may offer insights about atrial fibrillation (AF) progression and support personalized 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 atrioventricular (AV) nodal refractory period and conduction delay. Methods: 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 an ECG-derived respiration signal was extracted using a novel approach based on periodic component analysis. Results: We demonstrated using synthetic data that the 1D-CNN can estimate the respiratory modulation from RR series alone with a Pearson sample correlation of r = 0.805 and that the addition of either respiration signal (r = 0.830), AFR (r = 0.837), or both (r = 0.855) improves the estimation. Discussion: Initial results from analysis of ECG data suggest that our proposed estimate of respiration-induced autonomic modulation, a resp, is reproducible and sufficiently sensitive to monitor changes and detect individual differences. However, further studies are needed to verify the reproducibility, sensitivity, and clinical significance of a resp.

Keywords: ECG; atrial fibrillation; atrioventricular node; autonomic nervous system dysfunction; convolutional neural network; deep breathing test; network model; respiration-induced autonomic modulation.

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. The research was supported by the Swedish Research Council (grant VR 2019–04272), the Crafoord Foundation (grant 20200605), and the Swedish Heart-Lung foundation (no 2020-0173). The computations were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) and the Swedish National Infrastructure for Computing (SNIC) at Lund University partially funded by the Swedish Research Council through grant agreements no. 2022-06725 and no. 2018-05973. The Swedish Heart and Lung foundation was the main funding body of the SCAPIS cohort. SCAPIS was also supported by grants from the Knut and Alice Wallenberg Foundation, the Swedish Research Council, and Sweden’s Innovation agency.