Key result
1D-CNN trained on synthetic data estimates respiratory modulation from RR series with ~0.86 correlation.
Why the study?
Quantifying autonomic nervous system activity from ECG data could help personalize AF treatment, but is currently not possible.
Can a 1-dimensional convolutional neural network accurately estimate respiratory modulation in AV nodal conduction during atrial fibrillation from ECG data?
Can a 1-dimensional convolutional neural network accurately estimate respiratory modulation in AV nodal conduction during atrial fibrillation from ECG data?
Effect estimate: Pearson sample correlation of ρ = 0.855
A 1D-CNN trained on synthetic data can estimate respiratory modulation in AV nodal conduction during atrial fibrillation, providing a potential non-invasive method to quantify autonomic nervous system activity from ECG data.
The quantification of autonomic nervous system (ANS) activity from ECG data may provide useful information for personalizing atrial fibrillation (AF) treatment, but is currently not possible.Since respiration is known to elicit an ANS response, the purpose of this study was to assess whether the respiratory modulation in AV nodal refractory period and conduction delay during AF can be estimated from ECG data.We trained a 1-dimensional convolutional neural network (1D-CNN) on synthetic data generated using a network model of the AV node to estimate the respiratory modulation in AV nodal conduction.The synthetic data replicates clinical ECG-derived data and contained 1-minute segments of RR series, respiration signals and atrial fibrillatory rates (AFR).Further, the synthetic data was generated using a total of 4 million unique parameter sets.We showed using synthetic data that the 1D-CNN can estimate the respiratory modulation from an RR series, respiration signal and AFR with a Pearson sample correlation of = 0.855.The results of the present study suggest that the proposed method can be used to quantify respiratory-induced variations in ANS activity from ECG data.Further studies are needed to verify the estimates and to investigate the clinical value of the estimates.
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Plappert et al. (2023) studied Atrial Fibrillation (n=28). 1-dimensional convolutional neural network (1D-CNN) vs. True simulated values was evaluated on Accuracy of respiratory modulation amplitude (aresp) estimation in simulated testing dataset (Pearson sample correlation of ρ = 0.855). A 1-dimensional convolutional neural network trained on synthetic data estimated respiratory modulation from an RR series, respiration signal, and atrial fibrillatory rate with a Pearson sample correlation of ρ = 0.855.
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