Domain randomization using synthetic electrocardiograms for training neural networks

Kaisti, Matti; Laitala, Juho; Wong, David C.; Airola, Antti

doi:10.1016/j.artmed.2023.102583

Cited by 1 publication

(1 citation statement)

References 15 publications

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“…For example, synthetic images were generated to train neural networks to track cardiac motion and calculate cardiac strain ( Loecher et al, 2021 ), estimate tensors from free-breathing cardiac diffusion tensor imaging ( Weine et al, 2022 ), and predict end-diastole volume, end-systole volume, and ejection fraction ( Gheorghita et al, 2022 ). Furthermore, synthetic photoplethysmography (PPG) signals were generated to detect bradycardia and tachycardia ( Sološenko et al, 2022 ), and synthetic electrocardiogram (ECG) signals were generated to detect r-waves during different physical activities and atrial fibrillation ( Kaisti et al, 2023 ), and to predict the ventricular origin in outflow tract ventricular arrhythmias ( Doste et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

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

Plappert,

Engström,

Platonov

et al. 2024

Front. Physiol.

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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, aresp, 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 aresp.

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