Key result
A 50-layer convolutional neural network accurately detected atrial fibrillation episodes from continuous ambulatory photoplethysmography signals, achieving a test AUC of 94.8% and demonstrating robustness to motion artifacts.
Why the study?
Does a deep learning model applied to wearable PPG signals improve the detection of atrial fibrillation compared to a feature-based baseline in ambulatory free-living conditions?
Population
81 free-living subjects providing over 4000 hours of continuous wrist-worn PPG recordings.
Comparison
50-layer convolutional neural network for Atrial… vs Feature-based baseline algorithm using…
Authors
Loading...
Enables potential continuous AF monitoring via wearables; leaves open prospective clinical validation.
Observational (n=81)
Does a deep learning model applied to wearable PPG signals improve the detection of atrial fibrillation compared to a feature-based baseline in ambulatory free-living conditions?
Effect estimate: AUC 94.8%
A deep learning algorithm applied to continuous wrist-worn PPG signals can accurately detect atrial fibrillation in ambulatory settings with high robustness to motion artifacts.
Voisin et al. (2018) conducted an observational in Atrial Fibrillation (n=81). Deep learning algorithm (50-layer CNN) for PPG vs. Feature-based baseline algorithm (IBI RMSSD) was evaluated on Detection of Atrial Fibrillation (AUC on test set) (AUC 94.8%). A 50-layer convolutional neural network accurately detected atrial fibrillation episodes from continuous ambulatory photoplethysmography signals, achieving a test AUC of 94.8% and demonstrating robustness to motion artifacts.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: