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Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. It isassociated with an increased risk of stroke, heart failure, and othercardiovascular complications, but can be clinically silent. Passive AFmonitoring with wearables may help reduce adverse clinical outcomes related toAF. Detecting AF in noisy wearable data poses a significant challenge, leadingto the emergence of various deep learning techniques. Previous deep learningmodels learn from a single modality, either electrocardiogram (ECG) orphotoplethysmography (PPG) signals. However, deep learning models oftenstruggle to learn generalizable features and rely on features that are moresusceptible to corruption from noise, leading to sub-optimal performances incertain scenarios, especially with low-quality signals. Given the increasingavailability of ECG and PPG signal pairs from wearables and bedside monitors,we propose a new approach, SiamAF, leveraging a novel Siamese networkarchitecture and joint learning loss function to learn shared information fromboth ECG and PPG signals. At inference time, the proposed model is able topredict AF from either PPG or ECG and outperforms baseline methods on threeexternal test sets. It learns medically relevant features as a result of ournovel architecture design. The proposed model also achieves comparableperformance to traditional learning regimes while requiring much fewer traininglabels, providing a potential approach to reduce future reliance on manuallabeling.

SiamAF: Learning Shared Information from ECG and PPG Signals for Robust Atrial Fibrillation Detection