  Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. It is
associated with an increased risk of stroke, heart failure, and other
cardiovascular complications, but can be clinically silent. Passive AF
monitoring with wearables may help reduce adverse clinical outcomes related to
AF. Detecting AF in noisy wearable data poses a significant challenge, leading
to the emergence of various deep learning techniques. Previous deep learning
models learn from a single modality, either electrocardiogram (ECG) or
photoplethysmography (PPG) signals. However, deep learning models often
struggle to learn generalizable features and rely on features that are more
susceptible to corruption from noise, leading to sub-optimal performances in
certain scenarios, especially with low-quality signals. Given the increasing
availability of ECG and PPG signal pairs from wearables and bedside monitors,
we propose a new approach, SiamAF, leveraging a novel Siamese network
architecture and joint learning loss function to learn shared information from
both ECG and PPG signals. At inference time, the proposed model is able to
predict AF from either PPG or ECG and outperforms baseline methods on three
external test sets. It learns medically relevant features as a result of our
novel architecture design. The proposed model also achieves comparable
performance to traditional learning regimes while requiring much fewer training
labels, providing a potential approach to reduce future reliance on manual
labeling.
