  Atrial fibrillation (AF), a common cardiac arrhythmia, significantly
increases the risk of stroke, heart disease, and mortality.
Photoplethysmography (PPG) offers a promising solution for continuous AF
monitoring, due to its cost efficiency and integration into wearable devices.
Nonetheless, PPG signals are susceptible to corruption from motion artifacts
and other factors often encountered in ambulatory settings. Conventional
approaches typically discard corrupted segments or attempt to reconstruct
original signals, allowing for the use of standard machine learning techniques.
However, this reduces dataset size and introduces biases, compromising
prediction accuracy and the effectiveness of continuous monitoring. We propose
a novel deep learning model, Signal Quality Weighted Fusion of Attentional
Convolution and Recurrent Neural Network (SQUWA), designed to learn how to
retain accurate predictions from partially corrupted PPG. Specifically, SQUWA
innovatively integrates an attention mechanism that directly considers signal
quality during the learning process, dynamically adjusting the weights of time
series segments based on their quality. This approach enhances the influence of
higher-quality segments while reducing that of lower-quality ones, effectively
utilizing partially corrupted segments. This approach represents a departure
from the conventional methods that exclude such segments, enabling the
utilization of a broader range of data, which has great implications for less
disruption when monitoring of AF risks and more accurate estimation of AF
burdens. Our extensive experiments show that SQUWA outperform existing
PPG-based models, achieving the highest AUCPR of 0.89 with label noise
mitigation. This also exceeds the 0.86 AUCPR of models trained with using both
electrocardiogram (ECG) and PPG data.
