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.

翻译：房颤（AF）是最常见的心律失常类型，与中风、心力衰竭及其他心血管并发症风险增加相关，但临床可能呈现无症状表现。利用可穿戴设备进行被动式房颤监测有助于减少与房颤相关的不良临床结局。在噪声干扰严重的可穿戴数据中检测房颤是一项重大挑战，由此催生了多种深度学习技术。现有深度学习模型通常基于心电信号（ECG）或光电容积脉搏波（PPG）单一模态进行学习。然而，此类模型难以学习具有泛化能力的特征，且易受噪声干扰导致特征退化，在低质量信号场景下表现欠佳。鉴于可穿戴设备及床旁监护仪中ECG与PPG信号对的日益普及，我们提出一种名为SiamAF的新方法，通过构建新型孪生网络架构与联合学习损失函数，从ECG和PPG信号中提取共享信息。在推理阶段，该模型可通过PPG或ECG任一模态进行房颤预测，并在三个外部测试集上优于基线方法。由于我们创新的架构设计，模型能够学习具有医学相关性的特征。此外，本模型在显著减少训练标签需求的同时达到与传统学习策略相当的性能，为未来降低对人工标注的依赖提供了潜在解决方案。