  With recent advancements in deep learning methods, automatically learning
deep features from the original data is becoming an effective and widespread
approach. However, the hand-crafted expert knowledge-based features are still
insightful. These expert-curated features can increase the model's
generalization and remind the model of some data characteristics, such as the
time interval between two patterns. It is particularly advantageous in tasks
with the clinically-relevant data, where the data are usually limited and
complex. To keep both implicit deep features and expert-curated explicit
features together, an effective fusion strategy is becoming indispensable. In
this work, we focus on a specific clinical application, i.e., sleep apnea
detection. In this context, we propose a contrastive learning-based cross
attention framework for sleep apnea detection (named ConCAD). The cross
attention mechanism can fuse the deep and expert features by automatically
assigning attention weights based on their importance. Contrastive learning can
learn better representations by keeping the instances of each class closer and
pushing away instances from different classes in the embedding space
concurrently. Furthermore, a new hybrid loss is designed to simultaneously
conduct contrastive learning and classification by integrating a supervised
contrastive loss with a cross-entropy loss. Our proposed framework can be
easily integrated into standard deep learning models to utilize expert
knowledge and contrastive learning to boost performance. As demonstrated on two
public ECG dataset with sleep apnea annotation, ConCAD significantly improves
the detection performance and outperforms state-of-art benchmark methods.

Machine Learning