Respiratory spatial patterns describe the distribution and dynamics of lung conditions, and monitoring of their asymmetry or irregularities enables a more comprehensive assessment of respiratory functions. The feasibility of using the camera pixel array sensing combined with machine learning for analyzing respiratory spatial patterns was demonstrated, however, this approach faces challenges in patient generalization due to limited clinical data and individual respiratory variability. Data augmentation methods may address this by synthesizing new data, but they have a risk of destroying the symmetry or regular semantic information of respiratory patterns. To address this, we propose a prototype-driven hard-sample contrastive learning (PHCL) method tailored for camera-based respiratory imaging analysis. It first separates the samples into simple and hard-to-learn samples using prototypes and Gini-index distance measurement. Then it synthesizes a new feature by blending one-class simple samples and other-class hard samples to construct a transition boundary between different classes, so as to broaden the feature distribution. Then it employs contrastive learning to emphasize feature consistency between prototypes and hard same-class samples from different subjects to mitigate individual respiratory variability and refine class boundaries. Extensive experiments were conducted in the neonatal intensive care unit and the thoracic surgery department, where PHCL outperforms image augmentation and advanced feature augmentation methods by 1-10% in both accuracy and F1-score. Our work provides valuable insights into the analysis of asymmetric and irregular respiratory activities.
Huang et al. (Mon,) studied this question.