Robust optimization for PPG-based blood pressure estimation

Machine learning-based estimation of blood pressure (BP) using photoplethysmography (PPG) signals has gained significant attention for its non-invasive nature and potential for continuous monitoring. However, challenges remain in real-world applications, where performance can vary widely across different BP groups, especially among high-risk groups. This study is the first to propose a PPG-based BP estimation approach that specifically accounts for BP group disparities, aiming to improve robustness for high-risk BP groups.We present a comprehensive approach from the perspectives of data, model, and loss to enhance overall accuracy and reduce performance degradation for specific groups, referred to as “worst groups.” At the data level, we introduce in-group augmentation using Time-Cutmix to mitigate group imbalance severity. From a model perspective, we adopt a hybrid structure of convolutional and Transformer layers to integrate local and global information, improving average model performance. Additionally, we propose robust optimization techniques that consider data quantity and label distributions within each group. These methods effectively minimize performance loss for high-risk groups without compromising average and worst-group performance. Experimental results demonstrate the effectiveness of our methods in developing a robust BP estimation model tailored to handle group-based performance disparities.