.png){kind=icon}
返回
Robust optimization for PPG-based blood pressure estimation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.bspc.2025.107585.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Robust optimization
Worst-group optimization
Blood pressure estimation
PPG signal
期刊
IF:
4.9
论文数:
9.9K
被引数:
2.4W
机构
引用论文
MLP-BP: A novel framework for cuffless blood pressure measurement with PPG and ECG signals based on MLP-Mixer neural networksMlp-bp: 基于mlp-mixer神经网络的PPG和ECG信号无袖带血压测量的新框架
PREAI
分享收藏Hybrid modeling on reconstitution of continuous arterial blood pressure using finger photoplethysmography手指光电容积描记法重建连续动脉血压的混合建模
PREAI
分享收藏分享收藏Blood pressure estimation from appropriate and inappropriate PPG signals using A whole-based method
PREAI
分享收藏Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning使用深度学习评估PPG和rPPG信号的无创血压预测.png)
SENSORS
OAAI
SENSORSIF3.5
OAAI分享收藏
查看更多内容