用新方法从脉搏波信号重建心电图，实现无创心脏监测
CLEP-GAN: An Innovative Approach to Subject-Independent ECG Reconstruction from PPG Signals
- 基于微分方程生成合成数据提升训练多样性
- 融合对比学习与注意力门控，实现跨个体高精度重建
- 揭示性别年龄影响重建效果，强调数据多样性重要性
本研究针对从脉搏波（PPG）信号重建未见个体心电图（ECG）的挑战，致力于实现无创心脏监测。尽管已有多个公开的ECG-PPG数据集，但其多样性远不及图像数据集，且采集过程常引入噪声，使先进机器学习模型也难以准确重建ECG。为此，我们首先提出一种基于微分方程（ODE）的新型合成ECG-PPG数据生成技术，以增强训练多样性。随后，开发了一种新型跨个体PPG-to-ECG重建模型，融合对比学习、对抗学习与注意力门控机制，在未见个体上达到或超越现有方法性能。最后，分析了性别和年龄对重建精度的影响，强调在模型训练与数据增强中考虑人口统计学多样性的必要性。
原文摘要 · Abstract (English)
This study addresses the challenge of reconstructing unseen ECG signals from PPG signals, a critical task for non-invasive cardiac monitoring. While numerous public ECG-PPG datasets are available, they lack the diversity seen in image datasets, and data collection processes often introduce noise, complicating ECG reconstruction from PPG even with advanced machine learning models. To tackle these challenges, we first introduce a novel synthetic ECG-PPG data generation technique using an ODE model to enhance training diversity. Next, we develop a novel subject-independent PPG-to-ECG reconstruction model that integrates contrastive learning, adversarial learning, and attention gating, achieving results comparable to or even surpassing existing approaches for unseen ECG reconstruction. Finally, we examine factors such as sex and age that impact reconstruction accuracy, emphasizing the importance of considering demographic diversity during model training and dataset augmentation.
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