提出融合ECG与PPG信号的新方法，提升穿戴设备在信号干扰下的心率监测稳定性。
CardioFusion-AI: Robust ECG--PPG Fusion for Multimodal Physiological Monitoring Under Signal Degradation
- 通过多模态信号质量评估，动态调整不同传感器权重。
- 在信号完全丢失时仍保持误差低于1.66 bpm，优于单模态表现。
- 适用于重症监护或胎儿监测等对可靠性要求高的场景。
可穿戴式心电图（ECG）与光电容积脉搏波（PPG）传感器互补但易受运动伪影、接触不良和传感器断连影响。传统融合方法假设双模态均可靠，在信号退化时反而不如单一干净模态。本文提出CardioFusion-AI框架，其前端包括R峰与收缩峰检测、Orphanidou型信号质量指数及逐搏脉搏传导时间估计，已在53例重症监护真实数据（848个窗口；ECG心率均方误差1.61 bpm，PPG为2.78 bpm）和真实标注的胎儿ECG数据库（R峰F1值0.89-0.98）上验证。进一步在合成退化实验中对比八种融合策略，覆盖六种退化场景（从部分损坏到模态完全缺失），使用五组独立训练种子。注意力融合整体误差最低（1.66±0.43 bpm）。自适应门控在模态全失时能将权重转向健康模态，但在渐进退化下门控权重与信号质量相关性极低（相关系数r=0.10-0.24）。基于信号质量条件化的融合在缺PPG时表现更优（1.56±0.59 bpm），接近单模态上限1.48 bpm。因仅五组训练种子，无显著性差异检验通过；故报告效应量与置信区间。结果表明，模态可用性与模态质量是自适应融合中功能上不同的问题。
原文摘要 · Abstract (English)
Wearable electrocardiogram (ECG) and photoplethysmogram (PPG) sensors are complementary but individually fragile: motion artifact, poor contact, and sensor dropout can degrade one or both signals. Fusion strategies that assume both modalities are equally trustworthy can become less reliable than a single clean modality under degradation. We present CardioFusion-AI, a framework whose signal-processing front end, including R-peak and systolic-peak detection, an Orphanidou-type signal-quality index, and beat-by-beat pulse transit time estimation, is validated on 53 real intensive-care recordings (848 windows; heart-rate mean absolute error 1.61 bpm for ECG and 2.78 bpm for PPG) and a real annotated fetal ECG database (R-peak F1 0.89-0.98). We then conduct a controlled synthetic degradation study comparing eight ECG-PPG fusion strategies across six degradation regimes spanning graded corruption and complete modality loss, using five independent training seeds. Attention fusion achieved the lowest descriptive overall error (1.66+/-0.43 bpm). Both adaptive gates reallocated weight toward the healthy modality under complete modality loss, but showed near-zero correlation between gate weight and signal quality under graded degradation (r = 0.10-0.24). Signal-quality conditioning produced a specific improvement under missing-PPG conditions (1.56+/-0.59 bpm), approaching the 1.48 bpm unimodal ceiling. With only five training seeds, no pairwise comparison survives Holm-corrected significance testing; effect sizes and confidence intervals are therefore reported. These results indicate that modality availability and modality quality are functionally distinct problems for adaptive fusion.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。