Department of Intelligent Systems, Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia.
Jožef Stefan International Postgraduate School, Jamova cesta 39, 1000 Ljubljana, Slovenia.
Sensors (Basel). 2024 Mar 25;24(7):2100. doi: 10.3390/s24072100.
Electrocardiogram (ECG) reconstruction from contact photoplethysmogram (PPG) would be transformative for cardiac monitoring. We investigated the fundamental and practical feasibility of such reconstruction by first replicating pioneering work in the field, with the aim of assessing the methods and evaluation metrics used. We then expanded existing research by investigating different cycle segmentation methods and different evaluation scenarios to robustly verify both fundamental feasibility, as well as practical potential. We found that reconstruction using the discrete cosine transform (DCT) and a linear ridge regression model shows good results when PPG and ECG cycles are semantically aligned-the ECG R peak and PPG systolic peak are aligned-before training the model. Such reconstruction can be useful from a morphological perspective, but loses important physiological information (precise R peak location) due to cycle alignment. We also found better performance when personalization was used in training, while a general model in a leave-one-subject-out evaluation performed poorly, showing that a general mapping between PPG and ECG is difficult to derive. While such reconstruction is valuable, as the ECG contains more fine-grained information about the cardiac activity as well as offers a different modality (electrical signal) compared to the PPG (optical signal), our findings show that the usefulness of such reconstruction depends on the application, with a trade-off between morphological quality of QRS complexes and precise temporal placement of the R peak. Finally, we highlight future directions that may resolve existing problems and allow for reliable and robust cross-modal physiological monitoring using just PPG.
从接触式光电容积脉搏波图（PPG）重建心电图（ECG）将对心脏监测产生变革性影响。我们通过复制该领域的开创性工作，首先研究了这种重建的基本和实际可行性，旨在评估所使用的方法和评估指标。然后，我们通过研究不同的周期分割方法和不同的评估场景来扩展现有研究，以稳健地验证基本可行性和实际潜力。我们发现，在训练模型之前，使用离散余弦变换（DCT）和线性岭回归模型对 PPG 和 ECG 周期进行语义对齐（ECG R 波峰和 PPG 收缩期峰值对齐）时，使用 DCT 和线性岭回归模型进行重建可以获得很好的结果。这种重建从形态学角度来看可能是有用的，但由于周期对齐，它会丢失重要的生理信息（精确的 R 波峰位置）。我们还发现，在训练中使用个性化时性能更好，而在受试者外留一评估中使用通用模型时性能较差，这表明很难从 PPG 到 ECG 之间建立通用映射。虽然这种重建很有价值，因为 ECG 包含了比 PPG 更多关于心脏活动的精细信息，并且提供了不同的模态（电信号），但我们的研究结果表明，这种重建的有用性取决于应用，在 QRS 复合体的形态质量和 R 波峰的精确时间位置之间存在权衡。最后，我们强调了可能解决现有问题的未来方向，并允许仅使用 PPG 进行可靠和稳健的跨模态生理监测。
