Cross-Domain Joint Dictionary Learning for ECG Inference From PPG
Cross-Domain Joint Dictionary Learning for ECG Inference From PPG
复制标题
DOI：
10.1109/jiot.2022.3231862
复制
发表时间：
2021-01
影响因子：
10.6
通讯作者：
Xin Tian;Qiang Zhu;Yuenan Li;Min Wu
中科院分区：
文献类型：
--
作者：
Xin Tian;Qiang Zhu;Yuenan Li;Min Wu
从可穿戴的医疗物联网(IoHT)设备所能测量的光体积图(PPG)中推断临床金标准心电图(ECG)的逆问题是一个日益受到关注的研究方向。它结合了PPG易于测量的特点和丰富的心电临床知识，可用于长期连续的心脏监测。使用诸如离散余弦变换(DCT)的通用基来重建的现有技术，由于缺乏代表性功率，对于不常见的心电形状的保真度有限。为了更好地利用数据和改善数据表示，我们设计了两个字典学习框架：跨域联合字典学习(XDJDL)和标签一致XDJDL(LC-XDJDL)，以进一步提高心电推理的质量，丰富基于PPG的诊断知识。该联合字典学习框架建立在K-SVD技术的基础上，通过同时优化PPG和ECG的一对信号字典，并将其稀疏编码与疾病信息联系起来，从而扩展了表达能力。建议的模型使用来自两个基准数据集的各种PPG和ECG形态进行评估，这些数据集涵盖了不同的年龄组和疾病类型。结果表明，所提出的框架比以前的方法具有更好的推理性能，使用XDJDL的平均Pearson系数为0.88，使用LC-XDJDL的平均Pearson系数为0.92，这表明基于主动学习的PPG-ECG关系的PPG用于心电筛查具有令人鼓舞的潜力。通过实现对个人健康状况的动态监测和分析，拟议的框架有助于个性化医疗保健的新兴数字双胞胎范例。
The inverse problem of inferring clinical gold-standard electrocardiogram (ECG) from photoplethysmogram (PPG) that can be measured by affordable wearable Internet of Healthcare Things (IoHT) devices is a research direction receiving growing attention. It combines the easy measurability of PPG and the rich clinical knowledge of ECG for long-term continuous cardiac monitoring. The prior art for reconstruction using a universal basis, such as discrete cosine transform (DCT), has limited fidelity for uncommon ECG shapes due to the lack of representative power. To better utilize the data and improve data representation, we design two dictionary learning frameworks, the cross-domain joint dictionary learning (XDJDL), and the label-consistent XDJDL (LC-XDJDL), to further improve the ECG inference quality and enrich the PPG-based diagnosis knowledge. Building on the K-SVD technique, the proposed joint dictionary learning frameworks extend the expressive power by optimizing simultaneously a pair of signal dictionaries for PPG and ECG with the transforms to relate their sparse codes and disease information. The proposed models are evaluated with a variety of PPG and ECG morphologies from two benchmark datasets that cover various age groups and disease types. The results show the proposed frameworks achieve better inference performance than previous methods with average Pearson coefficients being 0.88 using XDJDL and 0.92 using LC-XDJDL, suggesting an encouraging potential for ECG screening using PPG based on the proactively learned PPG-ECG relationship. By enabling the dynamic monitoring and analysis of the health status of an individual, the proposed frameworks contribute to the emerging digital twins paradigm for personalized healthcare.