论文标题
表演者：用于心血管疾病检测的数字生物标志物的新型PPG至ECG重建变压器
Performer: A Novel PPG-to-ECG Reconstruction Transformer for a Digital Biomarker of Cardiovascular Disease Detection
论文作者
论文摘要
心电图（ECG）是一种捕获心脏活动的电测量，是诊断心血管疾病（CVD）的金标准。但是，由于ECG需要用户参与，因此无法进行连续的心脏监测。相比之下，Photoplethysmography（PPG）提供了易于收集的数据，但其精度有限限制了其临床用法。为了结合两个信号的优势，最近的研究结合了各种深度学习技术，以重建PPG信号为ECG；但是，缺乏上下文信息以及denoise生物医学信号的有限能力最终会限制模型性能。在这项研究中，我们提出了一种基于变压器的新型体系结构，可从PPG重建ECG，并将PPG和重建的ECG作为CVD检测的多种模态。该方法是第一次在生物医学波形重建上进行了变压器序列到序列翻译，结合了PPG和ECG的优势。我们还创建了基于斑块的注意（SPA），这是一种编码/解释生物医学波形的有效方法。通过获取各种序列长度并捕获交叉点连接，SPA最大化本地特征和全局上下文表示的信号处理。所提出的体系结构在BIDMC数据库中重建PPG为ECG的重建为0.29 RMSE的最先进性能，超过了先前的研究。我们还在模拟物III数据集上评估了该模型，在CVD检测中达到了95.9％的精度，并在PPG-BP数据集中评估了该模型，在相关的CVD糖尿病检测中达到了75.9％的精度，表明其普遍性。作为概念证明，一个名为Pearl（原型）的耳环可穿戴式可穿戴，旨在扩大护理点（POC）医疗保健系统。
Electrocardiography (ECG), an electrical measurement which captures cardiac activities, is the gold standard for diagnosing cardiovascular disease (CVD). However, ECG is infeasible for continuous cardiac monitoring due to its requirement for user participation. By contrast, photoplethysmography (PPG) provides easy-to-collect data, but its limited accuracy constrains its clinical usage. To combine the advantages of both signals, recent studies incorporate various deep learning techniques for the reconstruction of PPG signals to ECG; however, the lack of contextual information as well as the limited abilities to denoise biomedical signals ultimately constrain model performance. In this research, we propose Performer, a novel Transformer-based architecture that reconstructs ECG from PPG and combines the PPG and reconstructed ECG as multiple modalities for CVD detection. This method is the first time that Transformer sequence-to-sequence translation has been performed on biomedical waveform reconstruction, combining the advantages of both PPG and ECG. We also create Shifted Patch-based Attention (SPA), an effective method to encode/decode the biomedical waveforms. Through fetching the various sequence lengths and capturing cross-patch connections, SPA maximizes the signal processing for both local features and global contextual representations. The proposed architecture generates a state-of-the-art performance of 0.29 RMSE for the reconstruction of PPG to ECG on the BIDMC database, surpassing prior studies. We also evaluated this model on the MIMIC-III dataset, achieving a 95.9% accuracy in CVD detection, and on the PPG-BP dataset, achieving 75.9% accuracy in related CVD diabetes detection, indicating its generalizability. As a proof of concept, an earring wearable named PEARL (prototype), was designed to scale up the point-of-care (POC) healthcare system.
