Technology that translates photoplethysmogram (PPG) into the QRS complex of electrocardiogram (ECG) would be transformative for people who require continuously monitoring. However, directly decoding the QRS complex of ECG from PPG is challenging because PPG signals usually have different offsets due to 1) different devices, and 2) personal differences, which makes the alignment difficult. In this paper, we make the first attempt to reconstruct the QRS complex of ECG only from the recording of PPG by an end-to-end deep learning-based approach. Specifically, we propose a novel encoder-decoder architecture containing three components: 1) a sequence transformer network which automatically calibrates the offset, 2) an attention network, which dynamically identifies regions of interest, and 3) a new QRS complex-enhanced loss for better reconstruction. The experiment results on a real dataset demonstrate the effectiveness of the proposed method: 3.67% R peak failure rate of the reconstructed ECG and high correlation of pulse transit time between the reconstructed QRS complex and the groundtruth QRS complex ( $\rho = {\sf 0.844}$ ), which creates a new opportunity for low-cost clinical studies via the waveform-level reconstruction of the QRS complex of ECG from PPG.

中文翻译：

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通过转换注意神经网络从 PPG 重建 QRS 复合体

将光电容积脉搏波 (PPG) 转换为心电图 (ECG) 的 QRS 波群的技术对于需要持续监测的人来说将是变革性的。然而，从 PPG 直接解码 ECG 的 QRS 复合波是具有挑战性的，因为 PPG 信号通常由于 1) 不同的设备和 2) 个人差异而具有不同的偏移，这使得对齐变得困难。在本文中，我们首次尝试通过基于端到端深度学习的方法仅从 PPG 的记录中重建 ECG 的 QRS 复合波。具体来说，我们提出了一种新的编码器 - 解码器架构，包含三个组件：1）一个自动校准偏移量的序列变换器网络，2）一个动态识别感兴趣区域的注意力网络，以及 3）一个新的 QRS 复合增强损失更好的重建。