论文标题
使用循环生成对抗网络从光杀解物学产生的新型血压波形重建
Novel Blood Pressure Waveform Reconstruction from Photoplethysmography using Cycle Generative Adversarial Networks
论文作者
论文摘要
连续监测血压（BP）可以帮助个人管理其慢性疾病，例如高血压，需要在自由生活条件下进行非侵入性测量方法。最近使用不同的机器和深度学习方法来进行非侵入性估计BP的方法融合光插图仪（PPG）和心电图（ECG）信号；但是，它们无法重建完整的信号，从而导致模型较少。在本文中，我们提出了一种基于循环生成的对抗网络（CycleGAN）方法，以从干净的PPG信号中提取称为卧床血压（ABP）的BP信号。我们的方法使用一个循环生成的对抗网络，该网络扩展了域翻译的gan架构，并且在BP估计中，最大的最高范围高于最先进的方法。
Continuous monitoring of blood pressure (BP)can help individuals manage their chronic diseases such as hypertension, requiring non-invasive measurement methods in free-living conditions. Recent approaches fuse Photoplethysmograph (PPG) and electrocardiographic (ECG) signals using different machine and deep learning approaches to non-invasively estimate BP; however, they fail to reconstruct the complete signal, leading to less accurate models. In this paper, we propose a cycle generative adversarial network (CycleGAN) based approach to extract a BP signal known as ambulatory blood pressure (ABP) from a clean PPG signal. Our approach uses a cycle generative adversarial network that extends theGAN architecture for domain translation, and outperforms state-of-the-art approaches by up to 2x in BP estimation.
