论文标题
PPG2ABP：使用完全卷积神经网络将光杀解功能图（PPG）信号转换为动脉血压（ABP）波形
PPG2ABP: Translating Photoplethysmogram (PPG) Signals to Arterial Blood Pressure (ABP) Waveforms using Fully Convolutional Neural Networks
论文作者
论文摘要
心血管疾病是死亡率最严重的原因之一，每年在世界各地遭受沉重的生命。对血压的持续监测似乎是最可行的选择，但这需要一个侵入性的过程，带来了几层复杂性。这促使我们开发了一种通过使用光摄像机（PPG）信号的非侵入性方法来预测连续动脉血压（ABP）波形的方法。此外，我们通过使手工制作的功能计算无关紧要，这是对现有方法的缺点，从而探索了深度学习的优势，因为它可以使我们无法坚持理想形状的PPG信号。因此，我们提出了一种基于深度学习的方法PPG2ABP，该方法可以从输入PPG信号中预测连续的ABP波形，平均绝对误差为4.604 mmHg，可保留一致的形状，大小和相位。但是，PPG2ABP的成功率更大，事实证明，来自预测的ABP波形的DBP，MAP和SBP的计算值在几个指标下优于现有作品，尽管PPG2ABP并未明确培训ppg2ABP。
Cardiovascular diseases are one of the most severe causes of mortality, taking a heavy toll of lives annually throughout the world. The continuous monitoring of blood pressure seems to be the most viable option, but this demands an invasive process, bringing about several layers of complexities. This motivates us to develop a method to predict the continuous arterial blood pressure (ABP) waveform through a non-invasive approach using photoplethysmogram (PPG) signals. In addition we explore the advantage of deep learning as it would free us from sticking to ideally shaped PPG signals only, by making handcrafted feature computation irrelevant, which is a shortcoming of the existing approaches. Thus, we present, PPG2ABP, a deep learning based method, that manages to predict the continuous ABP waveform from the input PPG signal, with a mean absolute error of 4.604 mmHg, preserving the shape, magnitude and phase in unison. However, the more astounding success of PPG2ABP turns out to be that the computed values of DBP, MAP and SBP from the predicted ABP waveform outperforms the existing works under several metrics, despite that PPG2ABP is not explicitly trained to do so.
