Feature Extraction Of PPG By Convolutional Kernel And Estimation Of ABP By Higher-Order Regression

It is important to measure the blood pressure resulting from ischemic stroke or myocardial infarction during the long term of daily life to provide awareness of the need to increase healthy life expectancy. However, it is difficult to measure blood pressure for a long period of time using an invasive cartel or a cuff system with compression. Therefore, in this study, we estimate ABP from PPG, which is a non-invasive optical blood volume measurement. We used deep learning to obtain robust features for two groups of variables, including local variation and potential vascular elasticity. In addition, the direct projection from PPG to ABP is difficult and difficult to explain. Therefore, we set the problem of blood pressure estimation by reducing the difficulty to a regression problem with a rounded projection, which is relatively easy to explain. As a result, we obtained a MAE of 3.39 and an STD of 5.88, which are close to those of previous studies. Although more research on individual differences and robustness between groups is needed, the results are promising for future development in medical practice where explanations are needed.