Xi'an Institute of Optics and Precision Mechanics of CAS, Xi'an 710119, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Biosensors (Basel). 2021 Apr 13;11(4):120. doi: 10.3390/bios11040120.
According to the WTO, there were 1.13 billion hypertension patients worldwide in 2015. The WTO encouraged people to check the blood pressure regularly because a large amount of patients do not have any symptoms. However, traditional cuff measurement results are not enough to represent the patient's blood pressure status over a period of time. Therefore, there is an urgent need for portable, easy to operate, continuous measurement, and low-cost blood pressure measuring devices. In this paper, we adopted the convolutional neural network (CNN), based on the Hilbert-Huang Transform (HHT) method, to predict blood pressure (BP) risk level using photoplethysmography (PPG). Considering that the PPG's first and second derivative signals are related to atherosclerosis and vascular elasticity, we created a dataset called PPG+; the images of PPG+ carry information on PPG and its derivatives. We built three classification experiments by collecting 582 data records (the length of each record is 10 s) from the Medical Information Mart for Intensive Care (MIMIC) database: NT (normotension) vs. HT (hypertension), NT vs. PHT (prehypertension), and (NT + PHT) vs. HT; the F1 scores of the PPG + experiments using AlexNet were 98.90%, 85.80%, and 93.54%, respectively. We found that, first, the dataset established by the HHT method performed well in the BP grade prediction experiment. Second, because the Hilbert spectra of the PPG are simple and periodic, AlexNet, which has only 8 layers, got better results. More layers instead increased the cost and difficulty of training.
根据世界贸易组织（WTO）的数据，2015 年全球有 11.3 亿高血压患者。WTO 鼓励人们定期检查血压，因为大量患者没有任何症状。然而，传统的袖带测量结果不足以代表患者在一段时间内的血压状况。因此，迫切需要一种便携式、易于操作、连续测量和低成本的血压测量设备。在本文中，我们采用卷积神经网络（CNN），基于希尔伯特-黄变换（HHT）方法，使用光体积描记法（PPG）预测血压（BP）风险水平。考虑到 PPG 的一阶和二阶导数信号与动脉粥样硬化和血管弹性有关，我们创建了一个名为 PPG+的数据集；PPG+的图像携带 PPG 及其导数的信息。我们通过从医疗信息监护（MIMIC）数据库中收集 582 个数据记录（每个记录的长度为 10 秒），构建了三个分类实验：NT（正常血压）与 HT（高血压）、NT 与 PHT（高血压前期）和（NT+PHT）与 HT；使用 AlexNet 的 PPG+实验的 F1 分数分别为 98.90%、85.80%和 93.54%。我们发现，首先，HHT 方法建立的数据集在 BP 等级预测实验中表现良好。其次，由于 PPG 的希尔伯特谱简单且具有周期性，因此只有 8 层的 AlexNet 获得了更好的结果。更多的层反而增加了训练的成本和难度。
