Qin Caijie, Li Yong, Liu Chibiao, Ma Xibo
Institute of Information Engineering, Sanming University, Sanming 365004, China.
CBSR&NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing 100049, China.
Bioengineering (Basel). 2023 Mar 24;10(4):400. doi: 10.3390/bioengineering10040400.
Cardiovascular disease (CVD) has become a common health problem of mankind, and the prevalence and mortality of CVD are rising on a year-to-year basis. Blood pressure (BP) is an important physiological parameter of the human body and also an important physiological indicator for the prevention and treatment of CVD. Existing intermittent measurement methods do not fully indicate the real BP status of the human body and cannot get rid of the restraining feeling of a cuff. Accordingly, this study proposed a deep learning network based on the ResNet34 framework for continuous prediction of BP using only the promising PPG signal. The high-quality PPG signals were first passed through a multi-scale feature extraction module after a series of pre-processing to expand the perceptive field and enhance the perception ability on features. Subsequently, useful feature information was then extracted by stacking multiple residual modules with channel attention to increase the accuracy of the model. Lastly, in the training stage, the Huber loss function was adopted to stabilize the iterative process and obtain the optimal solution of the model. On a subset of the MIMIC dataset, the errors of both SBP and DBP predicted by the model met the AAMI standards, while the accuracy of DBP reached Grade A of the BHS standard, and the accuracy of SBP almost reached Grade A of the BHS standard. The proposed method verifies the potential and feasibility of PPG signals combined with deep neural networks in the field of continuous BP monitoring. Furthermore, the method is easy to deploy in portable devices, and it is more consistent with the future trend of wearable blood-pressure-monitoring devices (e.g., smartphones and smartwatches).
心血管疾病（CVD）已成为人类常见的健康问题，且CVD的患病率和死亡率逐年上升。血压（BP）是人体重要的生理参数，也是预防和治疗CVD的重要生理指标。现有的间歇性测量方法不能充分反映人体的真实血压状况，且无法摆脱袖带的束缚感。因此，本研究提出了一种基于ResNet34框架的深度学习网络，仅使用有前景的光电容积脉搏波（PPG）信号进行血压的连续预测。高质量的PPG信号在经过一系列预处理后，首先通过多尺度特征提取模块，以扩大感知野并增强对特征的感知能力。随后，通过堆叠多个带有通道注意力的残差模块来提取有用的特征信息，以提高模型的准确性。最后，在训练阶段，采用Huber损失函数来稳定迭代过程并获得模型的最优解。在MIMIC数据集的一个子集上，该模型预测的收缩压（SBP）和舒张压（DBP）误差均符合美国医疗器械促进协会（AAMI）标准，而DBP的准确率达到了英国高血压协会（BHS）标准的A级，SBP的准确率几乎达到了BHS标准的A级。所提出的方法验证了PPG信号结合深度神经网络在连续血压监测领域的潜力和可行性。此外，该方法易于在便携式设备中部署，更符合可穿戴血压监测设备（如智能手机和智能手表）的未来发展趋势。
