School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 102488, China.
School of Information, North China University of Technology, Beijing, 100144, China.
Comput Biol Med. 2023 Sep;164:107112. doi: 10.1016/j.compbiomed.2023.107112. Epub 2023 Jun 1.
Hypertension is a major cause of cardiovascular diseases. Accurate and convenient measurement of blood pressure are necessary for the detection, treatment, and control of hypertension. In recent years, face video based non-contact blood pressure prediction is a promising research topic. Interestingly, face diagnosis has been an important part of traditional Chinese medicine (TCM) for thousands of years. TCM practitioners observe some typical regions of the face to determine the health status of the Zang Fu organs (i.e., heart). However, the effectiveness of face diagnosis theory in conjunction with computer vision analysis techniques to predict blood pressure is unclear. We proposed an artificial intelligence framework for predicting blood pressure using deep convolutional neural networks in this study. First, we extracted pulse wave signals through 652 facial videos. Then, we trained and compared nine artificial neural networks and chose the best performed prediction model, with an overall true predict rate of 90%. We also investigated the impact of face reflex regions selection on blood pressure prediction model, and the five face regions outperformed. Our high effectiveness and stability framework may provide an objective and convenient computer-aided blood pressure prediction method for hypertension screening and disease prevention.
高血压是心血管疾病的主要病因。准确、便捷地测量血压对于高血压的检测、治疗和控制非常必要。近年来，基于面部视频的非接触式血压预测是一个很有前途的研究课题。有趣的是，面诊几千年来一直是中医（TCM）的重要组成部分。中医从业者通过观察面部的一些典型区域来确定脏腑（即心脏）的健康状况。然而，面诊理论与计算机视觉分析技术相结合来预测血压的有效性尚不清楚。在这项研究中，我们提出了一种使用深度卷积神经网络预测血压的人工智能框架。首先，我们通过 652 个面部视频提取脉搏波信号。然后，我们训练和比较了 9 个人工神经网络，并选择了表现最好的预测模型，整体真实预测率为 90%。我们还研究了对面部反射区域选择对血压预测模型的影响，发现五个面部区域表现优于其他区域。我们的高效、稳定的框架可能为高血压筛查和疾病预防提供一种客观、便捷的计算机辅助血压预测方法。
