Department of Automation, College of Electrical Engineering, Sichuan University, Chengdu 610065, China.
Key Laboratory of Information and Automation Technology of Sichuan Province, Chengdu 610065, China.
Sensors (Basel). 2023 Mar 9;23(6):2963. doi: 10.3390/s23062963.
Blood pressure (BP) monitoring is vital in daily healthcare, especially for cardiovascular diseases. However, BP values are mainly acquired through a contact-sensing method, which is inconvenient and unfriendly for BP monitoring. This paper proposes an efficient end-to-end network for estimating BP values from a facial video to achieve remote BP estimation in daily life. The network first derives a spatiotemporal map of a facial video. Then, it regresses the BP ranges with a designed blood pressure classifier and simultaneously calculates the specific value with a blood pressure calculator in each BP range based on the spatiotemporal map. In addition, an innovative oversampling training strategy was developed to handle the problem of unbalanced data distribution. Finally, we trained the proposed blood pressure estimation network on a private dataset, MPM-BP, and tested it on a popular public dataset, MMSE-HR. As a result, the proposed network achieved a mean absolute error (MAE) and root mean square error (RMSE) of 12.35 mmHg and 16.55 mmHg on systolic BP estimations, and those for diastolic BP were 9.54 mmHg and 12.22 mmHg, which were better than the values obtained in recent works. It can be concluded that the proposed method has excellent potential for camera-based BP monitoring in the indoor scenarios in the real world.
血压（BP）监测在日常医疗保健中至关重要，特别是对心血管疾病而言。然而，BP 值主要通过接触式传感方法获得，这种方法既不方便也不适合用于 BP 监测。本文提出了一种高效的端到端网络，可从面部视频中估计 BP 值，从而实现日常生活中的远程 BP 估计。该网络首先从面部视频中推导出时空图谱。然后，它使用设计的血压分类器回归 BP 范围，并基于时空图谱在每个 BP 范围内使用血压计算器计算特定值。此外，还开发了一种创新的过采样训练策略来处理数据分布不平衡的问题。最后，我们在私人数据集 MPM-BP 上训练了所提出的血压估计网络，并在流行的公共数据集 MMSE-HR 上对其进行了测试。结果表明，所提出的网络在收缩压估计方面的平均绝对误差（MAE）和均方根误差（RMSE）分别为 12.35mmHg 和 16.55mmHg，舒张压的 MAE 和 RMSE 分别为 9.54mmHg 和 12.22mmHg，优于近期研究的结果。可以得出结论，该方法在真实世界室内场景下基于摄像头的 BP 监测具有很大的潜力。
