Qiao Minghong, Chang Li, Zhou Zili, Jun Sam Cheng, He Ling, Zhang Jing
College of Biomedical Engineering, Sichuan University, Chengdu, People's Republic of China.
Department of Emergency, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.
Physiol Meas. 2025 Feb 7;13(2). doi: 10.1088/1361-6579/adae50.
This paper presents a novel dual-branch framework for estimating blood pressure (BP) using photoplethysmography (PPG) signals. The method combines deep learning with clinical prior knowledge and models different time periods (morning, afternoon, and evening) to achieve precise, cuffless BP estimation.Preprocessed single-channel PPG signals are input into two feature extraction branches. The first branch converts PPG dimensions to 2D and uses pre-trained Mobile Vision Transformer-v2 (MobileViTv2) and Visual Geometry Group19 (Vgg19) backbones to extract deep PPG features based on the different mechanisms of systolic blood pressure (SBP) and diastolic blood pressure (DBP) formation. The second branch calculates multi-dimensional feature parameters based on the relationship between PPG waveforms and factors affecting BP. We fuse the features from both branches and consider diurnal BP variations, using AutoML strategy to construct specific SBP and DBP estimation models for the different periods. The algorithm was developed on the human resting state PPG and BP dataset (HRSD) and validated on the MIMIC-IV dataset for generalization performance.The mean absolute error (MAE) for BP estimation is 6.42 mmHg SBP and 4.96 mmHg DBP in the morning, 4.84 mmHg (SBP) and 3.73 mmHg (DBP) in the afternoon, and 2.65 mmHg (SBP) and 2.56 mmHg (DBP) in the evening. Performance on the MIMIC-IV database was 4.34 mmHg (SBP) and 3.11 mmHg (DBP). The method meets the standards of the Association for the Advancement of Medical Instrumentation and achieves Grade A of the British Hypertension Society (BHS) standards.. This indicates that it is an accurate and reliable non-invasive BP monitoring technology, applicable for continuous health monitoring and cardiovascular disease prevention.
本文提出了一种用于使用光电容积脉搏波描记术（PPG）信号估计血压（BP）的新型双分支框架。该方法将深度学习与临床先验知识相结合，并对不同时间段（早晨、下午和晚上）进行建模，以实现精确的无袖带血压估计。预处理后的单通道PPG信号被输入到两个特征提取分支中。第一个分支将PPG维度转换为二维，并使用预训练的移动视觉变换器v2（MobileViTv2）和视觉几何组19（Vgg19）主干，基于收缩压（SBP）和舒张压（DBP）形成的不同机制提取深度PPG特征。第二个分支根据PPG波形与影响血压的因素之间的关系计算多维特征参数。我们融合两个分支的特征并考虑血压的昼夜变化，使用自动机器学习策略为不同时间段构建特定的SBP和DBP估计模型。该算法是在人体静息状态PPG和血压数据集（HRSD）上开发的，并在MIMIC-IV数据集上进行验证以评估泛化性能。血压估计的平均绝对误差（MAE）在早晨为SBP 6.42 mmHg和DBP 4.96 mmHg，下午为4.84 mmHg（SBP）和3.73 mmHg（DBP），晚上为2.65 mmHg（SBP）和2.56 mmHg（DBP）。在MIMIC-IV数据库上的性能为4.34 mmHg（SBP）和3.11 mmHg（DBP）。该方法符合医疗仪器促进协会的标准，并达到英国高血压学会（BHS）标准的A级。这表明它是一种准确可靠的无创血压监测技术，适用于连续健康监测和心血管疾病预防。
