Department of Computer Engineering, Kwangwoon University, Seoul 01897, Korea.
Interdisciplinary Program in Bioengineering, Seoul National University, Seoul 03080, Korea.
Sensors (Basel). 2020 Apr 20;20(8):2338. doi: 10.3390/s20082338.
Blood pressure (BP) is a vital sign that provides fundamental health information regarding patients. Continuous BP monitoring is important for patients with hypertension. Various studies have proposed cuff-less BP monitoring methods using pulse transit time. We propose an end-to-end deep learning architecture using only raw signals without the process of extracting features to improve the BP estimation performance using the attention mechanism. The proposed model consisted of a convolutional neural network, a bidirectional gated recurrent unit, and an attention mechanism. The model was trained by a calibration-based method, using the data of each subject. The performance of the model was compared to the model that used each combination of the three signals, and the model with the attention mechanism showed better performance than other state-of-the-art methods, including conventional linear regression method using pulse transit time (PTT). A total of 15 subjects were recruited, and electrocardiogram, ballistocardiogram, and photoplethysmogram levels were measured. The 95% confidence interval of the reference BP was [86.34, 143.74] and [51.28, 88.74] for systolic BP (SBP) and diastolic BP (DBP), respectively. The R 2 values were 0.52 and 0.49, and the mean-absolute-error values were 4.06 ± 4.04 and 3.33 ± 3.42 for SBP and DBP, respectively. In addition, the results complied with global standards. The results show the applicability of the proposed model as an analytical metric for BP estimation.
血压（BP）是提供有关患者基本健康信息的重要生命体征。高血压患者需要连续监测血压。各种研究已经提出了使用脉搏传输时间的无袖带血压监测方法。我们提出了一种端到端的深度学习架构，仅使用原始信号而不经过特征提取过程，以使用注意力机制提高血压估计性能。所提出的模型由卷积神经网络、双向门控循环单元和注意力机制组成。该模型通过基于校准的方法进行训练，使用每个受试者的数据。将模型的性能与使用三种信号的每种组合的模型进行了比较，并且具有注意力机制的模型的性能优于包括使用脉搏传输时间（PTT）的传统线性回归方法在内的其他最新方法。共招募了 15 名受试者，测量了心电图、心冲击图和光体积描记图的水平。参考血压的 95%置信区间为 [86.34, 143.74] 和 [51.28, 88.74]，分别为收缩压（SBP）和舒张压（DBP）。R 2 值分别为 0.52 和 0.49，平均绝对误差值分别为 4.06±4.04 和 3.33±3.42，用于 SBP 和 DBP。此外，结果符合全球标准。结果表明，所提出的模型可作为血压估计的分析指标具有适用性。
