Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan.
Department of Automatic Control Engineering, Feng Chia University, Taichung 40724, Taiwan.
Sensors (Basel). 2020 Sep 30;20(19):5606. doi: 10.3390/s20195606.
Blood pressure monitoring is one avenue to monitor people's health conditions. Early detection of abnormal blood pressure can help patients to get early treatment and reduce mortality associated with cardiovascular diseases. Therefore, it is very valuable to have a mechanism to perform real-time monitoring for blood pressure changes in patients. In this paper, we propose deep learning regression models using an electrocardiogram (ECG) and photoplethysmogram (PPG) for the real-time estimation of systolic blood pressure (SBP) and diastolic blood pressure (DBP) values. We use a bidirectional layer of long short-term memory (LSTM) as the first layer and add a residual connection inside each of the following layers of the LSTMs. We also perform experiments to compare the performance between the traditional machine learning methods, another existing deep learning model, and the proposed deep learning models using the dataset of Physionet's multiparameter intelligent monitoring in intensive care II (MIMIC II) as the source of ECG and PPG signals as well as the arterial blood pressure (ABP) signal. The results show that the proposed model outperforms the existing methods and is able to achieve accurate estimation which is promising in order to be applied in clinical practice effectively.
血压监测是监测人们健康状况的一种途径。早期发现异常血压可以帮助患者得到早期治疗，降低与心血管疾病相关的死亡率。因此，拥有一种对患者血压变化进行实时监测的机制是非常有价值的。在本文中，我们提出了使用心电图 (ECG) 和光电容积脉搏波 (PPG) 的深度学习回归模型，用于实时估计收缩压 (SBP) 和舒张压 (DBP) 值。我们使用双向长短期记忆 (LSTM) 作为第一层，并在 LSTM 的每个后续层内部添加一个残差连接。我们还进行了实验，比较了传统机器学习方法、另一个现有的深度学习模型以及使用 Physionet 的多参数智能监护仪 II (MIMIC II) 数据集作为 ECG 和 PPG 信号以及动脉血压 (ABP) 信号来源的所提出的深度学习模型之间的性能。结果表明，所提出的模型优于现有方法，能够实现准确的估计，有望在临床实践中得到有效应用。
