Zhang Qingxue, Zhou Dian, Zeng Xuan
Departpment of Electrical Engineering, University of Texas at Dallas, 800 W Campbell Rd, Richardson, TX, 75080, USA.
Department of Microelectronics, Fudan University, 220 Handan Rd, Shanghai, 200433, China.
Biomed Eng Online. 2017 Feb 6;16(1):23. doi: 10.1186/s12938-017-0317-z.
Long-term continuous systolic blood pressure (SBP) and heart rate (HR) monitors are of tremendous value to medical (cardiovascular, circulatory and cerebrovascular management), wellness (emotional and stress tracking) and fitness (performance monitoring) applications, but face several major impediments, such as poor wearability, lack of widely accepted robust SBP models and insufficient proofing of the generalization ability of calibrated models.
This paper proposes a wearable cuff-less electrocardiography (ECG) and photoplethysmogram (PPG)-based SBP and HR monitoring system and many efforts are made focusing on above challenges. Firstly, both ECG/PPG sensors are integrated into a single-arm band to provide a super wearability. A highly convenient but challenging single-lead configuration is proposed for weak single-arm-ECG acquisition, instead of placing the electrodes on the chest, or two wrists. Secondly, to identify heartbeats and estimate HR from the motion artifacts-sensitive weak arm-ECG, a machine learning-enabled framework is applied. Then ECG-PPG heartbeat pairs are determined for pulse transit time (PTT) measurement. Thirdly, a PTT&HR-SBP model is applied for SBP estimation, which is also compared with many PTT-SBP models to demonstrate the necessity to introduce HR information in model establishment. Fourthly, the fitted SBP models are further evaluated on the unseen data to illustrate the generalization ability. A customized hardware prototype was established and a dataset collected from ten volunteers was acquired to evaluate the proof-of-concept system.
The semi-customized prototype successfully acquired from the left upper arm the PPG signal, and the weak ECG signal, the amplitude of which is only around 10% of that of the chest-ECG. The HR estimation has a mean absolute error (MAE) and a root mean square error (RMSE) of only 0.21 and 1.20 beats per min, respectively. Through the comparative analysis, the PTT&HR-SBP models significantly outperform the PTT-SBP models. The testing performance is 1.63 ± 4.44, 3.68, 4.71 mmHg in terms of mean error ± standard deviation, MAE and RMSE, respectively, indicating a good generalization ability on the unseen fresh data.
The proposed proof-of-concept system is highly wearable, and its robustness is thoroughly evaluated on different modeling strategies and also the unseen data, which are expected to contribute to long-term pervasive hypertension, heart health and fitness management.
长期连续的收缩压（SBP）和心率（HR）监测仪对医学（心血管、循环和脑血管管理）、健康（情绪和压力追踪）以及健身（运动表现监测）应用具有巨大价值，但面临一些主要障碍，如可穿戴性差、缺乏广泛接受的可靠SBP模型以及校准模型的泛化能力验证不足。
本文提出了一种基于可穿戴式无袖心电图（ECG）和光电容积脉搏波描记图（PPG）的SBP和HR监测系统，并针对上述挑战做出了诸多努力。首先，将ECG/PPG传感器集成到单臂手环中，以提供超强的可穿戴性。针对微弱的单臂ECG采集，提出了一种高度便捷但具有挑战性的单导联配置，而非将电极置于胸部或双腕。其次，为从对运动伪影敏感的微弱手臂ECG中识别心跳并估计HR，应用了一个基于机器学习的框架。然后确定ECG-PPG心跳对以进行脉搏传输时间（PTT）测量。第三，应用PTT&HR-SBP模型进行SBP估计，并将其与许多PTT-SBP模型进行比较，以证明在模型建立中引入HR信息的必要性。第四，在未见数据上进一步评估拟合的SBP模型，以说明其泛化能力。建立了一个定制的硬件原型，并采集了来自十名志愿者的数据集，以评估概念验证系统。
半定制原型成功从左上臂采集到PPG信号以及微弱的ECG信号，其幅度仅约为胸部ECG的10%。HR估计的平均绝对误差（MAE）和均方根误差（RMSE）分别仅为每分钟0.21次和1.20次心跳。通过对比分析，PTT&HR-SBP模型显著优于PTT-SBP模型。在平均误差±标准差、MAE和RMSE方面，测试性能分别为1.63±4.44、3.68、4.71 mmHg，表明对未见的新数据具有良好的泛化能力。
所提出的概念验证系统具有高度可穿戴性，并且在不同建模策略以及未见数据上对其稳健性进行了全面评估，有望为长期普遍的高血压、心脏健康和健身管理做出贡献。
