Highly wearable cuff-less blood pressure and heart rate monitoring with single-arm electrocardiogram and photoplethysmogram signals.
BACKGROUND
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.


METHODS
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.


RESULTS
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.


CONCLUSIONS
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.
Departpment of Electrical Engineering, University of Texas at Dallas, 800 W Campbell Rd, Richardson, TX, 75080, USA. qingxue.zhg@gmail.com.
Qingxue
http://orcid.org/0000-0001-7057-2088
Q
Zhang
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.
Dian
D
Zhou
Department of Microelectronics, Fudan University, 220 Handan Rd, Shanghai, 200433, China.
Xuan
X
Zeng
D000328:Adult; D001795:Blood Pressure Determination / Q000295:instrumentation / Q000379:methods*; D004562:Electrocardiography; D005260:Female; D006339:Heart Rate; D006801:Humans; D008297:Male; D017156:Photoplethysmography; D063177:Pulse Wave Analysis; D012815:Signal Processing, Computer-Assisted
Blood pressure; Electrocardiography; Heart rate; Machine learning; Photoplethysmogram; Pulse transit time; Wearable computers