Blood Pressure Estimation Using On-body Continuous Wave Radar and Photoplethysmogram in Various Posture and Exercise Conditions

• Published in: Scientific reports Vol. 9; no. 1; pp. 16346 - 13
• Main Authors: Pour Ebrahim, Malikeh, Heydari, Fatemeh, Wu, Taiyang, Walker, Katherine, Joe, Keith, Redoute, Jean-Michel, Yuce, Mehmet Rasit
• Format: Journal Article Web Resource
• Language: English
• Published: London Nature Publishing Group UK 08.11.2019; Nature Publishing Group
• Subjects: 639/766/1130; 692/4019; 692/700/139; 9/10; Adult; Aged; Algorithms; Aortic valve; Blood Pressure; Blood Pressure Determination - methods; EKG; Electrical & electronics engineering; Engineering, computing & technology; Exercise; Female; Healthy Volunteers; Heart Rate; Humanities and Social Sciences; Humans; Ingénierie électrique & électronique; Ingénierie, informatique & technologie; Male; Mathematical models; Middle Aged; Monitoring, Physiologic - methods; multidisciplinary; Photoplethysmography - methods; Posture; Pulse Wave Analysis; Radar; Science; Science (multidisciplinary); Sensors; Signal processing; Signal Processing, Computer-Assisted; Sternum; Wavelet transforms
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-019-52710-8

The pulse arrival time (PAT), pre-ejection period (PEP) and pulse transit time (PTT) are calculated using on-body continuous wave radar (CWR), Photoplethysmogram (PPG) and Electrocardiogram (ECG) sensors for wearable continuous systolic blood pressure (SBP) measurements. The CWR and PPG sensors are placed on the sternum and left earlobe respectively. This paper presents a signal processing method based on wavelet transform and adaptive filtering to remove noise from CWR signals. Experimental data are collected from 43 subjects in various static postures and 26 subjects doing 6 different exercise tasks. Two mathematical models are used to calculate SBPs from PTTs/PATs. For 38 subjects participating in posture tasks, the best cumulative error percentage (CEP) is 92.28% and for 21 subjects participating in exercise tasks, the best CEP is 82.61%. The results show the proposed method is promising in estimating SBP using PTT. Additionally, removing PEP from PAT leads to improving results by around 9%. The CWR sensors present a low-power, continuous and potentially wearable system with minimal body contact to monitor aortic valve mechanical activities directly. Results of this study, of wearable radar sensors, demonstrate the potential superiority of CWR-based PEP extraction for various medical monitoring applications, including BP measurement.