Video-based Contactless Blood Pressure Estimation: A Review

Hypertension (HTN) is viewed as a significant but modifiable element to Cardiovascular disease (CVD). A non-invasive BP monitoring system continuously is beneficial to early detection and prevention of CVD, considering the inconvenience and discomfort of the conventional BP measurement methods. Recently, as the remote pulse detection technique develops, remote photoplethysmography (rPPG), measuring BP based on videos without contact becomes possible. It has been developed in the past few years. The aim of this review is to introduce a general framework of video-based BP estimation, analyze the related algorithms and summarize the existing studies. Moreover, we introduce the possible future directions in this field.

[1]  Gerard de Haan,et al.  Robust Pulse Rate From Chrominance-Based rPPG , 2013, IEEE Transactions on Biomedical Engineering.

[2]  Saso Koceski,et al.  Continuous Blood Pressure Monitoring as a Basis for Ambient Assisted Living (AAL) – Review of Methodologies and Devices , 2019, Journal of Medical Systems.

[3]  A. Patzak,et al.  Continuous blood pressure measurement by using the pulse transit time: comparison to a cuff-based method , 2011, European Journal of Applied Physiology.

[4]  Akio Nozawa,et al.  Contactless blood pressure sensing using facial visible and thermal images , 2018, Artificial Life and Robotics.

[5]  Survi Kyal,et al.  Toward Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Theory and Practice , 2015, IEEE Transactions on Biomedical Engineering.

[6]  Jun Ozawa,et al.  Improved human pulse peak estimation using derivative features for noncontact pulse transit time measurements , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[7]  Zhong-Ping Feng,et al.  Smartphone-Based Blood Pressure Measurement Using Transdermal Optical Imaging Technology. , 2019, Circulation. Cardiovascular imaging.

[8]  Aniruddha Sinha,et al.  Estimation of blood pressure levels from reflective Photoplethysmograph using smart phones , 2013, 13th IEEE International Conference on BioInformatics and BioEngineering.

[9]  Domenico Grimaldi,et al.  A Neural Network-based method for continuous blood pressure estimation from a PPG signal , 2013, 2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).

[10]  Qiaolin Ye,et al.  Robust blood pressure estimation using an RGB camera , 2018, J. Ambient Intell. Humaniz. Comput..

[11]  R. Romashko,et al.  A new look at the essence of the imaging photoplethysmography , 2015, Scientific Reports.

[12]  G. Haan,et al.  Improved motion robustness of remote-PPG by using the blood volume pulse signature , 2014, Physiological measurement.

[13]  L. O. Svaasand,et al.  Remote plethysmographic imaging using ambient light. , 2008, Optics express.

[14]  M. Y. Mashor,et al.  Measuring of Systolic Blood Pressure Based On Heart Rate , 2008 .

[15]  Sander Stuijk,et al.  Algorithmic Principles of Remote PPG , 2017, IEEE Transactions on Biomedical Engineering.

[16]  Claudia Gonzalez Viejo,et al.  Non-Contact Heart Rate and Blood Pressure Estimations from Video Analysis and Machine Learning Modelling Applied to Food Sensory Responses: A Case Study for Chocolate , 2018, Sensors.

[17]  Ashok Veeraraghavan,et al.  DistancePPG: Robust non-contact vital signs monitoring using a camera , 2015, Biomedical optics express.

[18]  Po-Wei Huang,et al.  Image based contactless blood pressure assessment using Pulse Transit Time , 2017, 2017 International Automatic Control Conference (CACS).

[19]  Tomoyuki Yambe,et al.  Contactless Technique for Measuring Blood-Pressure Variability from One Region in Video Plethysmography , 2019 .

[20]  Aljo Mujcic,et al.  Blood pressure estimation using video plethysmography , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[21]  Joseph Finkelstein,et al.  Introducing Contactless Blood Pressure Assessment Using a High Speed Video Camera , 2016, Journal of Medical Systems.

[22]  Tardi Tjahjadi,et al.  Robust contactless pulse transit time estimation based on signal quality metric , 2020, Pattern Recognit. Lett..

[23]  Frédéric Bousefsaf,et al.  Continuous wavelet filtering on webcam photoplethysmographic signals to remotely assess the instantaneous heart rate , 2013, Biomed. Signal Process. Control..

[24]  Tzung K. Hsiai,et al.  Cuff-Less and Continuous Blood Pressure Monitoring: A Methodological Review , 2017 .

[25]  Sujay Deb,et al.  Face video based touchless blood pressure and heart rate estimation , 2016, 2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP).

[26]  Jun Ozawa,et al.  Non-contact pulse transit time measurement using imaging camera, and its relation to blood pressure , 2015, 2015 14th IAPR International Conference on Machine Vision Applications (MVA).

[27]  Khanam,et al.  Remote Monitoring of Vital Signs in Diverse Non-Clinical and Clinical Scenarios Using Computer Vision Systems: A Review , 2019, Applied Sciences.

[28]  Maxime Cannesson,et al.  Non-invasive continuous blood pressure monitoring: a review of current applications , 2013, Frontiers of Medicine.

[29]  Tomasz Kocejko,et al.  Proceedings of the Federated Conference on Computer Science and Information Systems pp. 405–410 ISBN 978-83-60810-22-4 Measuring Pulse Rate with a Webcam – a Non-contact Method for Evaluating Cardiac Activity , 2022 .

[30]  Makoto Yoshizawa,et al.  Techniques for estimating blood pressure variation using video images , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[31]  Changyun Wen,et al.  Continuous and Noninvasive Measurement of Systolic and Diastolic Blood Pressure by One Mathematical Model with the Same Model Parameters and Two Separate Pulse Wave Velocities , 2011, Annals of Biomedical Engineering.

[32]  Yuting Yang,et al.  Noncontact Monitoring Breathing Pattern, Exhalation Flow Rate and Pulse Transit Time , 2014, IEEE Transactions on Biomedical Engineering.

[33]  Asanka G. Perera,et al.  Remote monitoring of cardiorespiratory signals from a hovering unmanned aerial vehicle , 2017, BioMedical Engineering OnLine.

[34]  Luis Enrique Mendoza,et al.  Relationship of blood pressure with the electrical signal of the heart using signal processing Relación entre la presión sanguínea y la señal eléctrica del corazón usando una señal de procesamiento , 2014 .

[35]  Yuan-Ting Zhang,et al.  Continuous Blood Pressure Measurement From Invasive to Unobtrusive: Celebration of 200th Birth Anniversary of Carl Ludwig , 2016, IEEE J. Biomed. Health Informatics.

[36]  Sander Stuijk,et al.  A Novel Algorithm for Remote Photoplethysmography: Spatial Subspace Rotation , 2016, IEEE Transactions on Biomedical Engineering.

[37]  T. Ma,et al.  A Correlation Study on the Variabilities in Pulse Transit Time, Blood Pressure, and Heart Rate Recorded Simultaneously from Healthy Subjects , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[38]  David Fofi,et al.  Heart rate estimation using facial video: A review , 2017, Biomed. Signal Process. Control..

[39]  Genyue Fu,et al.  Transdermal Optical Imaging Reveal Basal Stress via Heart Rate Variability Analysis: A Novel Methodology Comparable to Electrocardiography , 2018, Front. Psychol..

[40]  Andreas Patzak,et al.  Continuous blood pressure measurement using pulse transit time , 2013, Somnologie - Schlafforschung und Schlafmedizin.

[41]  Zhiqi Shen,et al.  Video-based human heart rate measurement using joint blind source separation , 2017, Biomed. Signal Process. Control..