Contactless Technique for Measuring Blood-Pressure Variability from One Region in Video Plethysmography

Sugita, Norihiro; Yoshizawa, Makoto; Abe, Makoto; Tanaka, Akira; Homma, Noriyasu; Yambe, Tomoyuki

doi:10.1007/s40846-018-0388-8

Cited by 42 publications

(29 citation statements)

References 39 publications

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“…Video Plethysmogram (VPG) is a proven method for BP acquisition in contactless and non-invasive measurements using video analysis as the medium [71], [72]. This technique has potential to be used in remote health monitoring in hospitals, elderly homes, workplaces, and with drivers to detect anomalies in health conditions [72]. Similar to PPG, VPG monitors BP from the reaction of light on the ROI.…”

Section: ) Video Plethysmogrammentioning

confidence: 99%

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Continuous Non-Invasive Blood Pressure Monitoring: A Methodological Review on Measurement Techniques

Ellington

Lee

et al. 2020

IEEE Access

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Continuous measurement of blood pressure is crucial to the assessment of many medical conditions. However, the current clinical gold standard involving an arterial catheter, occluding cuff, and other invasive procedures are performed in hospital settings while home-based devices can provide only intermittent measurement and are not as reliable. Therefore, there is a significant need for continuous noninvasive blood pressure (cNIBP) monitoring in the daily life. Pulse transit time (PTT)/pulse arrival time (PAT)-based blood pressure measurement has proven its potential to address this need. In this article, we present state-of-the-art devices and recent literature related to measurement technologies used in PTT/PAT-based methods for cNIBP monitoring. Various physiological signals which could be used to enable cNIBP in the home setting are categorized into two groups (i.e., proximal waveforms and distal waveforms) and are thoroughly discussed and compared. Given insightful analysis of these waveforms, we highlight their combinations to derive PTT/PAT values for BP measurement then discuss challenges presented from the cuffless and PTT/PAT-based nature of these devices. Finally, we conclude with future directions needed for home-based cNIBP adaptation and present societal broader impacts.

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Section: ) Video Plethysmogrammentioning

confidence: 99%

“…1). Thus, recording and analyzing the green channel of the video frames reflect the change of blood present [72]. This method is prone to noise when the user is in motion or the camera used does not meet the proper requirements.…”

Section: ) Video Plethysmogrammentioning

confidence: 99%

Continuous Non-Invasive Blood Pressure Monitoring: A Methodological Review on Measurement Techniques

Ellington

Lee

et al. 2020

IEEE Access

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“…Others have focused on specific concepts such as the Eulerian video magnification (EMV) 84 , 130 , the exploitation of the ballistocardiographic effect 34 , the derivation of signal quality indices 131 , or a performance evaluation of several existing methods on a publicly available dataset 37 . Yet other works deal with specific aspects that reach beyond the basic vital signs such as the estimation ofblood pressure variability 132 , pulse wave delay 133 , the jugular venous pulse waveform 134 , and venous oxygen saturation 135 .…”

Section: Survey Taxonomymentioning

confidence: 99%

A Broader Look: Camera-Based Vital Sign Estimation across the Spectrum

Antink

Lyra²,

Paul

et al. 2019

Yearb Med Inform

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Objectives: Camera-based vital sign estimation allows the contactless assessment of important physiological parameters. Seminal contributions were made in the 1930s, 1980s, and 2000s, and the speed of development seems ever increasing. In this suivey, we aim to overview the most recent works in this area, describe their common features as well as shortcomings, and highlight interesting “outliers”. Methods: We performed a comprehensive literature research and quantitative analysis of papers published between 2016 and 2018. Quantitative information about the number of subjects, studies with healthy volunteers vs. pathological conditions, public datasets, laboratory vs. real-world works, types of camera, usage of machine learning, and spectral properties of data was extracted. Moreover, a qualitative analysis of illumination used and recent advantages in terms of algorithmic developments was also performed. Results: Since 2016, 116 papers were published on camera-based vital sign estimation and 59% of papers presented results on 20 or fewer subjects. While the average number of participants increased from 15.7 in 2016 to 22.9 in 2018, the vast majority of papers (n=100) were on healthy subjects. Four public datasets were used in 10 publications. We found 27 papers whose application scenario could be considered a real-world use case, such as monitoring during exercise or driving. These include 16 papers that dealt with non-healthy subjects. The majority of papers (n=61) presented results based on visual, red-green-blue (RGB) information, followed by RGB combined with other parts of the electromagnetic spectrum (n=18), and thermography only (n=12), while other works (n=25) used other mono- or polychromatic non-RGB data. Surprisingly, a minority of publications (n=39) made use of consumer-grade equipment. Lighting conditions were primarily uncontrolled or ambient. While some works focused on specialized aspects such as the removal of vital sign information from video streams to protect privacy or the influence of video compression, most algorithmic developments were related to three areas: region of interest selection, tracking, or extraction of a one-dimensional signal. Seven papers used deep learning techniques, 17 papers used other machine learning approaches, and 92 made no explicit use of machine learning. Conclusion: Although some general trends and frequent shortcomings are obvious, the spectrum of publications related to camera-based vital sign estimation is broad. While many creative solutions and unique approaches exist, the lack of standardization hinders comparability of these techniques and of their performance. We believe that sharing algorithms and/ or datasets will alleviate this and would allow the application of newer techniques such as deep learning.

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“…The physiological signals are recorded with a contact sensor (for measuring wrist signal) and a camera (for measuring face signal), both sensors being embedded in a smartwatch. From [39] Recent studies have demonstrated that PTT can be measured unobtrusively from PPG signals taken at different body sites (Fig.6), like wrist and face using a smartwatch [39] or hand and face using a single video camera [40]. Junior et al [41] have used smartphone camera and microphone to measure PTT: the moment the blood leaves the heart is detected by recording the heart sound with the microphone while the camera is employed to detect the time the blood reaches the finger by analyzing the PPG pulse wave.…”

Section: Blood Pressurementioning

confidence: 99%

Remote sensing of vital signs and biomedical parameters: A review

Bousefsaf¹,

Maaoui²,

Pruski³

2018

MMC_C

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According to the World Health Organization, cardiovascular diseases correspond to the prime cause of death globally. Several technologies are employed to measure vital signs remotely. For instance, webcams correspond to ubiquitous systems that can be used to detect cardiovascular pathologies by sensing important physiological parameters like pulse rate. A review of technologies and methods used to remotely measure vital signs and biomedical parameters is proposed in this article. Remote sensing of physiological parameters concerns every person: from healthy, ill or hospitalized persons to people with disabilities or with reduced autonomy.

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Contactless Technique for Measuring Blood-Pressure Variability from One Region in Video Plethysmography

Cited by 42 publications

References 39 publications

Continuous Non-Invasive Blood Pressure Monitoring: A Methodological Review on Measurement Techniques

Continuous Non-Invasive Blood Pressure Monitoring: A Methodological Review on Measurement Techniques

A Broader Look: Camera-Based Vital Sign Estimation across the Spectrum

Remote sensing of vital signs and biomedical parameters: A review

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