Non-contact video-based vital sign monitoring using ambient light and auto-regressive models

By Lionel Tarassenko, M. Villarroel, Alessandro Guazzi, João Jorge, David A. Clifton, Chris W. Pugh. Physiological Measurement, 2014

428 citations recorded by the source corpus.

Remote sensing of the reflectance photoplethysmogram using a video camera typically positioned 1 m away from the patient’s face is a promising method for monitoring the vital signs of patients without attaching any electrodes or sensors to them. Most of the papers in the literature on non-contact vital sign monitoring report results on human volunteers in controlled environments. We have been able to obtain estimates of heart rate and respiratory rate and preliminary results on changes in oxygen saturation from double-monitored patients undergoing haemodialysis in the Oxford Kidney Unit. To achieve this, we have devised a novel method of cancelling out aliased frequency components caused by artificial light flicker, using auto-regressive (AR) modelling and pole cancellation. Secondly, we have been able to construct accurate maps of the spatial distribution of heart rate and respiratory rate information from the coefficients of the AR model. In stable sections with minimal patient motion, the mean absolute error between the camera-derived estimate of heart rate and the reference value from a pulse oximeter is similar to the mean absolute error between two pulse oximeter measurements at different sites (finger and earlobe). The activities of daily living affect the respiratory rate, but the camera-derived estimates of this parameter are at least as accurate as those derived from a thoracic expansion sensor (chest belt). During a period of obstructive sleep apnoea, we tracked changes in oxygen saturation using the ratio of normalized reflectance changes in two colour channels (red and blue), but this required calibration against the reference data from a pulse oximeter.

Subfields: Biomedical Engineering, Surgery

View Paper: https://www.semanticscholar.org/paper/77da55ffaf938c192dcd4f6576d2c1de2c8c996f

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In Biomedical Engineering

In Surgery

Daniel J. McDuff

Citations in the corpus

Wearable Photoplethysmographic Sensors—Past and Present (2014, background): “The green signal was found to provide the strongest plethysmographic signal among camera RGB signals [11,12].”
Algorithmic Principles of Remote PPG (2017, background): “ing, such as the monitoring of heart rate [7]–[11], respiration [8], SpO2 [8], [12], blood pressure [13], neonates [14], [15], and the detection of atrial fibrillation [16] and mental”
A Novel Algorithm for Remote Photoplethysmography: Spatial Subspace Rotation (2016, background): “More recently, they also lead to further advances in vision-based intelligent systems, such as vital signs monitoring [13], living subject detection [14], facial-expression analysis [15], mental-stress detection [16], etc.”
Remote patient monitoring: a comprehensive study (2017, methodology): “A non-contact video based method is used to obtain vital signs, heart rate and respiration rate of patients undergoing haemodialysis (Tarassenko et al. 2014).”
Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review (2017, neutral): “Heart rate and SpO2 can also be estimated from imaging-PPG signals, increasing their utility [25], [27].”
DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks (2018, background): “[31], the CAN still shows improvement in five tasks and overall.”
Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement (2020, methodology): “Respiratory signals (breathing) can also be recovered from motion-based analyses of the head and torso [13].”