Abstract
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.
Highlights
It has been established that it is possible to record the blood volume changes associated with the cardiac cycle remotely from facial images of human subjects with a digital video camera up to 2 m away from the subject, using only ambient light as the light source
Our methods for non-contact vital sign monitoring using AR models are described: we introduce a novel pole-cancellation algorithm for the removal of aliased frequencies; we describe how heart rate, respiratory rate and changes in peripheral oxygen saturation can be estimated from video images of patients undergoing dialysis in the local renal unit
The camera substudy was part of a clinical study (Research Ethics Committee reference number 11/SC/0207) in which vital signs were recorded with conventional patient monitors, the aim being to understand the changes in physiological variables which occur during dialysis (Borhani et al 2010)
Summary
It has been established that it is possible to record the blood volume changes associated with the cardiac cycle remotely from facial images of human subjects with a digital video camera up to 2 m away from the subject, using only ambient light as the light source. This ability to record photoplethysmographic signals remotely opens up the possibility of non-contact vital sign monitoring. Conventional patient monitoring using pulse oximetry to measure heart rate and peripheral arterial oxygen saturation (SpO2) requires a probe to be attached to the patient’s ear or finger. The same non-contact monitoring technology can be used to obtain estimates of respiratory rate, another parameter of major clinical significance (Cretikos et al 2008)
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