Photoplethysmography (PPG) technology has been used to develop small, wearable, pulse rate sensors. These devices, consisting of infrared light-emitting diodes (LEDs) and photodetectors, offer a simple, reliable, low-cost means of monitoring the pulse rate noninvasively. Recent advances in optical technology have facilitated the use of high-intensity green LEDs for PPG, increasing the adoption of this measurement technique. In this review, we briefly present the history of PPG and recent developments in wearable pulse rate sensors with green LEDs. The application of wearable pulse rate monitors is discussed.

Wearable Photoplethysmographic Sensors—Past and Present

This paper introduces a mathematical model that incorporates the pertinent optical and physiological properties of skin reflections with the objective to increase our understanding of the algorithmic principles behind remote photoplethysmography (rPPG). The model is used to explain the different choices that were made in existing rPPG methods for pulse extraction. The understanding that comes from the model can be used to design robust or application-specific rPPG solutions. We illustrate this by designing an alternative rPPG method, where a projection plane orthogonal to the skin tone is used for pulse extraction. A large benchmark on the various discussed rPPG methods shows that their relative merits can indeed be understood from the proposed model.

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Algorithmic Principles of Remote PPG

Photoplethysmography (PPG) is a noninvasive optical technique for detecting microvascular blood volume changes in tissues. Its ease of use, low cost and convenience make it an attractive area of research in the biomedical and clinical communities. Nevertheless, its single spot monitoring and the need to apply a PPG sensor directly to the skin limit its practicality in situations such as perfusion mapping and healing assessments or when free movement is required. The introduction of fast digital cameras into clinical imaging monitoring and diagnosis systems, the desire to reduce the physical restrictions, and the possible new insights that might come from perfusion imaging and mapping inspired the evolution of the conventional PPG technology to imaging PPG (IPPG). IPPG is a noncontact method that can detect heart-generated pulse waves by means of peripheral blood perfusion measurements. Since its inception, IPPG has attracted significant public interest and provided opportunities to improve personal healthcare. This study presents an overview of the wide range of IPPG systems currently being introduced along with examples of their application in various physiological assessments. We believe that the widespread acceptance of IPPG is happening, and it will dramatically accelerate the promotion of this healthcare model in the near future.

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Photoplethysmography Revisited: From Contact to Noncontact, From Point to Imaging

Soleil et peau

Healthcare is a field that is rapidly developing in technology and services. A recent development in this area is remote monitoring of patients which has many advantages in a fast aging world population with increasing health complications. With relatively simple applications to monitor patients inside hospital rooms, the technology has developed to the extent that the patient can be allowed normal daily activities at home while still being monitored with the use of modern communication and sensor technologies. Sensors for monitoring essential vital signs such as electrocardiogram reading, heart rate, respiration rate, blood pressure, temperature, blood glucose levels and neural system activity are available today. Range of remote healthcare varies from monitoring chronically ill patients, elders, premature children to victims of accidents. These new technologies can monitor patients based on the illness or based on the situation. The technology varies from sensors attached to body to ambient sensors attached to the environment and new breakthroughs show contactless monitoring which requires only the patient to be present within a few meters from the sensor. Fall detection systems and applications to monitor chronical ill patients have already become familiar to many. This study provides a review of the recent advances in remote healthcare and monitoring in both with-contact and contactless methods. With the review, the authors discuss some issues available in most systems. The paper also includes some directions for future research.

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Remote patient monitoring: a comprehensive study

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.

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

Current technologies to allow continuous monitoring of vital signs in pre-term infants in the hospital require adhesive electrodes or sensors to be in direct contact with the patient. These can cause stress, pain, and also damage the fragile skin of the infants. It has been established previously that the colour and volume changes in superficial blood vessels during the cardiac cycle can be measured using a digital video camera and ambient light, making it possible to obtain estimates of heart rate or breathing rate. Most of the papers in the literature on non-contact vital sign monitoring report results on adult healthy human volunteers in controlled environments for short periods of time. The authors' current clinical study involves the continuous monitoring of pre-term infants, for at least four consecutive days each, in the high-dependency care area of the Neonatal Intensive Care Unit (NICU) at the John Radcliffe Hospital in Oxford. The authors have further developed their video-based, non-contact monitoring methods to obtain continuous estimates of heart rate, respiratory rate and oxygen saturation for infants nursed in incubators. In this Letter, it is shown that continuous estimates of these three parameters can be computed with an accuracy which is clinically useful. During stable sections with minimal infant motion, the mean absolute error between the camera-derived estimates of heart rate and the reference value derived from the ECG is similar to the mean absolute error between the ECG-derived value and the heart rate value from a pulse oximeter. Continuous non-contact vital sign monitoring in the NICU using ambient light is feasible, and the authors have shown that clinically important events such as a bradycardia accompanied by a major desaturation can be identified with their algorithms for processing the video signal.

Continuous non-contact vital sign monitoring in neonatal intensive care unit

A novel method (Sophia) is presented to track oxygen saturation changes in a controlled environment using an RGB camera placed approximately 1.5 m away from the subject. The method is evaluated on five healthy volunteers (Fitzpatrick skin phenotypes II, III, and IV) whose oxygen saturations were varied between 80% and 100% in a purpose-built chamber over 40 minutes each. The method carefully selects regions of interest (ROI) in the camera image by calculating signal-to-noise ratios for each ROI. This allows it to track changes in oxygen saturation accurately with respect to a conventional pulse oximeter (median coefficient of determination, 0.85).

Non-contact measurement of oxygen saturation with an RGB camera

An abnormal respiratory rhythm is an early indicator of physiological deterioration. It is of critical importance in the clinical management of critically-ill or premature infants, for whom apnoea of prematurity is a major concern. Nevertheless, respiratory signals are still largely disregarded in neonatal intensive care units due to the high prevalence of noise and high false alarm rates in conventional monitoring. To address this, we present a novel method for the extraction of respiration from camera-based measurements taken from the top-view of an incubator. A total of 107 events from 30 neonatal admissions were annotated by three clinical reviewers as either true cessations of breathing (physiologically relevant) or false (artefact-related). The events were divided into two independent groups for training and validation and our algorithm was trained to classify true cessations. We achieved a good classification performance with 9 out of 10 cessations and 7 out of 10 artefactual events correctly identified in the training set, and with 7 out of 10 cessations and 34 out of 44 artefactual events correctly identified in the out-of-sample test set. A reduction in false alarm rate of 77.3% was achieved.

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Non-Contact Monitoring of Respiration in the Neonatal Intensive Care Unit

Autoregressive modelling is used to identify periodic physiological signals such as heart rate or breathing rate in an image of a subject. The colour channels of a video signal are windowed and normalised by dividing each signal by its mean. The ratios of the normalised channels to each other are found and principal component analyses conducted on the ratio signals. The most periodic of the principal components is selected and autoregressive models of one or more different orders are fitted to the selected component. Poles of the fitted autoregressive models of different orders are taken and pure sinusoids corresponding to the frequency of each pole are generated and their cross-correlation with the original component is found. Whichever pole corresponds to the sinusoid with the maximum cross-correlation is selected as the best estimate of the frequency of periodic physiological information in the original video signal. The method may be used in a patient monitor or in a webcam-enabled device such as a tablet computer or smart phone.

Alessandro Guazzi

Papers

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

Continuous non-contact vital sign monitoring in neonatal intensive care unit

Non-contact measurement of oxygen saturation with an RGB camera

Non-Contact Monitoring of Respiration in the Neonatal Intensive Care Unit

Method and apparatus for physiological monitoring