Non-contact vital-sign monitoring of patients undergoing haemodialysis treatment

Mauricio Villarroel,David Meredith,Chris Pugh,Lionel Tarassenko,João Jorge,Sheera Sutherland

doi:10.1038/s41598-020-75152-z

Mauricio Villarroel, David Meredith + Show 4 more

Open Access

https://doi.org/10.1038/s41598-020-75152-z

Copy DOI

Journal: Scientific Reports	Publication Date: Oct 28, 2020
Citations: 18	License type: open-access

Affiliation: University of Oxford, National Health Service

Abstract

A clinical study was designed to record a wide range of physiological values from patients undergoing haemodialysis treatment in the Renal Unit of the Churchill Hospital in Oxford. Video was recorded for a total of 84 dialysis sessions from 40 patients during the course of 1 year, comprising an overall video recording time of approximately 304.1 h. Reference values were provided by two devices in regular clinical use. The mean absolute error between the heart rate estimates from the camera and the average from two reference pulse oximeters (positioned at the finger and earlobe) was 2.8 beats/min for over 65% of the time the patient was stable. The mean absolute error between the respiratory rate estimates from the camera and the reference values (computed from the Electrocardiogram and a thoracic expansion sensor—chest belt) was 2.1 breaths/min for over 69% of the time for which the reference signals were valid. To increase the robustness of the algorithms, novel methods were devised for cancelling out aliased frequency components caused by the artificial light sources in the hospital, using auto-regressive modelling and pole cancellation. Maps of the spatial distribution of heart rate and respiratory rate information were developed from the coefficients of the auto-regressive models. Most of the periods for which the camera could not produce a reliable heart rate estimate lasted under 3 min, thus opening the possibility to monitor heart rate continuously in a clinical environment.

Highlights

A clinical study was designed to record a wide range of physiological values from patients undergoing haemodialysis treatment in the Renal Unit of the Churchill Hospital in Oxford
Using only a standard colour video camera and ambient light as the main illumination source, we propose data fusion algorithms to compute estimates of heart rate and respiratory rate from patients diagnosed with End-Stage Renal Disease (ESRD) undergoing haemodialysis treatment
As of March 2019, out of the 40 patients enrolled in the study, 27 had passed away (67.5%), 4 patients received a kidney transplant (10%), 1 patient was transferred to another dialysis unit, 7 continued to receive haemodialysis treatment in the hospital (17.5%) and only 1 patient recovered

Summary

Introduction

A clinical study was designed to record a wide range of physiological values from patients undergoing haemodialysis treatment in the Renal Unit of the Churchill Hospital in Oxford. The standard vital signs monitored for patients in a hospital usually include temperature, heart rate (HR), respiratory rate (RR), blood pressure and, when appropriate, peripheral oxygen saturation ( SpO2 ). Most of the current work in video-based non-contact vital-sign monitoring has so far been performed over short time periods (typically up to a couple of minutes per recording) and under tightly controlled conditions with relatively still and healthy subjects. Using only a standard colour video camera and ambient light as the main illumination source, we propose data fusion algorithms to compute estimates of heart rate and respiratory rate from patients diagnosed with End-Stage Renal Disease (ESRD) undergoing haemodialysis treatment. Waste products and toxins are removed by the dialysis machine and the clean blood is returned to the body It can be carried out at home, haemodialysis is most often performed in a clinical centre. Patients usually lie on a bed or sit on a chair while connected to the dialysis machine[5]

Objectives

Results

Conclusion