Your browser doesn't support javascript.
loading
Non-contact vital-sign monitoring of patients undergoing haemodialysis treatment.
Villarroel, Mauricio; Jorge, João; Meredith, David; Sutherland, Sheera; Pugh, Chris; Tarassenko, Lionel.
Afiliação
  • Villarroel M; Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, UK. mauricio.villarroel@eng.ox.ac.uk.
  • Jorge J; Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, UK.
  • Meredith D; Oxford Kidney Unit, Oxford University Hospitals National Health Service Trust, Oxford, UK.
  • Sutherland S; Oxford Kidney Unit, Oxford University Hospitals National Health Service Trust, Oxford, UK.
  • Pugh C; Nuffield Department of Medicine, University of Oxford, Oxford, UK.
  • Tarassenko L; Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, UK.
Sci Rep ; 10(1): 18529, 2020 10 28.
Article em En | MEDLINE | ID: mdl-33116150
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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Sinais Vitais / Monitorização Fisiológica Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Sinais Vitais / Monitorização Fisiológica Idioma: En Ano de publicação: 2020 Tipo de documento: Article