Respiratory rate estimation during triage of children in hospitals

Abstract

Accurate assessment of a child’s health is critical for appropriate allocation of medical resources and timely delivery of healthcare in Emergency Departments. The accurate measurement of vital signs is a key step in the determination of the severity of illness and respiratory rate is currently the most difficult vital sign to measure accurately. Several previous studies have attempted to extract respiratory rate from photoplethysmogram (PPG) recordings. However, the majority have been conducted in controlled settings using PPG recordings from healthy subjects. In many studies, manual selection of clean sections of PPG recordings was undertaken before assessing the accuracy of the signal processing algorithms developed. Such selection procedures are not appropriate in clinical settings. A major limitation of AR modelling, previously applied to respiratory rate estimation, is an appropriate selection of model order. This study developed a novel algorithm that automatically estimates respiratory rate from a median spectrum constructed applying multiple AR models to processed PPG segments acquired with pulse oximetry using a finger probe. Good-quality sections were identified using a dynamic template-matching technique to assess PPG signal quality. The algorithm was validated on 205 children presenting to the Emergency Department at the John Radcliffe Hospital, Oxford, UK, with reference respiratory rates up to 50 breaths per minute estimated by paediatric nurses. At the time of writing, the authors are not aware of any other study that has validated respiratory rate estimation using data collected from over 200 children in hospitals during routine triage.

• Autoregressive (AR) models
• Respiratory rate
• Photoplethysmogram (PPG)
• Pulse oximeters
• Paediatrics
• Vital signs

Acknowledgements

The author would like to acknowledge the Research Nurses in the Emergency Department at the John Radcliffe Hospital, Oxford (Sally Beer, Karen Warnes and Soubera Yousefi), who were funded by the NIHR Biomedical Research Centre, Oxford.

Declaration of interest

This paper presents independent research commissioned by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research funding scheme (RP-PG-0407-10347). The views expressed in this paper are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. Informed consent was obtained from the parents of children whose data was used in this work. The authors report no conflicts of interest.