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Respiratory rate estimation during triage of children in hospitals
Syed Ahmar Shah1, Susannah Fleming2, Matthew Thompson3
1a Department of Engineering Science , Institute of Biomedical Engineering, University of Oxford , Oxford , UK .
Insights
This study presents a novel algorithm for accurately estimating children's respiratory rate from pulse oximetry data. The method was validated in a hospital setting, improving emergency care assessments.
Area of Science:
- Biomedical Engineering
- Pediatric Emergency Medicine
- Signal Processing
Background:
- Accurate vital sign measurement is crucial for pediatric emergency care.
- Respiratory rate is a difficult yet critical vital sign to measure accurately in children.
- Previous methods for respiratory rate estimation from photoplethysmogram (PPG) signals often lack clinical applicability due to controlled settings and manual data selection.
Purpose of the Study:
- To develop and validate a novel, automated algorithm for estimating respiratory rate from PPG signals in pediatric emergency department patients.
- To overcome limitations of previous methods, including the need for manual data selection and appropriate model order selection in AR modeling.
- To assess the algorithm's performance in a real-world clinical setting with a large cohort of children.
Main Methods:
- Developed a novel algorithm using autoregressive (AR) modeling and median spectrum construction to estimate respiratory rate.
- Implemented a dynamic template-matching technique for automated identification of good-quality PPG signal segments.
- Validated the algorithm on PPG data from 205 children in an Emergency Department, comparing estimates to nurse-assessed respiratory rates (up to 50 breaths/min).
Main Results:
- The novel algorithm successfully estimated respiratory rate from processed PPG segments.
- The dynamic template-matching technique effectively identified usable PPG signal sections in a clinical environment.
- This study represents one of the largest validations of PPG-based respiratory rate estimation in hospitalized children during routine triage.
Conclusions:
- The developed algorithm provides a promising, automated approach for accurate respiratory rate estimation in pediatric emergency settings.
- The method's ability to handle real-world clinical data without manual selection enhances its practical utility.
- This advancement has the potential to improve the accuracy of illness severity assessment and resource allocation in emergency departments.
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.
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