Shah Syed Ahmar, Fleming Susannah, Thompson Matthew, Tarassenko Lionel
a Department of Engineering Science , Institute of Biomedical Engineering, University of Oxford , Oxford , UK .
b Nuffield Department of Primary Care Health Sciences , University of Oxford , Oxford , UK , and.
J Med Eng Technol. 2015;39(8):514-24. doi: 10.3109/03091902.2015.1105316.
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.
准确评估儿童健康状况对于急诊科合理分配医疗资源和及时提供医疗服务至关重要。准确测量生命体征是确定疾病严重程度的关键步骤，而呼吸频率是目前最难准确测量的生命体征。此前已有多项研究试图从光电容积脉搏波描记图（PPG）记录中提取呼吸频率。然而，大多数研究是在受控环境下使用健康受试者的PPG记录进行的。在许多研究中，在评估所开发的信号处理算法的准确性之前，会手动选择PPG记录的干净部分。这种选择程序在临床环境中并不适用。先前应用于呼吸频率估计的自回归（AR）建模的一个主要限制是模型阶数的适当选择。本研究开发了一种新算法，该算法通过对使用手指探头通过脉搏血氧饱和度仪获取的处理后的PPG段应用多个AR模型构建的中值频谱自动估计呼吸频率。使用动态模板匹配技术评估PPG信号质量，以识别高质量的部分。该算法在英国牛津约翰拉德克利夫医院急诊科就诊的205名儿童中进行了验证，儿科护士估计的参考呼吸频率高达每分钟50次呼吸。在撰写本文时，作者不知道有任何其他研究使用在医院常规分诊期间从200多名儿童收集的数据对呼吸频率估计进行了验证。
