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基于视频的儿童肺炎呼吸频率和胸部凹陷的非接触式监测。

Video based non-contact monitoring of respiratory rate and chest indrawing in children with pneumonia.

机构信息

Department of Biomedical Engineering, Military Institute of Science and Technology, Dhaka 1216, Bangladesh.

Department of Biomedical Physics and Technology, University of Dhaka, Dhaka 1000, Bangladesh.

出版信息

Physiol Meas. 2021 Nov 26;42(10). doi: 10.1088/1361-6579/ac34eb.

Abstract

Pneumonia is the single largest cause of death in children worldwide due to infectious diseases. According to WHO guidelines, fast breathing and chest indrawing are the key indicators of pneumonia in children requiring antibiotic treatments. The aim of this study was to develop a video based novel method for simultaneous monitoring of respiratory rate and chest indrawing without upsetting babies.Respiratory signals, corresponding to periodic movements of chest-abdominal walls during breathing, were extracted by analyzing RGB (red, green, blue) components in video frames captured by a smartphone camera. Respiratory rate was then obtained by applying fast Fourier transform on the de-noised respiratory signal. Chest indrawing was detected by analysing relative phases of regional chest-abdominal wall mobility. The performance of the developed algorithm was evaluated on both healthy and pneumonia children.The proposed method can measure respiratory rate with an overall mean absolute error of 1.8 bpm in the range 18-105 bpm. Phase difference between regional chest wall movements in the chest indrawing (pneumonia) cases was found to be 143 ± 23.9 degrees, which was significantly higher than that in the healthy cases 52.3 ± 32.6 degrees (< 0.001).Being non-intrusive and non-subjective, this computer-aided method can be useful in the monitoring for respiratory rate and chest indrawing for the diagnosis of pneumonia and its severity in children.

摘要

肺炎是全球范围内因传染病导致儿童死亡的首要原因。根据世界卫生组织的指导方针,呼吸急促和胸凹陷是儿童肺炎需要抗生素治疗的关键指标。本研究旨在开发一种基于视频的新方法,在不干扰婴儿的情况下同时监测呼吸频率和胸凹陷。通过分析智能手机摄像头拍摄的视频帧中的 RGB(红、绿、蓝)分量来提取与呼吸时胸腹壁周期性运动相对应的呼吸信号。然后通过对去噪呼吸信号进行快速傅里叶变换来获得呼吸频率。通过分析区域胸腹壁运动的相对相位来检测胸凹陷。该算法的性能在健康和肺炎儿童上进行了评估。该方法可在 18-105 bpm 的范围内测量呼吸率,平均绝对误差为 1.8 bpm。在胸凹陷(肺炎)病例中,区域性胸壁运动之间的相位差为 143±23.9 度,明显高于健康病例中的 52.3±32.6 度(<0.001)。作为一种非侵入性和非主观的方法,这种计算机辅助方法可用于监测呼吸频率和胸凹陷,以诊断肺炎及其严重程度。

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