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首页> 外文期刊>npj Digital Medicine >Non-contact physiological monitoring of preterm infants in the Neonatal Intensive Care Unit
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Non-contact physiological monitoring of preterm infants in the Neonatal Intensive Care Unit

机译：新生儿重症监护单位的早产儿的非接触生理监测

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The implementation of video-based non-contact technologies to monitor the vital signs of preterm infants in the hospital presents several challenges, such as the detection of the presence or the absence of a patient in the video frame, robustness to changes in lighting conditions, automated identification of suitable time periods and regions of interest from which vital signs can be estimated. We carried out a clinical study to evaluate the accuracy and the proportion of time that heart rate and respiratory rate can be estimated from preterm infants using only a video camera in a clinical environment, without interfering with regular patient care. A total of 426.6 h of video and reference vital signs were recorded for 90 sessions from 30 preterm infants in the Neonatal Intensive Care Unit (NICU) of the John Radcliffe Hospital in Oxford. Each preterm infant was recorded under regular ambient light during daytime for up to four consecutive days. We developed multi-task deep learning algorithms to automatically segment skin areas and to estimate vital signs only when the infant was present in the field of view of the video camera and no clinical interventions were undertaken. We propose signal quality assessment algorithms for both heart rate and respiratory rate to discriminate between clinically acceptable and noisy signals. The mean absolute error between the reference and camera-derived heart rates was 2.3 beats/min for over 76% of the time for which the reference and camera data were valid. The mean absolute error between the reference and camera-derived respiratory rate was 3.5 breaths/min for over 82% of the time. Accurate estimates of heart rate and respiratory rate could be derived for at least 90% of the time, if gaps of up to 30 seconds with no estimates were allowed.
机译：实施基于视频的非联系技术来监测医院中早产儿的生命迹象表明了几种挑战，例如在视频帧中检测存在或患者的缺失，鲁棒性对照明条件的变化，可以估计有关时间段的自动识别合适的时间段和地区。我们进行了一个临床研究，以评估可从临床环境中仅使用摄像机的早产和呼吸速率的准确性和比例的比例，而不会干扰常规患者护理。在牛津约翰拉迪耶医院的新生儿重症监护室（Nicu）的30个早产儿，共记录了426.6小时的视频和参考生命体征。每天在白天在常规环境光线下记录每个早产儿，连续四天。我们开发了多项任务深度学习算法，以自动分割皮肤区域，并仅在摄像机的视野中存在时才能估计生命体征，并且没有进行临床干预措施。我们为心率和呼吸速率提出了信号质量评估算法，以区分临床可接受和嘈杂的信号。参考和相机衍生的心率之间的平均绝对误差为2.3次/分钟，超过76％的参考和相机数据有效的时间。参考和相机源呼吸速率之间的平均绝对误差为3.5呼吸/分钟，超过82％。如果不允许估计的差距最多30秒的时间，可以达到至少90％的心率和呼吸速率的准确估计。

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