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.

中文翻译：

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新生儿重症监护室的早产儿的非接触式生理监测。

实施基于视频的非接触式技术来监视医院中早产儿的生命体征提出了一些挑战，例如检测视频帧中是否存在患者，对照明条件变化的鲁棒性，自动识别合适的时间段和感兴趣的区域，从中可以估计生命体征。我们进行了一项临床研究，以评估在临床环境中仅使用摄像机即可从早产儿估算出心率和呼吸频率的准确性和时间比例，而不会干扰常规患者的护理。在牛津约翰·拉德克利夫医院的新生儿重症监护病房（NICU），从30个早产儿共记录了426.6小时的视频和参考生命体征，共90个疗程。每天在连续的四天之内，在正常的环境光下记录每个早产婴儿。我们开发了多任务深度学习算法，以自动分割皮肤区域并仅当婴儿出现在摄像机视野中且未进行临床干预时才估计生命体征。我们提出了针对心率和呼吸率的信号质量评估算法，以区分临床上可接受的信号和嘈杂的信号。在参考和相机数据有效的超过76％的时间中，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。在超过82％的时间中，参考呼吸频率和相机呼吸频率之间的平均绝对误差为3.5呼吸/分钟。