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首页> 外文会议>Optical diagnostics and sensing XVIII: Toward Point-of-Care Diagnostics >Data fusion for improved camera-based detection of respiration in neonates
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Data fusion for improved camera-based detection of respiration in neonates

机译：数据融合可改善新生儿基于相机的呼吸检测

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Monitoring respiration during neonatal sleep is notoriously difficult due to the nonstationary nature of the signals and the presence of spurious noise. Current approaches rely on the use of adhesive sensors, which can damage the fragile skin of premature infants. Recently, non-contact methods using low-cost RGB cameras have been proposed to acquire this vital sign from (a) motion or (b) photoplethysmographic signals extracted from the video recordings. Recent developments in deep learning have yielded robust methods for subject detection in video data. In the analysis described here, we present a novel technique for combining respiratory information from high-level visual descriptors provided by a multi-task convolutional neural network. Using blind source separation, we find the combination of signals which best suppresses pulse and motion distortions and subsequently use this to extract a respiratory signal. Evaluation results were obtained from recordings on 5 neonatal patients nursed in the Neonatal Intensive Care Unit (NICU) at the John Radcliffe Hospital, Oxford, UK. We compared respiratory rates derived from this fused breathing signal against those measured using the gold standard provided by the attending clinical staff. We show that respiratory rate (RR) be accurately estimated over the entire range of respiratory frequencies.
机译：众所周知，由于信号的非平稳性质和伪噪声的存在，监测新生儿睡眠期间的呼吸非常困难。当前的方法依赖于粘性传感器的使用，这会损坏早产儿的脆弱皮肤。最近，已经提出了使用低成本RGB相机的非接触方法来从（a）运动或（b）从视频记录中提取的光电容积描记信号中获取该生命体征的方法。深度学习的最新发展为视频数据中的主题检测提供了可靠的方法。在这里描述的分析中，我们提出了一种新技术，用于组合来自由多任务卷积神经网络提供的高级视觉描述符的呼吸信息。使用盲源分离，我们找到了可以最好地抑制脉冲和运动失真的信号组合，随后使用它来提取呼吸信号。评估结果来自对英国牛津约翰·拉德克利夫医院新生儿重症监护病房（NICU）护理的5名新生儿的记录。我们将这种融合呼吸信号得出的呼吸频率与使用主治临床人员提供的黄金标准测得的呼吸频率进行了比较。我们显示在整个呼吸频率范围内可以准确估算呼吸频率（RR）。

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