Video-Based Respiratory Rate Estimation for Infants in the NICU

IF 3.7 3区 医学 Q2 ENGINEERING, BIOMEDICAL IEEE Journal of Translational Engineering in Health and Medicine-Jtehm Pub Date : 2024-10-30 DOI:10.1109/JTEHM.2024.3488523
Soodeh Ahani;Nikoo Niknafs;Pascal M. Lavoie;Liisa Holsti;Guy A. Dumont
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Abstract

Objective: Non-contact respiratory rate estimation (RR) is highly desirable for infants because of their sensitive skin. We propose a novel RGB video-based RR estimation method for infants in the neonatal intensive care unit (NICU) that can accurately measure the RR contact-less.Methods and Procedures: We utilize Eulerian video magnification (EVM) method and develop an adaptive peak prominence threshold value estimation method to address challenges of RR estimation (e.g., dark environments, shallow breathing, babies swaddled or under blankets). We recruited 13 infants recorded for 4 consecutive hours per case. We then evaluate the performance of the algorithm for several (i.e., 19 to 25) randomly selected videos, each lasting 1 minute, for each case.Results: Intraclass correlation coefficients of the proposed method over manually and automatically selected ROIs are 0.91 (95%CI: $0.89-0.93$ ) and 0.88 (95%CI: $0.85-0.9$ ), indicating excellent and good reliability, respectively. The Bland-Altman analysis of the proposed algorithm shows higher agreement between the estimated values via the proposed method and visually counted RR than the agreement between the RR obtained from the impedance sensors and reference RR, and agreement between a former EVM-based method and reference RR values.Conclusion: Our algorithm shows promising results for RR estimation in a real-life NICU environment under various conditions that can confound the estimation.Clinical impact: We present a robust algorithm for non-contact neonatal respiratory rate monitoring, capable of performing well under various environmental lighting conditions in NICU, even when the infant is clothed or covered.
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通过视频估算新生儿重症监护室婴儿的呼吸频率
目的:由于婴儿皮肤敏感,因此非接触式呼吸频率估计(RR)是非常理想的。我们为新生儿重症监护室(NICU)中的婴儿提出了一种基于 RGB 视频的新型呼吸频率估计方法,该方法可以准确测量非接触式呼吸频率:我们利用欧拉视频放大(EVM)方法,并开发了一种自适应峰值突出阈值估计方法,以解决RR估计的难题(如黑暗环境、浅呼吸、婴儿襁褓或毯子下)。我们招募了 13 名婴儿,每例连续记录 4 小时。然后,我们对每个病例随机选择的几个(即 19 到 25 个)视频(每个视频持续 1 分钟)进行了算法性能评估:在人工和自动选择的 ROI 上,拟议方法的类内相关系数分别为 0.91(95%CI:0.89-0.93 美元)和 0.88(95%CI:0.85-0.9 美元),表明可靠性极佳和良好。对所提算法的 Bland-Altman 分析表明,所提方法的估计值与目测计数的 RR 之间的一致性高于阻抗传感器获得的 RR 与参考 RR 之间的一致性,也高于以前基于 EVM 的方法与参考 RR 值之间的一致性:结论:我们的算法显示了在现实生活中的新生儿重症监护室环境中,在各种可能干扰估计的条件下进行 RR 估计的良好结果:临床影响:我们提出了一种用于非接触式新生儿呼吸频率监测的稳健算法,该算法能够在新生儿重症监护室的各种环境光线条件下表现良好,即使婴儿穿着衣服或盖着被子也不例外。
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来源期刊
CiteScore
7.40
自引率
2.90%
发文量
65
审稿时长
27 weeks
期刊介绍: The IEEE Journal of Translational Engineering in Health and Medicine is an open access product that bridges the engineering and clinical worlds, focusing on detailed descriptions of advanced technical solutions to a clinical need along with clinical results and healthcare relevance. The journal provides a platform for state-of-the-art technology directions in the interdisciplinary field of biomedical engineering, embracing engineering, life sciences and medicine. A unique aspect of the journal is its ability to foster a collaboration between physicians and engineers for presenting broad and compelling real world technological and engineering solutions that can be implemented in the interest of improving quality of patient care and treatment outcomes, thereby reducing costs and improving efficiency. The journal provides an active forum for clinical research and relevant state-of the-art technology for members of all the IEEE societies that have an interest in biomedical engineering as well as reaching out directly to physicians and the medical community through the American Medical Association (AMA) and other clinical societies. The scope of the journal includes, but is not limited, to topics on: Medical devices, healthcare delivery systems, global healthcare initiatives, and ICT based services; Technological relevance to healthcare cost reduction; Technology affecting healthcare management, decision-making, and policy; Advanced technical work that is applied to solving specific clinical needs.
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