用摄像头检测早产儿呼吸暂停，无需贴传感器。
Video-based detection of cessation of breathing in pre-term infants using machine learning
- 从婴儿胸部运动视频提取呼吸信号，用ResNet模型识别呼吸停止。
- 纯视频模型准确率达76.9%，融合生理信号后提升至90.6%。
- 适合新生儿监护室医生和智能医疗研发者参考。
早产儿因呼吸控制不成熟，易发生危及生命的呼吸暂停。但新生儿重症监护室（NICU）中，接触式监测常受运动伪影、传感器移位和皮肤脆弱影响。非接触式视频监测不依赖粘贴传感器，可提供额外呼吸信息。本研究基于30名早产儿的视频与临床数据，通过动态追踪躯干区域提取呼吸运动，生成摄像头衍生的时间序列信号。仅使用视频的模型采用残差网络（ResNet）架构训练，混合模型则结合视频信号与阻抗气动图（IP）、心电图衍生呼吸（EDR）及脉搏波导出呼吸包络（PPG-derived respiratory envelope）。纯视频模型平衡准确率达76.9%，证明非接触式呼吸暂停检测可行；融合视频信号与IP后，平衡准确率提升至90.6%，优于任一单一模态，表明视频包含超越常规生理信号的呼吸信息。结果证实视频信号含临床相关呼吸特征，与传统信号结合可增强呼吸暂停检测能力，支持视频作为自动呼吸暂停检测的互补手段，有望提升新生儿呼吸监测的鲁棒性。
原文摘要 · Abstract (English)
Pre-term infants are susceptible to potentially harmful apnoea-related cessations of breathing due to immature respiratory control. However, reliable respiratory monitoring in the neonatal intensive care unit (NICU) remains challenging because motion artefacts, sensor displacement, and skin fragility can compromise contact-based measurements. Non-contact video monitoring offers a complementary approach that does not depend on adhesive sensors while providing additional respiratory information. We investigated whether camera-based signals can detect apnoea-related cessation of breathing (COBE) and provide complementary information to routinely acquired physiological signals. Using video and clinical recordings from 30 pre-term infants, respiratory motion was extracted from dynamically tracked torso regions to generate camera-derived time-series signals. Camera-only models were trained using residual network (ResNet) architectures, while hybrid models combined video-derived signals with impedance pneumography (IP), ECG-derived respiration (EDR), and the PPG-derived respiratory envelope. Camera-only models achieved a balanced accuracy of 76.9%, demonstrating the feasibility of non-contact COBE detection. Combining video-derived features with IP improved balanced accuracy to 90.6%, outperforming either modality alone and indicating that video provides respiratory information beyond standard physiological signals. These findings show that video-derived signals contain clinically relevant respiratory features and enhance COBE detection when combined with conventional physiological signals. This supports non-contact video as a complementary modality for automated COBE detection and highlights its potential to improve the robustness of neonatal respiratory monitoring.
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