[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82919-en":3,"doc-seo-82919-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},82919,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Video-based Detection of Cessation of Breathing in Pre-term Infants Using Machine Learning","Pre-term infants face harmful apnoea-related cessation of breathing driven by immature respiratory control, yet reliable NICU monitoring is difficult because motion artefacts, sensor displacement, and fragile skin can degrade contact measurements. This work tests whether camera-derived signals can detect COBE and complement routine physiological signals. From 30 infants’ video and clinical recordings, torso motion is tracked to form time-series features, modeled with ResNet and fused with IP, ECG-derived respiration, and PPG respiration. Video-only achieves balanced accuracy of 76.9%, while fusion with IP reaches 90.6%, improving robustness.","arXiv :2607 .05230v 1 [ cs .LG] 6 Jul 2026  \nVideo-based detection of cessation of breathing in pre-term infants using machine learning  \nDineo Serame 1 , Lionel Tarassenko 1 , and Mauricio Villarroel 1  \n1 Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK  \n* [mauricio.villarroel@eng.ox.ac.uk](mauricio.villarroel@eng.ox.ac.uk)  \nABSTRACT  \nPre-term infants are susceptible to potentially harmful apnoea-related cessations of breathing due to immature respiratory control mechanisms. However, reliable respiratory monitoring in this cohort remains challenging in the neonatal intensive care unit (NICU), where motion artefacts, sensor displacement, and skin fragility can compromise traditional contact-based measurements. Non-contact video monitoring offers a potential complementary modality that does not depend on adhesive sensors and may provide additional respiratory information.  \nWe evaluate whether camera-based signals can detect apnoea-related cessation of breathing (COBE) and whether they 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. Hybrid models subsequently combined video-derived signals with impedance pneumography (IP), ECG-derived respiration (EDR) and the PPG-derived respiratory envelope.  \nCamera-only models achieved a balanced accuracy of 76.9%, demonstrating the feasibility of non-contact detection of COBE events. Fusion of video-derived features with IP improved balanced accuracy to 90.6%, outperforming either modality alone and indicating that video-derived features contribute additional respiratory information beyond standard physiological signals.  \nThese findings show that video-derived signals contain clinically relevant respiratory features and can enhance COBE detection performance when combined with conventional physiological signals. This supports the use of non-contact video as a complementary modality for automated COBE detection and highlights its potential to improve the robustness of neonatal respiratory monitoring.  \nKeywords: Apnoea of prematurity; neonatal intensive care; video-based respiratory monitoring; non-contact monitoring; machine learning; multimodal data fusion.  \n1 Introduction  \nPre-term birth, defined as delivery before 37 weeks of gestation, remains a leading cause of neonatal morbidity and mortality worldwide 1–3. Respiratory complications such as apnoea, driven by immature respiratory control and lung development, are major contributors to mortality in this population. Apnoea of prematurity (AOP) affects up to 85% of infants born at or before 34 weeks of gestation4, 5. Episodes of cessation of breathing (COBE) are a defining feature of AOP and may result in hypoxaemia (reduced oxygen levels in the blood), bradycardia, neurological injury, and long-term developmental complications, including cognitive impairment and motor dysfunction6, 7. Accurate and reliable detection of COBE episodes is therefore critical for timely clinical intervention and improved outcomes.  \nAlthough polysomnography remains the gold standard for diagnosing apnoea8, 9 , it is impractical for continuous monitoring in the neonatal intensive care unit (NICU) due to its complexity, requirement for specialised equipment, and need for trained personnel. Routine respiratory monitoring and apnoea detection in the NICU rely primarily on contact-based techniques, including impedance pneumography (IP) and pulse oximetry 10, 11. Impedance pneumography uses chest electrodes to measure changes in transthoracic electrical impedance that occur due to cyclic changes in lung air volume during breathing 12, 13. These impedance variations provide an indirect measure of respiratory effort and are c","cbCairO5q5sTxIo5","https://ap.wps.com/l/cbCairO5q5sTxIo5","pdf",6176377,2,1,29,"English","en",105,"# Abstract\n# Introduction\n## Clinical problem: COBE in pre-term infants\n## Limitations of contact-based monitoring\n## Non-contact video monitoring as an alternative","[{\"question\":\"为什么在早产儿中监测“呼吸停止事件（COBE）”具有挑战？\",\"answer\":\"早产儿易发生与呼吸暂停相关的呼吸停止，但NICU中传统接触式监测会受到运动伪迹、传感器移位和皮肤脆弱等影响，从而降低可靠性。\"},{\"question\":\"文中如何从视频中提取用于检测COBE的信号？\",\"answer\":\"通过动态跟踪躯干区域的呼吸运动，从视频生成相机导出的时间序列信号特征，用于建立检测模型。\"},{\"question\":\"视频模型与常规生理信号融合后效果如何？\",\"answer\":\"单独使用视频信号的模型取得76.9%的平衡准确率；将视频特征与阻抗肺描记（IP）融合后提升到90.6%，优于任一单模态，说明视频提供了额外的呼吸信息。\"}]",1784183952,73,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"video-based-detection-of-cessation-of-breathing-in-pre-term-infants-using-machine-learning","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/video-based-detection-of-cessation-of-breathing-in-pre-term-infants-using-machine-learning/82919/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-24","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"为什么在早产儿中监测“呼吸停止事件（COBE）”具有挑战？","Question",{"text":75,"@type":76},"早产儿易发生与呼吸暂停相关的呼吸停止，但NICU中传统接触式监测会受到运动伪迹、传感器移位和皮肤脆弱等影响，从而降低可靠性。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"文中如何从视频中提取用于检测COBE的信号？",{"text":80,"@type":76},"通过动态跟踪躯干区域的呼吸运动，从视频生成相机导出的时间序列信号特征，用于建立检测模型。",{"name":82,"@type":73,"acceptedAnswer":83},"视频模型与常规生理信号融合后效果如何？",{"text":84,"@type":76},"单独使用视频信号的模型取得76.9%的平衡准确率；将视频特征与阻抗肺描记（IP）融合后提升到90.6%，优于任一单模态，说明视频提供了额外的呼吸信息。","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]