[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125089-en":3,"doc-seo-125089-105":30,"detail-sidebar-cat-0-en-105":95},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125089,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Explainable Machine Learning for Central Apnea Detection in Premature Infants","Diagnosis for apnea of prematurity relies on detecting central apneas in respiratory traces, yet clinical alarm systems produce many false positives. Prior work showed that an explainable CA detection model using elastic net logistic regression with features from physiological signals improved precision. This study evaluates additional explainable algorithms—SVM, XGBoost, and KNN—trained on a small dataset of 10 premature infants with leave-one-patient-out cross-validation. XGBoost yields the most promising model with fewer false alarms while maintaining competitive AUROC. Feature patterns remain consistent and useful for distinguishing central apneas from stable periods, potentially reducing nurse workload.","Explainable machine learning for central apnea detection in premature infants  \nCitation for published version (APA):  \nVarisco, G. , Peng, Z. , Andriessen, P. , van Pul, C. , & Long, X. (2024) . Explainable machine learning for central apnea detection in premature infants. In 2024 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2024 Article 10596705 Institute of Electrical and Electronics Engineers.  \n[https://doi.org/10.1109/MeMeA60663.2024.10596705](https://doi.org/10.1109/MeMeA60663.2024.10596705)  \nDocument license:  \nTAVERNE  \nDOI:  \n10.1109/MeMeA60663.2024.10596705  \nDocument status and date:  \nPublished: 29/07/2024  \nDocument Version:  \nPublisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)  \nPlease check the document version of this publication:  \n• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.  \n• The final author version and the galley proof are versions of the publication after peer review.  \n• The final published version features the final layout of the paper including the volume, issue and page numbers.  \nLink to publication  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal.  \nIf the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, please follow below link for the End User Agreement:  \n[www.tue.nl/taverne](www.tue.nl/taverne)  \nTake down policy  \nIf you believe that this document breaches copyright please contact us at:  \n[openaccess@tue.nl](openaccess@tue.nl)  \nproviding details and we will investigate your claim.  \nDownload date: 01. Feb. 2025  \n2024 IEEE International Symposium on Medical Measurements and Applications (MeMeA) ©2024 IEEE DOI: 10.1109/MEMEA60663.2024.10596705| 979-8-3503-0799-3/24/$31.00 |   \nExplainable Machine Learning for Central Apnea Detection in Premature Infants  \nGabriele Varisco  \nElectrical Engineering Eindhoven University of Technology Eindhoven, Netherlands Gynecology Ma`xima Medical Center Veldhoven, Netherlands [g.varisco@tue.nl](g.varisco@tue.nl)  \nZheng Peng  \nApplied Physics  \nEindhoven University of Technology Eindhoven, Netherlands Clinical Physics Ma`xima Medical Center Veldhoven, Netherlands  \nPeter Andriessen  \nPediatrics Ma`xima Medical Center Veldhoven, Netherlands Applied Physics  \nEindhoven University of Technology Eindhoven, Netherlands  \nCarola van Pul  \nClinical Physics Ma`xima Medical Center Veldhoven, Netherlands Electrical Engineering Eindhoven University of Technology Eindhoven, Netherlands  \nXi Long  \nElectrical Engineering Eindhoven University of Technology Eindhoven, Netherlands  \nAbstract—Diagnosis for apnea of prematurity is commonly performed by detecting central apneas (CAs) in the respiratory traces of premature infants. Previous studies reported that up to 65% of CA alarms sounding in clinical practice are false.  \nWe recently showed that using a CA detection model based on elastic net logistic regression (ENLR) and features derived from physiological signals can lead to improved precision. This study explores the possibility of using oth","cbCairH0GniRpNDz","https://ap.wps.com/l/cbCairH0GniRpNDz","pdf",387640,1,7,"English","en",105,"# Abstract\n# Introduction\n## Motivation and clinical challenge\n## Prior approach with explainable models\n# Methods\n## Dataset and validation strategy\n## Explainable algorithm variants (SVM, XGBoost, KNN)\n# Results\n## Performance comparison (AUROC and false alarms)\n## Feature selection and importance analysis\n# Index Terms","[{\"question\":\"为什么需要解释性机器学习用于早产儿中心性呼吸暂停检测？\",\"answer\":\"临床上通过呼吸波形检测中心性呼吸暂停，但相当比例的告警是误报。解释性模型有助于在提升检测精度的同时理解关键判别特征。\"},{\"question\":\"本研究使用了哪些机器学习算法进行中心性呼吸暂停检测？\",\"answer\":\"在相同数据集上构建了基于 SVM、XGBoost 和 KNN 的中心性呼吸暂停检测模型，并用于与既有的解释性方法进行对比。\"},{\"question\":\"XGBoost模型相较其他模型带来了什么改进？\",\"answer\":\"XGBoost在稳定期远离呼吸暂停事件时，每位患儿每小时误报数量更少，同时AUROC仍保持具有竞争力的水平。\"},{\"question\":\"哪些特征对模型判别起到了关键作用？\",\"answer\":\"树模型前部决策路径中的特征在留出不同患儿测试时保持一致，并且在XGBoost与ENLR中都具有较高的重要性，能够有效区分中心性呼吸暂停与稳定期。\"}]","Explainable Machine Learning for Central Apnea Detection in Premature Infants | PDF",1785896573,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":28},"explainable-machine-learning-for-central-apnea-detection-in-premature-infants","",{"@graph":36,"@context":89},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/explainable-machine-learning-for-central-apnea-detection-in-premature-infants/125089/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"为什么需要解释性机器学习用于早产儿中心性呼吸暂停检测？","Question",{"text":75,"@type":76},"临床上通过呼吸波形检测中心性呼吸暂停，但相当比例的告警是误报。解释性模型有助于在提升检测精度的同时理解关键判别特征。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"本研究使用了哪些机器学习算法进行中心性呼吸暂停检测？",{"text":80,"@type":76},"在相同数据集上构建了基于 SVM、XGBoost 和 KNN 的中心性呼吸暂停检测模型，并用于与既有的解释性方法进行对比。",{"name":82,"@type":73,"acceptedAnswer":83},"XGBoost模型相较其他模型带来了什么改进？",{"text":84,"@type":76},"XGBoost在稳定期远离呼吸暂停事件时，每位患儿每小时误报数量更少，同时AUROC仍保持具有竞争力的水平。",{"name":86,"@type":73,"acceptedAnswer":87},"哪些特征对模型判别起到了关键作用？",{"text":88,"@type":76},"树模型前部决策路径中的特征在留出不同患儿测试时保持一致，并且在XGBoost与ENLR中都具有较高的重要性，能够有效区分中心性呼吸暂停与稳定期。","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,123,126,131,134,138],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":124,"slug":125},30,"research-report",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":129,"slug":130},9,"Religion & Spirituality",20,"religion-spirituality",{"id":129,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":129,"slug":133},"World Cup","world-cup",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":135,"slug":137},10,"Lifestyle","lifestyle",{"id":139,"doc_module":4,"doc_module_name":46,"category_name":140,"show_sort_weight":110,"slug":141},19,"General","general"]