[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120348-en":3,"doc-seo-120348-105":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":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},120348,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Quantitative electroencephalogram and machine learning to predict expired sevoflurane concentration in infants - Research report","Processed electroencephalography (EEG) indices commonly used to guide anesthetic dosing in adults lack validation in young infants. Raw EEG can be transformed into quantitative EEG (qEEG) parameters, enabling data-driven prediction. This work tests whether machine learning with qEEG can classify expired sevoflurane concentrations using frontal EEG from infants ≤3 months. One-minute epochs were labeled into four eSevo levels, with eight models evaluated via accuracy and F1-score. Among 42 infants (4574 epochs), top models reached 67.5–68.7% accuracy, and SHAP highlighted burst suppression ratio and entropy β as key contributors, with consistent results when omitting burst suppression ratio.","EUR Research Information Portal  \nQuantitative electroencephalogram and machine learning to predict expired sevoflurane concentration in infants  \nPublished in:  \nJournal of Clinical Monitoring and Computing  \nPublication status and date:  \nE-pub ahead of print: 17/05/2025  \nDOI (link to publisher):  \n10.1007/s10877-025-01301-2  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nDocument License/Available under:  \nCC BY  \nCitation for the published version (APA):  \nKumar, R. , Skowno, J. , von Ungern-Sternberg, B. S. , BRAIN Collaborative Investigators, Davidson, A. , Xu, T. , Zhang, J. , Song, X. , Zhang, M. , Zhao, P. , Liu, H. , Jiang, Y. , Zuo, Y. , de Graaff, J. C. , Vutskits, L. , Olbrecht, V. A. , Szmuk, P. , Simpao, A. F. , Tsui, F. , ... Yuan, I. (2025) . Quantitative electroencephalogram and machine learning to predict expired sevoflurane concentration in infants. Journal of Clinical Monitoring and Computing, Article 036031. Advance online publication. [https://doi.org/10.1007/s10877-025-01301-2](https://doi.org/10.1007/s10877-025-01301-2)  \nLink to publication on the EUR Research Information Portal  \nTerms and Conditions of Use  \nExcept as permitted by the applicable copyright law, you may not reproduce or make this material available to any third party without the prior written permission from the copyright holder(s) . Copyright law allows the following uses of this material without prior permission:  \n• you may download, save and print a copy of this material for your personal use only;  \n• you may share the EUR portal link to this material.  \nIn case the material is published with an open access license (e.g. a Creative Commons (CC) license), other uses may be allowed. Please check the terms and conditions of the specific license.  \nTake-down policy  \nIf you believe that this material infringes your copyright and/or any other intellectual property rights, you may request its removal by contacting us at the following email address: [openaccess.library@eur.nl. Please](openaccess.library@eur.nl. Please) provide us with all the relevant information, including the reasons why you believe any of your rights have been infringed. In case of a legitimate complaint, we will make the material inaccessible and/or remove it from the website.  \nJournal of Clinical Monitoring and Computing [https://doi.org/10.1007/s10877-025-01301-2](https://doi.org/10.1007/s10877-025-01301-2)  \nQuantitative electroencephalogram and machine learning to predict expired sevoflurane concentration in infants  \nRachit Kumar1 · Justin Skowno2,3 · Britta S. von Ungern‑Sternberg4,5,6,7 · Andrew Davidson8,9,10 · Ting Xu11 · Jianmin Zhang12 · XingRong Song13 · Mazhong Zhang14,15,16 · Ping Zhao17 · Huacheng Liu18,19,20 · Yifei Jiang21 · Yunxia Zuo22 · Jurgen C. de Graaff23,24 · Laszlo Vutskits25,26 · Vanessa A. Olbrecht27,28 · Peter Szmuk29,30 ·  \nAllan F. Simpao31 · Fuchiang (Rich) Tsui31,32 · Jayant (Nick) Pratap31 · Asif Padiyath31 · Olivia Nelson31 · Charles D. Kurth33 · Ian Yuan31 · BRAIN Collaborative Investigators  \nReceived: 12 December 2024 / Accepted: 23 April 2025 © The Author(s) 2025  \nAbstract  \nProcessed electroencephalography (EEG) indices used to guide anesthetic dosing in adults are not validated in young infants. Raw EEG can be processed mathematically, yielding quantitative EEG parameters (qEEG) . We hypothesized that machine learning combined with qEEG can accurately classify expired sevoflurane concentrations in young infants. Knowledge from this may contribute to development of future infant-specific EEG algorithms. Frontal EEG collected from infants ≤ 3 months were time-matched as one-minute epochs to expired sevoflurane (eSevo) . Fifteen qEEG parameters were extracted from each epoch and eight machine learning models combined the qEEG to classify each epoch into one of four eSevo levels (%): 0.1–1.0, 1.0–2.1, 2.1–2.9, and >2.9. 64 epochs formed the post hoc SHAP dataset to determine the qEEG that contributed mos","cbCaiqcpCSgJ5fp9","https://ap.wps.com/l/cbCaiqcpCSgJ5fp9","pdf",1971743,1,17,"English","en",105,"# Abstract\n# 1 Introduction","[{\"question\":\"What problem does this study address about infant anesthesia dosing?\",\"answer\":\"Conventional EEG-based dosing indices validated in adults are not validated in young infants, making anesthesia dosing potentially unreliable for this population.\"},{\"question\":\"How was the expired sevoflurane concentration (eSevo) prediction task set up?\",\"answer\":\"Frontal EEG from infants ≤3 months was segmented into one-minute epochs, extracted into 15 qEEG parameters per epoch, and classified into four eSevo levels.\"},{\"question\":\"Which features and models contributed most to prediction performance?\",\"answer\":\"Top-performing classifiers (K-nearest neighbors, default multi-layer perceptron, and support vector machine) achieved about 67.5–68.7% accuracy, with SHAP identifying burst suppression ratio and entropy β as the strongest contributors.\"}]","Quantitative electroencephalogram and machine learning to predict expired sevoflurane concentration in infants - Research report | PDF",1785729605,43,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"quantitative-electroencephalogram-and-machine-learning-to-predict-expired-sevoflurane-concentration-in-infants-research-report","",{"@graph":36,"@context":85},[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/quantitative-electroencephalogram-and-machine-learning-to-predict-expired-sevoflurane-concentration-in-infants-research-report/120348/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does this study address about infant anesthesia dosing?","Question",{"text":75,"@type":76},"Conventional EEG-based dosing indices validated in adults are not validated in young infants, making anesthesia dosing potentially unreliable for this population.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the expired sevoflurane concentration (eSevo) prediction task set up?",{"text":80,"@type":76},"Frontal EEG from infants ≤3 months was segmented into one-minute epochs, extracted into 15 qEEG parameters per epoch, and classified into four eSevo levels.",{"name":82,"@type":73,"acceptedAnswer":83},"Which features and models contributed most to prediction performance?",{"text":84,"@type":76},"Top-performing classifiers (K-nearest neighbors, default multi-layer perceptron, and support vector machine) achieved about 67.5–68.7% accuracy, with SHAP identifying burst suppression ratio and entropy β as the strongest contributors.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"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"]