[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127667-en":3,"doc-seo-127667-105":30,"detail-sidebar-cat-0-en-105":92},{"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":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},127667,962084925636,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine Learning to Classify Cardiotocography for Fetal Hypoxia Detection","Fetal hypoxia during labor can lead to severe outcomes such as stillbirth and cerebral palsy, and cardiotocography (CTG) is widely used for non-invasive fetal monitoring. The study addresses inter-clinician inconsistencies in visual CTG interpretation by using machine learning to classify abnormal CTG. Without a gold standard, prior work relied on surrogate biomarkers, some of which lacked clinical relevance. The proposed approach uses Apgar scores as a surrogate benchmark for recovery and evaluates model performance using validated features and CTG-specific characteristics.","Edinburgh Research Explorer  \nMachine Learning to Classify Cardiotocography for Fetal Hypoxia Detection  \nCitation for published version:  \nFrancis, F, Luz, S, Wu, H, Townsend, R & Stock, SS 2023, Machine Learning to Classify Cardiotocography for Fetal Hypoxia Detection. in Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2023. vol. 2023, Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Institute of Electrical and Electronics Engineers, The 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Sydney, New South Wales, Australia, 24/07/23 . [https://doi.org/10.1109/EMBC40787.2023.10340803](https://doi.org/10.1109/EMBC40787.2023.10340803)  \nDigital Object Identifier (DOI):  \n10.1109/EMBC40787.2023.10340803  \nLink:  \nLink to publication record in Edinburgh Research Explorer  \nDocument Version:  \nPeer reviewed version  \nPublished In:  \nProceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2023  \nGeneral rights  \nCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please [contact openaccess@ed.ac.uk](contact openaccess@ed.ac.uk) providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 26. Nov. 2025  \nMachine Learning to Classify Cardiotocography for Fetal Hypoxia  \nDetection  \nFarah Francis, Saturnino Luz, Honghan Wu, Rosemary Townsend, Sarah S. Stock  \nAbstract—Fetal hypoxia can cause damaging consequences on babies' such as stillbirth and cerebral palsy. Cardiotocography (CTG) has been used to detect intrapartum fetal hypoxia during labor. It is a non-invasive machine that measures the fetal heart rate and uterine contractions. Visual CTG suffers inconsistencies in interpretations among clinicians that can delay interventions. Machine learning (ML) showed potential in classifying abnormal CTG, allowing automatic interpretation. In the absence of a gold standard, researchers used various surrogate biomarkers to classify CTG, where some were clinically irrelevant. We proposed using Apgar scores as the surrogate benchmark of babies' ability to recover from birth. Apgar scores measure newborns' ability to recover from active uterine contraction, which measures appearance, pulse, grimace, activity and respiration. The higher the Apgar score, the healthier the baby is.  \nWe employ signal processing methods topre-process and extract validated features of 552 raw CTG. We also included CTG-specific characteristics as outlined in the NICE guidelines. We employed ML techniques using 22 features and measured performances between ML classifiers. While we found that ML can distinguish CTG with low Apgar scores, results for the lowest Apgar scores, which are rare in the dataset we used, would benefit from more CTG data for better performance. We need an external dataset to validate our model for generalizability to ensure that it does not overfit a specific population.  \nClinical Relevance— This study demonstrated the potential of using a clinically relevant benchmark for classifying CTG to allow automatic early detection of hypoxia to reduce decision-making time in maternity units.  \nI. INTRODUCTION  \nFetal hypoxia occurs when the baby's continuous oxygen supply is disrupted during labor. Fetal hypoxia can cause stillbirth, neonatal encephalopathy and developmental disabilities [1-3]. During uterine contractions (UC), tempora","cbCaidxYlOWUBWrH","https://ap.wps.com/l/cbCaidxYlOWUBWrH","pdf",382386,1,5,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What clinical problem does the study address?\",\"answer\":\"Fetal hypoxia during labor can cause serious adverse outcomes, and current CTG interpretation—especially visual assessment—has inconsistencies that may delay intervention.\"},{\"question\":\"How do the authors define the benchmark for CTG classification?\",\"answer\":\"They use Apgar scores as a surrogate benchmark representing the newborn’s ability to recover from active uterine contractions.\"},{\"question\":\"What data and features are used for the machine learning approach?\",\"answer\":\"The method pre-processes and extracts validated features from 552 raw CTG signals and incorporates CTG-specific characteristics aligned with NICE guidelines, then evaluates ML classifiers using 22 features.\"}]","Machine Learning to Classify 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clinical problem does the study address?","Question",{"text":76,"@type":77},"Fetal hypoxia during labor can cause serious adverse outcomes, and current CTG interpretation—especially visual assessment—has inconsistencies that may delay intervention.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How do the authors define the benchmark for CTG classification?",{"text":81,"@type":77},"They use Apgar scores as a surrogate benchmark representing the newborn’s ability to recover from active uterine contractions.",{"name":83,"@type":74,"acceptedAnswer":84},"What data and features are used for the machine learning approach?",{"text":85,"@type":77},"The method pre-processes and extracts validated features from 552 raw CTG signals and incorporates CTG-specific characteristics aligned with NICE guidelines, then evaluates ML classifiers using 22 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