[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125644-en":3,"doc-seo-125644-105":30,"detail-sidebar-cat-0-en-105":83},{"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},125644,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","ECG Recordings as Predictors of Very Early Autism Likelihood - A Machine Learning Approach","Rising autism spectrum disorder (ASD) prevalence increases the need for earlier, reliable detection. Conventional ASD diagnosis relies on behavioral observation and standardized testing by trained experts, while intervention can begin at 1–2 years but diagnoses are often delayed until ages 2–5. This study evaluates non-invasive electrocardiogram (ECG) recordings as an ASD biomarker in 3–6-month-old infants with typical and elevated familial likelihood. ECG-derived features such as heart rate variability and autonomic sympathetic/parasympathetic activity feed multiple machine learning classifiers. Results show that infant ECG signals carry informative signals for familial ASD likelihood, with KNN achieving the strongest performance across sensitivity, F1-score, precision, accuracy, and ROC metrics.","University of South Carolina  \nScholar Commons  \n\n| Publications | Artificial Intelligence Institute |\n| --- | --- |\n| 7-11-2023\u003Cbr>ECG Recordings as Predictors of Very Early Autism Likelihood: A Machine Learning Approach\u003Cbr>Deepa Tilwani\u003Cbr>University of South Carolina-Columbia\u003Cbr>Jessica Bradshaw\u003Cbr>University of South Carolina-Columbia\u003Cbr>Amit Sheth\u003Cbr>University of South Carolina-Columbia\u003Cbr>Christian O'Reilly\u003Cbr>University of South Carolina-Columbia\u003Cbr>Follow this and additional works at: [https://scholarcommons.sc.edu/aii_fac_pub](https://scholarcommons.sc.edu/aii_fac_pub)\u003Cbr> Part of the Computer Engineering Commons, and the Electrical and Computer Engineering Commons |  |\n\nPublication Info  \nPublished in Bioengineering, ed. Larbi Boubchir, Issue Machine Learning for Biomedical Applications, Volume II, 2023.  \n© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/4.0/](creativecommons.org/licenses/by/4.0/)) .  \nThis Article is brought to you by the Artificial Intelligence Institute at Scholar Commons. It has been accepted for inclusion in Publications by an authorized administrator of Scholar Commons. For more information, please [contact](contact digres@mailbox.sc.edu)[ digres@mailbox.sc.edu](contact digres@mailbox.sc.edu).  \n bioengineering  \nArticle  \nECG Recordings as Predictors of Very Early Autism Likelihood: A Machine Learning Approach  \nDeepa Tilwani 1,2,3,4, *, Jessica Bradshaw 3,4,5, Amit Sheth 1,2 and Christian O'Reilly 1,2,3,4  \nCitation: Tilwani, D.; Bradshaw, J.; Sheth, A.; O'Reilly, C. ECG Recordings as Predictors of Very Early Autism Likelihood: A Machine Learning Approach. Bioengineering 2023, 1, 827. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/bioengineering10070827](10.3390/bioengineering10070827)  \nAcademic Editor: Larbi Boubchir  \nReceived: 7 May 2023  \nRevised: 22 June 2023  \nAccepted: 5 July 2023  \nPublished: 11 July 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Artificial Intelligence Institute, University of South Carolina, Columbia, SC 29208, USA; [amit@sc.edu](amit@sc.edu) (A.S.); [christian.oreilly@sc.edu](christian.oreilly@sc.edu) (C.O.)  \n2 Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA  \n3 Carolina Autism and Neurodevelopment Research Center, University of South Carolina, Columbia, SC 29208, USA; [jbradshaw@sc.edu](jbradshaw@sc.edu)  \n4 Institute for Mind and Brain, University of South Carolina, Columbia, SC 29208, USA  \n5 Department of Psychology, University of South Carolina, Columbia, SC 29208, USA  \n* Correspondence: [dtilwani@mailbox.sc.edu](dtilwani@mailbox.sc.edu)  \nAbstract: In recent years, there has been a rise in the prevalence of autism spectrum disorder (ASD) . The diagnosis of ASD requires behavioral observation and standardized testing completed by highly trained experts. Early intervention for ASD can begin as early as 1–2 years of age, but ASD diagnoses are not typically made until ages 2–5 years, thus delaying the start of intervention. There is an urgent need for non-invasive biomarkers to detect ASD in infancy. While previous research using physiological recordings has focused on brain-based biomarkers of ASD, this study investigated the potential of electrocardiogram (ECG) recordings as an ASD biomarker in 3–6-month-old infants. We recorded the heart activity of infants at typical and elevated familial likelihood for ASD during naturalistic interactions with objects and caregi","cbCaioOpvAxSW27M","https://ap.wps.com/l/cbCaioOpvAxSW27M","pdf",1577700,1,18,"English","en",105,"# Abstract\n# Introduction\n## Autism spectrum disorder background and prevalence\n## Family history and risk\n## Need for early, low-cost ASD indicators\n# Materials and Methods\n## Infant cohort and recording protocol\n## ECG feature extraction\n## Machine learning classification approach\n# Results\n## Classification performance across models\n# Discussion and Conclusion\n## Interpretation of ECG predictive signals\n## Future directions for biomarker development","[{\"question\":\"Which machine learning model performed best in classifying ASD likelihood, and what does it imply?\",\"answer\":\"KNN achieved the best overall performance across sensitivity, F1-score, precision, accuracy, and ROC metrics, indicating that infant ECG signals contain relevant information related to ASD familial likelihood.\"}]","ECG Recordings as Predictors of Very Early Autism Likelihood - A Machine Learning Approach | PDF",1785900389,45,{"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":78,"head_meta":80,"extra_data":82,"updated_unix":28},"ecg-recordings-as-predictors-of-very-early-autism-likelihood-a-machine-learning-approach","",{"@graph":36,"@context":77},[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/ecg-recordings-as-predictors-of-very-early-autism-likelihood-a-machine-learning-approach/125644/",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],{"name":72,"@type":73,"acceptedAnswer":74},"Which machine learning model performed best in classifying ASD likelihood, and what does it imply?","Question",{"text":75,"@type":76},"KNN achieved the best overall performance across sensitivity, F1-score, precision, accuracy, and ROC metrics, indicating that infant ECG signals contain relevant information related to ASD familial likelihood.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,120,123,127],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":98,"slug":130},19,"General","general"]