[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124827-en":3,"doc-seo-124827-105":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},124827,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Application of Statistical Analysis and Machine Learning to Identify Infants’ Abnormal Suckling Behavior","Objective assessment identifies infants with abnormal suckling behavior using simple non-nutritive suckling devices. Breastfeeding stops early in many mother–child dyads, yet current care lacks objective screening within the first days of life, when milk supply depends on successful practices. A vacuum measurement system records non-nutritive sucking in 91 healthy full-term infants. Normative metrics are established, and computational methods (Mahalanobis distance, KNN) detect anomalies. Case studies include healthy newborns and infants with ankyloglossia, evaluating how oral dysfunction and interventions affect measurements.","UC San Diego  \nUC San Diego Previously Published Works  \nTitle  \nApplication of Statistical Analysis and Machine Learning to Identify Infants’ Abnormal Suckling Behavior  \nPermalink  \n[https://escholarship.org/uc/item/1465s4zb](https://escholarship.org/uc/item/1465s4zb)  \nAuthors  \nTruong, Phuong  \nWalsh, Erin Scott, Vanessa Pet al.  \nPublication Date  \n2024  \nDOI  \n10.1109/jtehm.2024.3390589  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nReceived 18 October 2023; revised 23 January 2024 and 20 March 2024; accepted 22 March 2024.  \nDate of publication 17 April 2024; date of current version 26 April 2024.  \nDigital Object Identifier 10.1109/JTEHM.2024.3390589  \nApplication of Statistical Analysis and Machine Learning to Identify Infants’ Abnormal Suckling Behavior  \nPHUONG TRUONG1, ERIN WALSH2, VANESSA P. SCOTT3, MICHELLE LEFF3, ALICE CHEN4, AND JAMES FRIEND1,5,(Fellow, IEEE)  \n1Medically Advanced Devices Laboratory, Department of Mechanical and Aerospace Engineering, Jacobs School of Engineering,  \nUniversity of California at San Diego, La Jolla, CA 92093, USA  \n2Center for Voice and Swallowing, Department of Otolaryngology, School of Medicine, University of California at San Diego, La Jolla, CA 92122, USA  \n3Department of Pediatrics, School of Medicine, University of California at San Diego, La Jolla, CA 92037, USA  \n4Center for Integrative Medicine, Department of Family Medicine, School of Medicine, University of California at San Diego, La Jolla, CA 92037, USA  \n5Department of Surgery, School of Medicine, University of California at San Diego, La Jolla, CA 92093, USA CORRESPONDING AUTHOR: J. FRIEND ([jfriend@ucsd.edu](jfriend@ucsd.edu))  \nThis work was supported in part by the Clinical and Translational Science Institute under a National Institutes of Health grant 2UL1TR001442-08 to JF, EW, and VS; in part by the University of California San Diego via the Galvanizing Engineering in Medicine and the Academic Senate grant schemes to JF, EW, and VS; in part by the William H. and Mattie Wattis Harris Foundation to JF; and in part by the Krupp Endowed Fund to AC.  \nABSTRACT Objective: Identify infants with abnormal suckling behavior from simple non-nutritive suckling devices. Background: While it is well known breastfeeding is beneficial to the health of both mothers and infants, breastfeeding ceases in 75 percent of mother-child dyads by 6 months. The current standard of care lacks objective measurements to screen infant suckling abnormalities within the first few days of life, a critical time to establish milk supply and successful breastfeeding practices. Materials and Methods: A non-nutritive suckling vacuum measurement system, previously developed by the authors, is used to gather data from 91 healthy full-term infants under thirty days old. Non-nutritive suckling was recorded for a duration of sixty seconds. We establish normative data for the mean suck vacuum, maximum suck vacuum, suckling frequency, burst duration, sucks per burst, and vacuum signal shape. We then apply computational methods (Mahalanobis distance, KNN) to detect anomalies in the data to identify infants with abnormal suckling. We finally provide case studies of healthy newborn infants and infants diagnosed with ankyloglossia. Results: In a series of case evaluations, we demonstrate the ability to detect abnormal suckling behavior using statistical analysis and machine learning. We evaluate cases of ankyloglossia to determine how oral dysfunction and surgical interventions affect non-nutritive suckling measurements. Conclusions: Statistical analysis (Mahalanobis Distance) and machine learning [K nearest neighbor (KNN)] can be viable approaches to rapidly interpret infant suckling measurements. Particularly in practices using the digital suck assessment with a gloved finger, it can provide a more objective, early stage screening method to identify abnormal infant suckling vacuum","cbCaibXCmt8X7lTd","https://ap.wps.com/l/cbCaibXCmt8X7lTd","pdf",3102240,1,14,"English","en",105,"# Abstract\n# I. Introduction\n## Breastfeeding benefits and need for early screening\n# Materials and Methods\n## Vacuum measurement system and data collection\n## Normative metrics and anomaly detection (Mahalanobis distance, KNN)\n# Results\n## Detection of abnormal suckling\n## Ankyloglossia case evaluations\n# Conclusions and Clinical Impact\n## Rapid, objective screening approach","[{\"question\":\"How does the study identify infants with abnormal suckling behavior?\",\"answer\":\"It records non-nutritive sucking with a vacuum measurement system, establishes normative suckling metrics, and applies statistical analysis (Mahalanobis distance) and machine learning (KNN) to detect anomalies.\"},{\"question\":\"What data and normative features are used in the analysis?\",\"answer\":\"The system measures vacuum-related suckling characteristics, including mean and maximum suck vacuum, suckling frequency, burst duration, sucks per burst, and vacuum signal shape.\"},{\"question\":\"How are infants with ankyloglossia evaluated in the study?\",\"answer\":\"Case studies compare non-nutritive suckling measurements in infants diagnosed with ankyloglossia, assessing how oral dysfunction and surgical interventions influence the recorded metrics.\"}]","Application of Statistical Analysis and Machine Learning to Identify Infants’ Abnormal Suckling Behavior | 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does the study identify infants with abnormal suckling behavior?","Question",{"text":75,"@type":76},"It records non-nutritive sucking with a vacuum measurement system, establishes normative suckling metrics, and applies statistical analysis (Mahalanobis distance) and machine learning (KNN) to detect anomalies.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and normative features are used in the analysis?",{"text":80,"@type":76},"The system measures vacuum-related suckling characteristics, including mean and maximum suck vacuum, suckling frequency, burst duration, sucks per burst, and vacuum signal shape.",{"name":82,"@type":73,"acceptedAnswer":83},"How are infants with ankyloglossia evaluated in the study?",{"text":84,"@type":76},"Case studies compare non-nutritive suckling measurements in infants diagnosed with ankyloglossia, assessing how oral dysfunction and surgical interventions influence the recorded 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