{"paper_id":"27409b7f-5da3-4165-a337-604d7240b6ed","body_text":"Research on an Intelligent Fetal Heart Monitoring Image Classification Model Based on Hybrid Attention Mechanism and Convolutional Neural Networks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Research on an Intelligent Fetal Heart Monitoring Image Classification Model Based on Hybrid Attention Mechanism and Convolutional Neural Networks Xinhao Wang, Chunxia Lin, Qingshan You, Xueying Yang, Ling Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5090931/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Fetal heart rate (FHR) monitoring, a non-invasive method using cardiotocography (CTG), reflects fetal condition in real-time to identify abnormalities and reduce intrauterine hypoxia risk. Hypoxia, a common prenatal issue linked to restricted fetal growth, neurological disorders, and perinatal mortality, alters fetal heart rate patterns such as baseline variability and accelerations. Intelligent CTG classification using FHR signals is challenging but aids in decision-making. Traditional machine learning requires cumbersome feature extraction, hindering real-time classification. This study proposes a method using hybrid attention and ResNet50 for computer vision image classification, assisting doctors and enabling preliminary patient judgments. Utilizing a real hospital dataset, this approach achieved 87% accuracy in experiments. Its advantage lies in directly processing fetal heart monitoring images, bypassing complex feature extraction, and leveraging deep learning for accurate classification, providing a more reliable fetal health monitoring method. Fetal Heart Rate Monitoring Deep Learning Computer Vision Machine learning Attention Mechanism Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-5090931\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":356520014,\"identity\":\"0b5893a2-22d9-4e6a-a162-919b73f2c33b\",\"order_by\":0,\"name\":\"Xinhao Wang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Civil Aviation Flight University of China\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xinhao\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"},{\"id\":356520018,\"identity\":\"94a1457f-9689-4be4-b130-d39e4037fe80\",\"order_by\":1,\"name\":\"Chunxia Lin\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYBACfvbmAwc/VEjIsbE3EKlFsudY4mGJMzbG/DwHiNRicCNH+QBvW1rizBkJxGo5c4bhgMSZw4wbbj7eeIOhxiaasMOO9x44UFBxmNngdlqxBcOxtNwGQlr4zpxLANnCZnA7x0yCseEwYS0MN3IMgH45zGNw8wyRWgQgWtIkJGfwEKkFGMgJoEA24OcB+iWBGL8Ao/LwR2BU1rexH95440ONDRF+QQIGEgmkKIdoIVXHKBgFo2AUjAwAACJmSWDMbf4cAAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"Chengdu Longquanyi District first People's Hospital\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Chunxia\",\"middleName\":\"\",\"lastName\":\"Lin\",\"suffix\":\"\"},{\"id\":356520022,\"identity\":\"f28a419e-6cd1-44a2-89c1-8ea30b5793db\",\"order_by\":2,\"name\":\"Qingshan You\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Civil Aviation Flight University of China\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Qingshan\",\"middleName\":\"\",\"lastName\":\"You\",\"suffix\":\"\"},{\"id\":356520024,\"identity\":\"dbdd02df-f11d-414a-bd26-dab466418528\",\"order_by\":3,\"name\":\"Xueying Yang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Chengdu Medical College\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xueying\",\"middleName\":\"\",\"lastName\":\"Yang\",\"suffix\":\"\"},{\"id\":356520028,\"identity\":\"bf82653b-81b6-401d-890d-bd9c49be9755\",\"order_by\":4,\"name\":\"Ling Zhu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Chengdu Medical College\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ling\",\"middleName\":\"\",\"lastName\":\"Zhu\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-09-15 02:17:51\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-5090931/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-5090931/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":69742758,\"identity\":\"138fcac2-8784-4e6c-bab2-7dcc644984b1\",\"added_by\":\"auto\",\"created_at\":\"2024-11-24 18:46:44\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":766327,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"finalpaper.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5090931/v1_covered_ab9d19fe-2563-42e4-b4b1-cd5599031c60.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Research on an Intelligent Fetal Heart Monitoring Image Classification Model Based on Hybrid Attention Mechanism and Convolutional Neural Networks\",\"fulltext\":[],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":false,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":true,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":true,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Fetal Heart Rate Monitoring, Deep Learning, Computer Vision, Machine learning, Attention Mechanism\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-5090931/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-5090931/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eFetal heart rate (FHR) monitoring, a non-invasive method using cardiotocography (CTG), reflects fetal condition in real-time to identify abnormalities and reduce intrauterine hypoxia risk. Hypoxia, a common prenatal issue linked to restricted fetal growth, neurological disorders, and perinatal mortality, alters fetal heart rate patterns such as baseline variability and accelerations. Intelligent CTG classification using FHR signals is challenging but aids in decision-making. Traditional machine learning requires cumbersome feature extraction, hindering real-time classification. This study proposes a method using hybrid attention and ResNet50 for computer vision image classification, assisting doctors and enabling preliminary patient judgments. Utilizing a real hospital dataset, this approach achieved 87% accuracy in experiments. Its advantage lies in directly processing fetal heart monitoring images, bypassing complex feature extraction, and leveraging deep learning for accurate classification, providing a more reliable fetal health monitoring method.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Research on an Intelligent Fetal Heart Monitoring Image Classification Model Based on Hybrid Attention Mechanism and Convolutional Neural Networks\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-10-31 16:54:37\",\"doi\":\"10.21203/rs.3.rs-5090931/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"564a6378-0eee-47dd-b38d-ec81d859129c\",\"owner\":[],\"postedDate\":\"October 31st, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-11-24T18:38:37+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-10-31 16:54:37\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-5090931\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-5090931\",\"identity\":\"rs-5090931\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}