Extraction of fetal heart beat sounds in abdominal phonocardiograms using deep attention transformer network

Extraction of fetal heart beat sounds in abdominal phonocardiograms using deep attention transformer network | 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 Article Extraction of fetal heart beat sounds in abdominal phonocardiograms using deep attention transformer network Murad Almadani, Mohanad Alkhodari, Samit Ghosh, Leontios Hadjileontiadis, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3786850/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 Conventional methods for assessing fetal well-being often require skilled clinicians and are susceptible to noise interference. Echocardiography, a primary technique for this purpose, is reliable but entails high costs and necessitates specialized equipment and trained personnel, presenting challenges in low- and middle-income countries. Phonocardiography (PCG) has recently emerged as a cost-effective alternative, but its performance and complexity limit its widespread use. In this study, we introduce Fetal Heart Sounds U-NetR (FHSU-NETR), a lightweight, easily deployable deep learning model tailored for the simultaneous extraction of fetal and maternal heart activity from raw PCG signals. Validated with data from 20 normal subjects, including a case of fetal tachycardia arrhythmia, FHSU-NETR demonstrated exceptional performance, accurately identifying 95% of the total $35,960$ fetal heartbeats. This significantly outperformed the only method published on the same dataset, regarded as a benchmark method, which detected only 270 beats. The model exhibited a low mean difference in fetal heart rate estimation (-2.55±10.25 bpm) across the entire dataset relative to the ground-truth fetal ECG, successfully detecting the arrhythmia case. Similarly, FHSU-NETR showed a low mean difference in maternal heart rate estimation (-1.15±5.76 bpm) compared to the ground-truth maternal ECG. The model's exceptional ability to identify arrhythmia cases within the dataset underscores its potential for real-world application and generalization. Leveraging the capabilities of deep learning, our proposed model holds promise to alleviate the reliance on medical experts for the interpretation of extensive PCG recordings, thereby enhancing efficiency in clinical settings. Physical sciences/Engineering/Biomedical engineering Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Mathematics and computing/Applied mathematics Full Text Additional Declarations (Not answered) 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-3786850","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":266012763,"identity":"c5164343-81c7-4046-affb-1f0c11906f8f","order_by":0,"name":"Murad Almadani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYLACHgYGGSBlAEQ2QJqx8QAxWnigWtJAWhpI0cJwGCyAV4vujNyHH94w2PHoth/e+Lmg4Lzd2vbDQFtqbKJxaTG7kW4sOYchmcfsTFqx9AyD28nbziQCtRxLy23AqSWNQZqHgZnH7ECOgTQPUIvZAaAWxobD+LQw/+ZhqOcxO//G+DePwblks/MPCWphA9pymMfsRo4Z0JYDdmY3CNly5hmb5RyD40Atz8qseQySE8xuAG1JwOeX42nMN95UVMuZnU/efJvnj5292fn0hw8+1Njg1AIBBghmIlhlAl7laMCeFMWjYBSMglEwMgAAby1dlMpMcfwAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-7501-8585","institution":"Khalifa University","correspondingAuthor":true,"prefix":"","firstName":"Murad","middleName":"","lastName":"Almadani","suffix":""},{"id":266012764,"identity":"d421330f-2df4-4874-bba4-1468bab08437","order_by":1,"name":"Mohanad Alkhodari","email":"","orcid":"https://orcid.org/0000-0002-5248-6327","institution":"University of Oxford","correspondingAuthor":false,"prefix":"","firstName":"Mohanad","middleName":"","lastName":"Alkhodari","suffix":""},{"id":266012765,"identity":"fc43d68d-2e0d-4f2d-a6af-7524b42499ff","order_by":2,"name":"Samit Ghosh","email":"","orcid":"","institution":"Khalifa University","correspondingAuthor":false,"prefix":"","firstName":"Samit","middleName":"","lastName":"Ghosh","suffix":""},{"id":266012766,"identity":"6d3a7537-8105-42b8-acb1-e4a47ed90281","order_by":3,"name":"Leontios Hadjileontiadis","email":"","orcid":"https://orcid.org/0000-0002-9932-9302","institution":"Department of Biomedical Engineering, Khalifa University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Leontios","middleName":"","lastName":"Hadjileontiadis","suffix":""},{"id":266012767,"identity":"ff2c0a6c-9dab-4cab-8ca7-2352a4deb443","order_by":4,"name":"Ahsan Khandoker","email":"","orcid":"","institution":"Khalifa University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ahsan","middleName":"","lastName":"Khandoker","suffix":""}],"badges":[],"createdAt":"2023-12-21 12:20:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3786850/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3786850/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56910053,"identity":"e4de8dca-558b-4213-a479-d509c85466e5","added_by":"auto","created_at":"2024-05-22 04:43:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2524413,"visible":true,"origin":"","legend":"","description":"","filename":"Extractionoffetalheartbeatsoundsinabdominalphonocardiogramsusingdeepattentiontransformernetwork.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3786850/v1_covered_c9177cba-3877-4702-a03e-7dfcb761dcae.pdf"}],"financialInterests":"(Not answered)","formattedTitle":"Extraction of fetal heart beat sounds in abdominal phonocardiograms using deep attention transformer network","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":"[email protected]","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":"","lastPublishedDoi":"10.21203/rs.3.rs-3786850/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3786850/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Conventional methods for assessing fetal well-being often require skilled clinicians and are susceptible to noise interference. Echocardiography, a primary technique for this purpose, is reliable but entails high costs and necessitates specialized equipment and trained personnel, presenting challenges in low- and middle-income countries. Phonocardiography (PCG) has recently emerged as a cost-effective alternative, but its performance and complexity limit its widespread use. In this study, we introduce Fetal Heart Sounds U-NetR (FHSU-NETR), a lightweight, easily deployable deep learning model tailored for the simultaneous extraction of fetal and maternal heart activity from raw PCG signals. Validated with data from 20 normal subjects, including a case of fetal tachycardia arrhythmia, FHSU-NETR demonstrated exceptional performance, accurately identifying 95\\% of the total $35,960$ fetal heartbeats. This significantly outperformed the only method published on the same dataset, regarded as a benchmark method, which detected only 270 beats. The model exhibited a low mean difference in fetal heart rate estimation (-2.55±10.25 bpm) across the entire dataset relative to the ground-truth fetal ECG, successfully detecting the arrhythmia case. Similarly, FHSU-NETR showed a low mean difference in maternal heart rate estimation (-1.15±5.76 bpm) compared to the ground-truth maternal ECG. The model's exceptional ability to identify arrhythmia cases within the dataset underscores its potential for real-world application and generalization. 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