Sleep Apnea Detection using Multimodal Physiological Signals

Sleep Apnea Detection using Multimodal Physiological Signals | 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 Sleep Apnea Detection using Multimodal Physiological Signals Tasnim Nishat Islam, Afia Zuhaira This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6897852/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 Sleep apnea, characterized by repeated interruptions in breathing during sleep, is a highly prevalent disorder affecting individuals between the ages of 30 to 70. This study proposes an automated approach to sleep apnea detection using physiological signals—electrocardiography (ECG), electroencephalography (EEG), and peripheral oxygen saturation (SpO$_2$). We apply both classical machine learning methods, including Random Forest classifier, and deep learning technique Network to identify apnea events. Discriminative features are extracted from ECG, SpO$_2$ and EECG signals. Moreover, EEG and ECG data are converted into spectrograms to capture stage-specific frequency patterns. For deep learning classification, recurrence plots and spectrograms are used as input to a ResNet-18 convolutional neural network. The models achieve 83$\%$ accuracy in binary classification of apnea versus non-apnea events. This work highlights the potential of combining traditional machine learning with deep neural networks to develop an accessible, non-invasive diagnostic tool for sleep apnea using data from wearable sensors. Biomedical Engineering Sleep Apnea Random Forest CNN Resnet ECG SPO2 Signal Processing Full Text Additional Declarations The authors declare no competing interests. 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-6897852","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":471458714,"identity":"732345c4-237c-4c3b-a7c0-83531b2ce7d3","order_by":0,"name":"Tasnim Nishat Islam","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYDACZgbGAwwMNXIMDDwQLkHAA1QD1HLMmAQtDGAtzIkNRGuxZ2d/cOBnDlv6hmtnD35gqLAG6iXoMB6Dg73bZHI33M5LlmA4k06UFoYDvNvYgFpyzBgY2w4To4X9wcG/25jTDcBa/hGlhcHgMO825gSIlgZitBzmMTgsu+2Y4UyQXxKOpRsT1MLef/zhw7fbauT5buce/PChxlqWoBZUkECa8lEwCkbBKBgFuAAAwi49fwviYQkAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-1921-3650","institution":"University of Maryland Baltimore County","correspondingAuthor":true,"prefix":"","firstName":"Tasnim","middleName":"Nishat","lastName":"Islam","suffix":""},{"id":471458715,"identity":"40a1b29e-c900-47c5-851a-d5bd95478a72","order_by":1,"name":"Afia Zuhaira","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtUlEQVRIiWNgGAWjYDAC5oMNDAwGNgwMEiAeGzFa2BKBWgrSSNKSACQ+HCZBC38bc+PHHwbnE9fObn7A8KHsMGEtEscYmyUkDG4nbrtzzIBxxjkitDDcb2yQMABpuZHDwMzbRoQWeaAtPxIMzkG0/CVGi8ExxjaJAwYHIFoYidFiCNRi2WCQbAzyy8Gec+mEtcgdY39888cfO9ltt5sfPvhRZk1YCwo4QKL6UTAKRsEoGAW4AABvikA5ANy50QAAAABJRU5ErkJggg==","orcid":"","institution":"University of Maryland Baltimore County","correspondingAuthor":true,"prefix":"","firstName":"Afia","middleName":"","lastName":"Zuhaira","suffix":""}],"badges":[],"createdAt":"2025-06-15 10:41:44","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6897852/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6897852/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84766572,"identity":"9959084c-cfb8-4c0c-8a79-d32fd367b36f","added_by":"auto","created_at":"2025-06-17 07:19:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1713598,"visible":true,"origin":"","legend":"","description":"","filename":"SleepApneaAnalysisFixErrors1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6897852/v1_covered_ae604449-8477-4b4a-bea2-4ea8b74fe318.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eSleep Apnea Detection using Multimodal Physiological Signals\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Sleep Apnea, Random Forest, CNN, Resnet, ECG, SPO2, Signal Processing","lastPublishedDoi":"10.21203/rs.3.rs-6897852/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6897852/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSleep apnea, characterized by repeated interruptions in breathing during sleep, is a highly prevalent disorder affecting individuals between the ages of 30 to 70. This study proposes an automated approach to sleep apnea detection using physiological signals—electrocardiography (ECG), electroencephalography (EEG), and peripheral oxygen saturation (SpO$_2$). We apply both classical machine learning methods, including Random Forest classifier, and deep learning technique Network to identify apnea events. Discriminative features are extracted from ECG, SpO$_2$ and EECG signals. Moreover, EEG and ECG data are converted into spectrograms to capture stage-specific frequency patterns. For deep learning classification, recurrence plots and spectrograms are used as input to a ResNet-18 convolutional neural network. The models achieve 83$\\%$ accuracy in binary classification of apnea versus non-apnea events. 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