Monitoring The After-Effects Of Ischemic Stroke Through EEG Microstates And Machine Learning

Monitoring The After-Effects Of Ischemic Stroke Through EEG Microstates And Machine Learning | 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 Monitoring The After-Effects Of Ischemic Stroke Through EEG Microstates And Machine Learning Fang Wang, Yu-Chu Tian, Xueying Zhang, Fengyun Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1297608/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 Background and Purpose: Stroke may cause extensive after-effects such as motor function impairments and disorder of consciousness (DoC). Detecting these after-effects of stroke and monitoring their changes are challenging jobs currently undertaken via traditional clinical examinations. These behavioural examinations often take a great deal of manpower and time, thus consuming significant resources. Computer-aided examinations of the electroencephalogram (EEG) microstates derived from bedside EEG monitoring may provide an alternative way to assist medical practitioners in a quick assessment of the after-effects of stroke. Methods: In this study, we designed a framework to extract microstates maps and calculate their statistical parameters to input to classifiers to identify DoC in ischemic stroke patients automatically. As the dataset is imbalanced with the minority of patients being DoC, an ensemble of support vector machines (EOSVM) is designed to solve the problem that classifiers always tend to be the majority classes in the classification on an imbalanced dataset. Results: The experimental results show EOSVM get better performance (with accuracy and F1-Score both higher than 89%), improving sensitivity the most, from lower than 60% (SVM and AdaBoost) to higher than 80%. This highlighted the usefulness of the EOSVM-aided DoC detection based on microstates parameters.  Conclusion: Therefore, the classifier EOSVM classification based on features of EEG microstates is helpful to medical practitioners in DoC detection with saved resources that would otherwise be consumed in traditional clinic checks. 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-1297608","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":83735681,"identity":"74d1c218-8ff0-483a-b6fe-4c7684113ad6","order_by":0,"name":"Fang Wang","email":"","orcid":"","institution":"XiHua University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fang","middleName":"","lastName":"Wang","suffix":""},{"id":83735682,"identity":"131bf978-2fa1-4664-9d36-d89bdbceedaa","order_by":1,"name":"Yu-Chu Tian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYJCCAwwMNhAWD3EamEFa0hgY2EjRAgSHSdBicID/4IGfO87LbrjfwPjgbRuDvMEBglqYGQ72nrltvOEYA7Ph3DYGww3EaDnA23Y7EaiFTZq3jYGRKC0H/7adA2lh/w3UYk+UlsO8bQfAtjADtSQS1CJ5mNngsGxbsvHMY4nNknPOSSTPJKSF73jj449v2+xk+w4fPvjhTZmNbR8hLQqHITRjAwgxMEgQUA8E8g1wLaNgFIyCUTAKcAAAMy1FZu5M6F4AAAAASUVORK5CYII=","orcid":"","institution":"Queensland University of Technology","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yu-Chu","middleName":"","lastName":"Tian","suffix":""},{"id":83735684,"identity":"65bc9643-0d5a-4bf5-a6e0-0c5b73d66b34","order_by":2,"name":"Xueying Zhang","email":"","orcid":"","institution":"Taiyuan University of Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xueying","middleName":"","lastName":"Zhang","suffix":""},{"id":83735685,"identity":"2ec7014a-62af-4bba-a0cf-7c905e6d673f","order_by":3,"name":"Fengyun Hu","email":"","orcid":"","institution":"Shanxi Provincial People’s Hospital affiliated with Shanxi Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fengyun","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2022-01-26 03:29:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1297608/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1297608/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":18282884,"identity":"7506354e-e6ea-411f-8b2e-91340c41d4ba","added_by":"auto","created_at":"2022-02-16 14:49:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":860644,"visible":true,"origin":"","legend":"","description":"","filename":"CLOSED1thforsubmissionstoScientificReports1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1297608/v1_covered.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eMonitoring The After-Effects Of Ischemic Stroke Through EEG Microstates And Machine Learning\u003c/p\u003e","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1297608/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"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":false,"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-1297608/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1297608/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and Purpose: \u003c/strong\u003eStroke may cause extensive after-effects such as motor function impairments and disorder of consciousness (DoC). Detecting these after-effects of stroke and monitoring their changes are challenging jobs currently undertaken via traditional clinical examinations. These behavioural examinations often take a great deal of manpower and time, thus consuming significant resources. Computer-aided examinations of the electroencephalogram (EEG) microstates derived from bedside EEG monitoring may provide an alternative way to assist medical practitioners in a quick assessment of the after-effects of stroke. \u003cstrong\u003eMethods: \u003c/strong\u003eIn this study, we designed a framework to extract microstates maps and calculate their statistical parameters to input to classifiers to identify DoC in ischemic stroke patients automatically. As the dataset is imbalanced with the minority of patients being DoC, an ensemble of support vector machines (EOSVM) is designed to solve the problem that classifiers always tend to be the majority classes in the classification on an imbalanced dataset.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The experimental results show EOSVM get better performance (with accuracy and F1-Score both higher than 89%), improving sensitivity the most, from lower than 60% (SVM and AdaBoost) to higher than 80%. This highlighted the usefulness of the EOSVM-aided DoC detection based on microstates parameters. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u003cspan class=\"ql-cursor\"\u003e\u003c/span\u003eConclusion:\u003c/strong\u003e Therefore, the classifier EOSVM classification based on features of EEG microstates is helpful to medical practitioners in DoC detection with saved resources that would otherwise be consumed in traditional clinic checks.\u003c/p\u003e","manuscriptTitle":"Monitoring The After-Effects Of Ischemic Stroke Through EEG Microstates And Machine Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-02-16 14:49:13","doi":"10.21203/rs.3.rs-1297608/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"9e62238a-8e00-4f74-b604-ef0aab993371","owner":[],"postedDate":"February 16th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-08-09T07:14:19+00:00","versionOfRecord":[],"versionCreatedAt":"2022-02-16 14:49:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1297608","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1297608","identity":"rs-1297608","version":["v1"]},"buildId":"ehx78VzkSd0WSzXnipQa-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}