Development of a Standardized Real-Time Electrocardiogram (ECG) Dataset for Cardiovascular Disease Detection and Diagnosis Utilizing Advanced Machine Learning Approaches

Development of a Standardized Real-Time Electrocardiogram (ECG) Dataset for Cardiovascular Disease Detection and Diagnosis Utilizing Advanced Machine Learning Approaches | 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 Development of a Standardized Real-Time Electrocardiogram (ECG) Dataset for Cardiovascular Disease Detection and Diagnosis Utilizing Advanced Machine Learning Approaches Imteyaz Hussain khan, Amar Singh, Hilal Ahmed Rather This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6057444/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Jan, 2026 Read the published version in Physical and Engineering Sciences in Medicine → Version 1 posted 6 You are reading this latest preprint version Abstract Purpose Cardiovascular diseases (CVDs) are still the leading cause of death worldwide, emphasizing the critical need for reliable diagnostic systems. This study aims to create a standardized electrocardiogram (ECG) dataset that can be used to detect and classify six major CVDs using machine learning techniques and investigate feature selection and extraction methods for improved performance. Methods The Department of Cardiology at Sher-i-Kashmir Institute of Medical Sciences provided the dataset, which included 26,600 training and 7,980 validation entries. The Biocare 1210 ECG machine and ECG Data Interpretation 1000 Software were used to extract key parameters, including heart rate, PR interval, QT interval, QRS duration, and blood pressure. Principal Component Analysis (PCA) and correlation analysis were used to select features and reduce dimensionality to improve model efficiency. Machine learning algorithms such as Random Forest, Gradient Boosting, Decision Tree, Naive Bayes, K-Nearest Neighbors (KNN), and Deep Neural Networks (DNN) were tested using accuracy, precision, recall, and F1-score metrics. Results Random Forest had the highest test accuracy (86.44%), followed by Gradient Boosting (82.68%) and Decision Tree (82.61%). Simpler models, such as Naive Bayes and KNN, performed moderately, while DNN and logistic regression achieved lower accuracies (29.95%). PCA and correlation analysis improved the model's interpretability while reducing computational complexity. Conclusion This study emphasizes the significance of algorithm selection and feature engineering in clinical applications. The dataset and findings pave the way for the creation of dependable AI-powered diagnostic tools that strike a balance between accuracy and interpretability, as well as a valuable resource for advancing CVD detection using ECG data. Cardiovascular Diseases ECG dataset classification detection Machine learning Full Text Cite Share Download PDF Status: Published Journal Publication published 13 Jan, 2026 Read the published version in Physical and Engineering Sciences in Medicine → Version 1 posted Reviewers agreed at journal 29 Apr, 2025 Reviewers invited by journal 29 Apr, 2025 Editor invited by journal 28 Apr, 2025 Editor assigned by journal 04 Apr, 2025 First submitted to journal 04 Apr, 2025 Editorial decision: Major revisions 24 Feb, 2025 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. 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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-6057444","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":449424559,"identity":"7c43b788-1013-4d65-b8d6-58d522086083","order_by":0,"name":"Imteyaz Hussain khan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBACNiBmBmIefvYGIGVgQbwWGcmeAyAtEsTZBNJiY3AjAcQmQguf2OGDtwtq7vAY3Hx+dcOPAgkG/vbuBPwOk05Ltp5x7BmP5O2csps9QIdJnDm7gYCWHDNpHrbDPHy3c9Ju8AC1GEjkEqPl32Eehptn0m7+IVoLb9thHoEb7MduE2kL0C+8fYd5JHty2G7LGEjwEPSL/Ozkg7d5vh2252c//uzmmz82cvztvfi1gAA0LngMwCRB5Uha2B8QpXoUjIJRMApGHgAAYYpBwihtLsgAAAAASUVORK5CYII=","orcid":"","institution":"Lovely Professional University","correspondingAuthor":true,"prefix":"","firstName":"Imteyaz","middleName":"Hussain","lastName":"khan","suffix":""},{"id":449424560,"identity":"1e575ca5-9192-4948-badd-1a00eb1261d7","order_by":1,"name":"Amar Singh","email":"","orcid":"","institution":"Lovely Professional University","correspondingAuthor":false,"prefix":"","firstName":"Amar","middleName":"","lastName":"Singh","suffix":""},{"id":449424561,"identity":"72a462c9-36eb-4e6a-b353-41a8528b0698","order_by":2,"name":"Hilal Ahmed Rather","email":"","orcid":"","institution":"SKIMS: Sher-i-Kashmir Institute of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hilal","middleName":"Ahmed","lastName":"Rather","suffix":""}],"badges":[],"createdAt":"2025-02-18 15:08:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6057444/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6057444/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s13246-025-01682-3","type":"published","date":"2026-01-13T16:30:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":100616110,"identity":"ce8c7858-6801-477c-b0c4-1c737cb1e872","added_by":"auto","created_at":"2026-01-19 17:40:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":826036,"visible":true,"origin":"","legend":"","description":"","filename":"researchpaper1newformate.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6057444/v1_covered_d0a56a8a-7ec2-4b64-a165-019a58091524.pdf"}],"financialInterests":"","formattedTitle":"Development of a Standardized Real-Time Electrocardiogram (ECG) Dataset for Cardiovascular Disease Detection and Diagnosis Utilizing Advanced Machine Learning Approaches","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"physical-and-engineering-sciences-in-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"apes","sideBox":"Learn more about [Physical and Engineering Sciences in Medicine](http://link.springer.com/journal/13246)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/apes/default.aspx","title":"Physical and Engineering Sciences in Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Cardiovascular Diseases, ECG dataset, classification, detection, Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-6057444/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6057444/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eCardiovascular diseases (CVDs) are still the leading cause of death worldwide, emphasizing the critical need for reliable diagnostic systems. This study aims to create a standardized electrocardiogram (ECG) dataset that can be used to detect and classify six major CVDs using machine learning techniques and investigate feature selection and extraction methods for improved performance.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe Department of Cardiology at Sher-i-Kashmir Institute of Medical Sciences provided the dataset, which included 26,600 training and 7,980 validation entries. The Biocare 1210 ECG machine and ECG Data Interpretation 1000 Software were used to extract key parameters, including heart rate, PR interval, QT interval, QRS duration, and blood pressure. Principal Component Analysis (PCA) and correlation analysis were used to select features and reduce dimensionality to improve model efficiency. Machine learning algorithms such as Random Forest, Gradient Boosting, Decision Tree, Naive Bayes, K-Nearest Neighbors (KNN), and Deep Neural Networks (DNN) were tested using accuracy, precision, recall, and F1-score metrics.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e \u003cem\u003eRandom Forest had the highest test accuracy (86.44%), followed by Gradient Boosting (82.68%) and Decision Tree (82.61%). Simpler models, such as Naive Bayes and KNN, performed moderately, while DNN and logistic regression achieved lower accuracies (29.95%). PCA and correlation analysis improved the model's interpretability while reducing computational complexity.\u003c/em\u003e \u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study emphasizes the significance of algorithm selection and feature engineering in clinical applications. The dataset and findings pave the way for the creation of dependable AI-powered diagnostic tools that strike a balance between accuracy and interpretability, as well as a valuable resource for advancing CVD detection using ECG data.\u003c/p\u003e","manuscriptTitle":"Development of a Standardized Real-Time Electrocardiogram (ECG) Dataset for Cardiovascular Disease Detection and Diagnosis Utilizing Advanced Machine Learning Approaches","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-30 16:27:13","doi":"10.21203/rs.3.rs-6057444/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-04-29T05:07:00+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-29T04:01:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Physical and Engineering Sciences in Medicine","date":"2025-04-28T10:34:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-04T10:54:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Physical and Engineering Sciences in Medicine","date":"2025-04-04T05:12:55+00:00","index":"","fulltext":""},{"type":"decision","content":"Major revisions","date":"2025-02-24T13:42:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"physical-and-engineering-sciences-in-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"apes","sideBox":"Learn more about [Physical and Engineering Sciences in Medicine](http://link.springer.com/journal/13246)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/apes/default.aspx","title":"Physical and Engineering Sciences in Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"35fa0421-145a-4209-88fc-8a4ed6f1c381","owner":[],"postedDate":"April 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T17:06:52+00:00","versionOfRecord":{"articleIdentity":"rs-6057444","link":"https://doi.org/10.1007/s13246-025-01682-3","journal":{"identity":"physical-and-engineering-sciences-in-medicine","isVorOnly":false,"title":"Physical and Engineering Sciences in Medicine"},"publishedOn":"2026-01-13 16:30:19","publishedOnDateReadable":"January 13th, 2026"},"versionCreatedAt":"2025-04-30 16:27:13","video":"","vorDoi":"10.1007/s13246-025-01682-3","vorDoiUrl":"https://doi.org/10.1007/s13246-025-01682-3","workflowStages":[]},"version":"v1","identity":"rs-6057444","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6057444","identity":"rs-6057444","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}