Scaling profiles: A novel diagnostic approach for enhanced characterization of heart rate variability in cardiac pathologies

Scaling profiles: A novel diagnostic approach for enhanced characterization of heart rate variability in cardiac pathologies | 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 Scaling profiles: A novel diagnostic approach for enhanced characterization of heart rate variability in cardiac pathologies Yudai Fujimoto, Madhur Mangalam, Eiichi Watanabe, Ken Kiyono This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6488836/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 Time series analysis methods, such as Detrended Fluctuation Analysis (DFA) and Detrending Moving-Average Analysis (DMA), estimate scaling exponents and characterize long-range temporal correlations by identifying broad linear regions in log-log plots of fluctuation magnitude and scale. However, this approach has limitations when applied to complex physiological signals that exhibit transitions from scaling to non-scaling behavior, lack clear scaling properties, or display multiple scaling regimes. Previous research has indicated that long-range temporal correlations in heart rate variability (HRV) differ between healthy individuals and those with cardiovascular conditions such as congestive heart failure and atrial fibrillation. Despite these findings, the full diagnostic performance of scale-dependent variations in these exponents remains unexplored. To address this gap, we introduce a novel “scaling profile” methodology that systematically maps local scaling behavior across various scales and window widths. We preferred DMA for all analyses because DFA yields highly unstable local scaling exponents. We establish the validity of this methodology using simulated processes with known long-range temporal correlations. Applying this method to HRV data from healthy individuals and patients with congestive heart failure and atrial fibrillation demonstrated remarkable diagnostic performance. Specifically, we quantified the diagnostic performance of scaling profiles using receiver operating characteristic (ROC) analysis, revealing patterns of discriminatory power that align with pathophysiological mechanisms underlying different cardiac conditions. Congestive heart failure exhibited good discrimination (maximum area under the curve, AUCmax = 0.889) at smaller window widths and shorter scales, while atrial fibrillation showed excellent discriminatory power with AUCmax ∼ 1, reflecting condition-specific patterns of autonomic modulation. ROC analysis revealed local scaling exponents could effectively discriminate between survivor and nonsurvivor CHF patients ( AUCmax = 0.63) and between AF patients with and without ischemic stroke (AUCmax = 0.64). Kaplan-Meier survival analyses demonstrated significant stratification of highand low-risk CHF patients for mortality (χ2 = 7.07, p = 0.008) and highand low-risk AF patients for ischemic stroke (χ2 = 8.48, p = 0.004) using optimal cutoff values of local scaling exponents, confirming their robust prognostic value across both cardiovascular conditions. Our approach reveals rich dynamical signatures that more comprehensively characterize cardiac pathologies, advancing the theoretical framework and clinical utility of scaling analysis in physiological time series. Health sciences/Cardiology Biological sciences/Systems biology/Complexity Biological sciences/Systems biology/Numerical simulations Biological sciences/Systems biology/Signal processing Biological sciences/Systems biology/Time series 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-6488836","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":460651866,"identity":"5b497efa-9f90-40d9-bf3d-3e64dfacb0af","order_by":0,"name":"Yudai Fujimoto","email":"data:image/png;base64,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","orcid":"","institution":"Osaka University","correspondingAuthor":true,"prefix":"","firstName":"Yudai","middleName":"","lastName":"Fujimoto","suffix":""},{"id":460651867,"identity":"c89bf414-4324-4722-ba21-68f76ffb63a2","order_by":1,"name":"Madhur Mangalam","email":"","orcid":"","institution":"University of Nebraska at Omaha","correspondingAuthor":false,"prefix":"","firstName":"Madhur","middleName":"","lastName":"Mangalam","suffix":""},{"id":460651868,"identity":"f6987d73-b79f-4129-8b08-44eb6bd7b27c","order_by":2,"name":"Eiichi Watanabe","email":"","orcid":"","institution":"Fujita Health University","correspondingAuthor":false,"prefix":"","firstName":"Eiichi","middleName":"","lastName":"Watanabe","suffix":""},{"id":460651869,"identity":"4d930b0d-a103-47d4-9d4a-53bcd6e7c08e","order_by":3,"name":"Ken Kiyono","email":"","orcid":"","institution":"Osaka University","correspondingAuthor":false,"prefix":"","firstName":"Ken","middleName":"","lastName":"Kiyono","suffix":""}],"badges":[],"createdAt":"2025-04-20 10:53:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6488836/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6488836/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105525056,"identity":"23cd994b-5d21-44e2-9cbc-2063fbedba58","added_by":"auto","created_at":"2026-03-27 03:55:26","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3127535,"visible":true,"origin":"","legend":"","description":"","filename":"Fujimotoetal.2025NatureScientificReports.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6488836/v1_covered_f0102fbf-301d-4a76-8026-a8e4003b6e1c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Scaling profiles: A novel diagnostic approach for enhanced characterization of heart rate variability in cardiac pathologies","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":"","lastPublishedDoi":"10.21203/rs.3.rs-6488836/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6488836/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Time series analysis methods, such as Detrended Fluctuation Analysis (DFA) and Detrending Moving-Average Analysis (DMA), estimate scaling exponents and characterize long-range temporal correlations by identifying broad linear regions in log-log plots of fluctuation magnitude and scale. However, this approach has limitations when applied to complex physiological signals that exhibit transitions from scaling to non-scaling behavior, lack clear scaling properties, or display multiple scaling regimes. Previous research has indicated that long-range temporal correlations in heart rate variability (HRV) differ between healthy individuals and those with cardiovascular conditions such as congestive heart failure and atrial fibrillation. Despite these findings, the full diagnostic performance of scale-dependent variations in these exponents remains unexplored. To address this gap, we introduce a novel “scaling profile” methodology that systematically maps local scaling behavior across various scales and window widths. We preferred DMA for all analyses because DFA yields highly unstable local scaling exponents. We establish the validity of this methodology using simulated processes with known long-range temporal correlations. Applying this method to HRV data from healthy individuals and patients with congestive heart failure and atrial fibrillation demonstrated remarkable diagnostic performance. Specifically, we quantified the diagnostic performance of scaling profiles using receiver operating characteristic (ROC) analysis, revealing patterns of discriminatory power that align with pathophysiological mechanisms underlying different cardiac conditions. Congestive heart failure exhibited good discrimination (maximum area under the curve, AUCmax = 0.889) at smaller window widths and shorter scales, while atrial fibrillation showed excellent discriminatory power with AUCmax ∼ 1, reflecting condition-specific patterns of autonomic modulation. ROC analysis revealed local scaling exponents could effectively discriminate between survivor and nonsurvivor CHF patients ( AUCmax = 0.63) and between AF patients with and without ischemic stroke (AUCmax = 0.64). Kaplan-Meier survival analyses demonstrated significant stratification of highand low-risk CHF patients for mortality (χ2 = 7.07, p = 0.008) and highand low-risk AF patients for ischemic stroke (χ2 = 8.48, p = 0.004) using optimal cutoff values of local scaling exponents, confirming their robust prognostic value across both cardiovascular conditions. 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