Quantum-Inspired Neural Networks for High-Precision Nerve Conduction Velocity Estimation

Quantum-Inspired Neural Networks for High-Precision Nerve Conduction Velocity Estimation | 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 Quantum-Inspired Neural Networks for High-Precision Nerve Conduction Velocity Estimation Hossein Sadeghi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6484182/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 This study presents a transformative quantum-inspired machine learning framework for nerveconduction velocity (NCV) analysis that significantly advances current diagnostic capabilities. Ournovel approach integrates quantum computing principles with advanced signal processing to overcome the three fundamental limitations of conventional methods: oversimplified nerve modeling,temperature sensitivity, and static measurement interpretation. The framework introduces threekey innovations: quantum Fourier feature transformation achieving 32% better feature separation(p<0.001), a temperature-resilient hybrid neural network (64-32-16 architecture), which reduces thermal dependence by 68%, and a probabilistic uncertainty quantification system. Extensive validationon 1000 simulated cases demonstrated exceptional performance metrics, including unprecedentedprecision (MSE: 0.42±0.03 m/s), superior explanatory power (R2: 0.91±0.02), and excellent earlydetection capability (94% sensitivity). The system maintains full clinical interpretability while delivering 28% better temperature compensation and 92% detection rate for critical 3-5µm fibers.These advancements establish a new standard in electrodiagnostic medicine, combining theoreticalinnovation from quantum information theory with immediate clinical applicability for neuropathydiagnosis. The framework’s open-source implementation facilitates widespread adoption and furtherdevelopment of precision neurophysiology. Quantum machine learning Nerve conduction velocity Neuropathy diagnosis Fourier neural networks Clinical neurophysiology 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. 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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-6484182","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":491728745,"identity":"dbdae6a2-384e-49a0-bee2-fdf5cddcb412","order_by":0,"name":"Hossein Sadeghi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYBACgwNAgoeBIYGNvQHIOgARPUCcFp4DpGphkEggpBauhf3hhzcVtXl8ko+fffhx5jADf/sBxsMVeLTYH+Axlpxz5ngxm3Sa8cyeG4cZJM4kMBw8g8+W+28YpHnbjiW2SScYM/B8OMzAcIOB4WADfoc9/s37D6hF8vhnxj9ALfKEtTCYSfM21CS2SfAYM/MAHWZAWAuPmeWcYwcS23hyipllzqTzGJ5JbCDosBtvauoS57cf38z45pi1nNzxw4c/4tMCBYfhLGAcMRKhgYGhjhhFo2AUjIJRMFIBAP4IVspIyByUAAAAAElFTkSuQmCC","orcid":"","institution":"Arak University","correspondingAuthor":true,"prefix":"","firstName":"Hossein","middleName":"","lastName":"Sadeghi","suffix":""}],"badges":[],"createdAt":"2025-04-19 10:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6484182/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6484182/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90416279,"identity":"5a5da49f-7394-4af5-aaef-93bffb9c76e0","added_by":"auto","created_at":"2025-09-02 13:17:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":543089,"visible":true,"origin":"","legend":"","description":"","filename":"PaperNCVN.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6484182/v1_covered_7e2cf0bd-039f-474a-8fba-77664714015a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quantum-Inspired Neural Networks for High-Precision Nerve Conduction Velocity Estimation","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":"Quantum machine learning, Nerve conduction velocity, Neuropathy diagnosis, Fourier neural networks, Clinical neurophysiology","lastPublishedDoi":"10.21203/rs.3.rs-6484182/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6484182/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This study presents a transformative quantum-inspired machine learning framework for nerveconduction velocity (NCV) analysis that significantly advances current diagnostic capabilities. 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