A Signal Decomposition Method Based on Improved Complementary Ensemble Empirical Mode Decomposition for Human Pulse Wave Signal

A Signal Decomposition Method Based on Improved Complementary Ensemble Empirical Mode Decomposition for Human Pulse Wave Signal | 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 A Signal Decomposition Method Based on Improved Complementary Ensemble Empirical Mode Decomposition for Human Pulse Wave Signal Haichu Chen, Huannan Zheng, Zhifeng Wang, Baoqian Cai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2319670/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 Traditional artificial blood pump requires the perfusionist to continuously monitor and adjust the speed, which is not suitable for the changeable and high-risk cardiac surgery procedures. In order to improve its efficiency and precision, and adaptively control the artificial blood pump, this paper proposes a human pulse wave signal decomposition method based on the improved complementary integrated empirical mode decomposition (CEEMD). The method first preprocesses the signal using the second-order differential peak-valley algorithm, and then decomposes the preprocessed signal into a finite number of intrinsic mode functions (IMFs) using the CEEMD algorithm. The method preprocesses the signal by using second-order differential peak-valley algorithm, and then using CEEMD to decompose the preprocessed signal into a finite number of intrinsic mode functions. After that, the IMFs were denoised by principal component analysis (PCA), and four control signals were obtained as servo motor control signals, which were used to construct the artificial blood pump control model. Finally, the method is used to compare and analyze the collected pulse wave signal and the signal downloaded from the PhysioBank database. The results show that compared with CEEMD, the method can reduce the error rate of the reconstructed signal by about 15%, with high efficiency, high performance, accuracy and robustness. Complementary Ensemble Empirical Mode Decomposition human pulse wave Principal Component Analysis 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. 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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-2319670","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":158167272,"identity":"7a410021-878e-41e8-8100-77f8257a6372","order_by":0,"name":"Haichu Chen","email":"","orcid":"","institution":"Foshan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Haichu","middleName":"","lastName":"Chen","suffix":""},{"id":158167273,"identity":"b9dd07c0-276e-479a-ae8b-0b6acd88f0ac","order_by":1,"name":"Huannan Zheng","email":"","orcid":"","institution":"Foshan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Huannan","middleName":"","lastName":"Zheng","suffix":""},{"id":158167274,"identity":"42328fd4-15b3-4b3e-ad1f-2a912bc70222","order_by":2,"name":"Zhifeng Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBAC+/lvDICUjRw/M/PhB0RpYZzBA9KSZizZzpZmQIqWw4kbzvMoSBClhVmCx/Bzwa/DiZsP8zAYMNTYRBPUwibfu1l6Zl+68bbDvAceMBxLy20gpIVHgneDNG+Ptey2w3wJBowNhwlrkZDg3fybt4eZcXMzj4EEUVoMJPi/SfP8cFbcwEy8Fr4ya96GNGOJw8BATiDGL/YzeIxv8/wBRmX/4cMPPtTYENYCBoxtUEYCUcrB4A/xSkfBKBgFo2AEAgBwLzz96dcCBAAAAABJRU5ErkJggg==","orcid":"","institution":"Foshan University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Zhifeng","middleName":"","lastName":"Wang","suffix":""},{"id":158167275,"identity":"bf590a48-1316-4f86-9de7-2518e5f122dd","order_by":3,"name":"Baoqian Cai","email":"","orcid":"","institution":"Foshan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Baoqian","middleName":"","lastName":"Cai","suffix":""}],"badges":[],"createdAt":"2022-11-28 07:14:22","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false,"coiExplicitlySet":false},"doi":"10.21203/rs.3.rs-2319670/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2319670/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":38140263,"identity":"c9eb3b4c-6ec2-4ffa-93cb-cc5599aa04e6","added_by":"auto","created_at":"2023-06-07 06:59:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":570690,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2319670/v1_covered_7f909512-a9e6-4c13-8c90-c0c374982c17.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Signal Decomposition Method Based on Improved Complementary Ensemble Empirical Mode Decomposition for Human Pulse Wave Signal","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":true,"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":"Complementary Ensemble Empirical Mode Decomposition, human pulse wave, Principal Component Analysis","lastPublishedDoi":"10.21203/rs.3.rs-2319670/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2319670/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTraditional artificial blood pump requires the perfusionist to continuously monitor and adjust the speed, which is not suitable for the changeable and high-risk cardiac surgery procedures. In order to improve its efficiency and precision, and adaptively control the artificial blood pump, this paper proposes a human pulse wave signal decomposition method based on the improved complementary integrated empirical mode decomposition (CEEMD). The method first preprocesses the signal using the second-order differential peak-valley algorithm, and then decomposes the preprocessed signal into a finite number of intrinsic mode functions (IMFs) using the CEEMD algorithm. The method preprocesses the signal by using second-order differential peak-valley algorithm, and then using CEEMD to decompose the preprocessed signal into a finite number of intrinsic mode functions. After that, the IMFs were denoised by principal component analysis (PCA), and four control signals were obtained as servo motor control signals, which were used to construct the artificial blood pump control model. Finally, the method is used to compare and analyze the collected pulse wave signal and the signal downloaded from the PhysioBank database. 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