Multi-Noise Removal from ECG Signals Using Residual Block-Based Generative Adversarial Networks with Customized Loss Function

Multi-Noise Removal from ECG Signals Using Residual Block-Based Generative Adversarial Networks with Customized Loss Function | 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 Multi-Noise Removal from ECG Signals Using Residual Block-Based Generative Adversarial Networks with Customized Loss Function Mohamed Sraitih, Amir Hajjam El Hassani, Younes Jabrane, Emmanuel Andres This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7442470/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract The field of biomedical signal processing has witnessed significant advancements, particularly in the domain of electrocardiogram (ECG) denoising. Existing methods often lack consideration of both local and global feature differences, have limited applicability to various noise types, and can introduce signal distortion after denoising. In our research, we propose and investigate a multi-noise removal model based on Generative Adversarial Networks (GANs), specifically using residual networks. This enables the generator to learn the distribution of ECG noise through adversarial training against a stacked-layer discriminator, thereby addressing persistent challenges in ECG noise reduction—crucial for accurate diagnosis of cardiac conditions. Extensive experiments were conducted on both intra- and inter-patient paradigms using an ECG arrhythmia dataset from the PhysioNet Database, ensuring a comprehensive evaluation of the model’s performance. While many studies—including those evaluated on the intra-patient setting, which tends to overestimate results—report high accuracy, our method demonstrated superior generalization and reliability in the challenging inter-patient paradigm. Specifically, our approach outperformed recent techniques, achieving the highest Signal-to-Noise Ratio (SNR) average of 36.21 and the lowest Root Mean Square Error (RMSE) scores across most SNR input levels. In addition to these standard metrics, our approach demonstrated a maximum improvement in Approximate Entropy (ApEn) of 0.0307 relative to the original clean signal under the most challenging noise condition. This improvement underscores the model’s ability to enhance signal quality after denoising, with promising generalization to new patients. These results highlight the effectiveness of our GAN-based approach for real-world ECG denoising and its potential to advance medical diagnostics. Biomedical signal processing multi-noise removal Noise reduction Electrocardiogram signal Generative Adversarial Networks Residual networks Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 01 May, 2026 Reviews received at journal 28 Sep, 2025 Reviewers agreed at journal 06 Sep, 2025 Reviewers agreed at journal 06 Sep, 2025 Reviewers invited by journal 01 Sep, 2025 Editor assigned by journal 26 Aug, 2025 Submission checks completed at journal 26 Aug, 2025 First submitted to journal 23 Aug, 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. 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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-7442470","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":521735442,"identity":"8bdd4c40-b825-4cdf-be84-f0a9d6022b5d","order_by":0,"name":"Mohamed Sraitih","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIie2OMUvDQBTHH9zgcuJaCCSfQHgSuCXgZ6kUmuUOMhYMGBCapdK1oB+i4he48iBdjnYN6FAXpw7BVRHvCg5igmTrcL/h7t39+fF/AB7PUcLdwQ6jBtShvbGHolHH/RRbo6+K/5Tz8m71Xk8SiO7plZrsJZ2Xt0vG80sb6VZFmM0okCYF3IzRLvamFqbKGK9GNhq2K7XEQE0JkINTSC0HEtlpwUDoTiX+UF8E0eykcUqK0d4pNyC2uy5FBKogAMMPLUMccKeQjTpajBGJrFKOhmfaIF0szDhbPVRrLuqOlvUsfpZ5EtrFnprJJ0VnJT3u9vl1KLbtLT/wXy/958fj8Xg8vfgGJwFlRgdLk1MAAAAASUVORK5CYII=","orcid":"","institution":"University of Technology of Belfort-Montbéliard","correspondingAuthor":true,"prefix":"","firstName":"Mohamed","middleName":"","lastName":"Sraitih","suffix":""},{"id":521735443,"identity":"438a1390-ff34-42a7-9420-5ddd70800e84","order_by":1,"name":"Amir Hajjam El Hassani","email":"","orcid":"","institution":"University of Technology of Belfort-Montbéliard","correspondingAuthor":false,"prefix":"","firstName":"Amir","middleName":"Hajjam El","lastName":"Hassani","suffix":""},{"id":521735444,"identity":"6f66b209-47b7-440e-bc63-e0cf06655411","order_by":2,"name":"Younes Jabrane","email":"","orcid":"","institution":"Cadi Ayyad University","correspondingAuthor":false,"prefix":"","firstName":"Younes","middleName":"","lastName":"Jabrane","suffix":""},{"id":521735445,"identity":"cebbbe2d-e42e-4ef6-8501-5c39ca914b1c","order_by":3,"name":"Emmanuel Andres","email":"","orcid":"","institution":"Hôpitaux Universitaires de Strasbourg","correspondingAuthor":false,"prefix":"","firstName":"Emmanuel","middleName":"","lastName":"Andres","suffix":""}],"badges":[],"createdAt":"2025-08-23 16:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7442470/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7442470/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102296934,"identity":"84e8a5c1-0d71-445c-b944-5bc716b0089a","added_by":"auto","created_at":"2026-02-10 10:22:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2298163,"visible":true,"origin":"","legend":"","description":"","filename":"MNRECG.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7442470/v1_covered_a45d1531-dce9-4b49-be6c-7c5fe7fecacb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-Noise Removal from ECG Signals Using Residual Block-Based Generative Adversarial Networks with Customized Loss Function","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"signal-image-and-video-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sivp","sideBox":"Learn more about [Signal, Image and Video Processing](http://link.springer.com/journal/11760)","snPcode":"11760","submissionUrl":"https://submission.nature.com/new-submission/11760/3","title":"Signal, Image and Video Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Biomedical signal processing, multi-noise removal, Noise reduction, Electrocardiogram signal, Generative Adversarial Networks, Residual networks","lastPublishedDoi":"10.21203/rs.3.rs-7442470/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7442470/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The field of biomedical signal processing has witnessed significant advancements, particularly in the domain of electrocardiogram (ECG) denoising. Existing methods often lack consideration of both local and global feature differences, have limited applicability to various noise types, and can introduce signal distortion after denoising. In our research, we propose and investigate a multi-noise removal model based on Generative Adversarial Networks (GANs), specifically using residual networks. This enables the generator to learn the distribution of ECG noise through adversarial training against a stacked-layer discriminator, thereby addressing persistent challenges in ECG noise reduction—crucial for accurate diagnosis of cardiac conditions. Extensive experiments were conducted on both intra- and inter-patient paradigms using an ECG arrhythmia dataset from the PhysioNet Database, ensuring a comprehensive evaluation of the model’s performance. 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