Adaptive Toeplitz Convolution- enhanced Classifier for Anomaly Detection in ECG Big Data

Adaptive Toeplitz Convolution- enhanced Classifier for Anomaly Detection in ECG Big Data | 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 Adaptive Toeplitz Convolution- enhanced Classifier for Anomaly Detection in ECG Big Data Lili Wu, Majid Khan Majahar Ali, Tao Li, Chenmin Ni, Ying Tian, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4683990/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Mar, 2025 Read the published version in Journal of Big Data → Version 1 posted 9 You are reading this latest preprint version Abstract The anomaly detection of electrocardiogram (ECG) data is crucial for identifying deviations from normal heart rhythm patterns and providing timely interventions for high-risk patients. Various autoencoder (AE) models within machine learning (ML) have been proposed for this task. However, these models often do not explicitly consider the specific patterns in ECG time series, thereby impacting their learning efficiency. In contrast, we adopt a method based on prior knowledge of ECG time series shapes, employing multi-stage preprocessing, adaptive convolution kernels, and Toeplitz matrices to replace the encoding part of the AE. This approach combines inherent ECG features with the symmetry of Toeplitz matrices, effectively extracting features from ECG signals and reducing dimensionality. Our model consistently outperforms state-of-the-art models in anomaly detection, achieving an overall accuracy exceeding 99.6%, with Precision and Area Under the Receiver Operating Characteristic Curve (AUC) reaching 99.8%, and Recall peaking at 99.9%. Moreover, the runtime is significantly reduced. These results demonstrate that our technique effectively detects anomalies through automatic feature extraction and enhances detection performance on the ECG5000 dataset, a benchmark collection of heartbeat signals. ECG Time Series Toeplitz Convolution Classifier Anomaly Detection Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 05 Mar, 2025 Read the published version in Journal of Big Data → Version 1 posted Editorial decision: Revision requested 24 Nov, 2024 Reviews received at journal 11 Nov, 2024 Reviews received at journal 06 Nov, 2024 Reviewers agreed at journal 04 Nov, 2024 Reviewers agreed at journal 21 Oct, 2024 Reviewers invited by journal 22 Jul, 2024 Editor assigned by journal 17 Jul, 2024 Submission checks completed at journal 05 Jul, 2024 First submitted to journal 04 Jul, 2024 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-4683990","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":332561295,"identity":"fb381593-aad6-4974-8e1a-22e369aa7c18","order_by":0,"name":"Lili 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