Development of SA-LightCS for Lightweight ECG Signal Reconstruction Using Compressive Sensing

Development of SA-LightCS for Lightweight ECG Signal Reconstruction Using Compressive Sensing | 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 Development of SA-LightCS for Lightweight ECG Signal Reconstruction Using Compressive Sensing Yanming Hu, Zhensong Li, Yuling Feng, Junchen Dong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6439209/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 ECG monitoring systems generate extensive data, challenging storage and transmission in resource-constrained environments. While compressed sensing offers data reduction potential, existing approaches struggle to balance computational efficiency with signal fidelity. Our SA-LightCS framework addresses these limitations through three innovations: depthwise separable convolutions reducing parameters by 63.5%, parallel residual structures preserving multi-scale features, and self-attention mechanisms capturing global signal dependencies. Tested on MIT-BIH datasets, our model outperforms conventional methods (CoSaMP, SOMP) and recent deep learning approaches (CAE, CSNet), achieving 12.9% lower PRD and 1.99 dB higher SNR at 5% compression while requiring significantly fewer computational resources. SA-LightCS provides an effective solution for real-time ECG monitoring in resource-limited healthcare applications. Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Engineering/Biomedical engineering electrocardiogram signal compressed sensing self-attention mechanism lightweight model depthwise separable convolution 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. 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