Real-Time Prediction and Alarm System for Heart Rate and Blood Pressure: AI- for Anesthesia and Critical Care

Real-Time Prediction and Alarm System for Heart Rate and Blood Pressure: AI- for Anesthesia and Critical Care | 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 Biological Sciences - Article Real-Time Prediction and Alarm System for Heart Rate and Blood Pressure: AI- for Anesthesia and Critical Care Ayang Zhao, Xinzhu Wang, Guibo Fan, Qicheng Lin, Qizhi Zheng, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7260853/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 An intelligent real-time prediction and alarm system that integrates deep learning with mode alarm strategies, improve the accuracy, timeliness, and clinical relevance of physiological monitoring. Traditional monitoring systems often rely on rigid threshold-based alarms that fail to account for dynamic biosignal fluctuations, leading to high false alarm rates and limited adaptability. The system extracts physiological data using optical character recognition (OCR) and standardizes the data to build a training dataset. A deep learning framework incorporating eight candidate models, including RNNs, CNNs, and Transformers, was established, with automatic model selection performed via Optuna and Pareto frontier analysis to identify the optimal configuration. To enhance prediction under limited data conditions, we proposed “pre-learning” and “self-learning” strategies and conducted a systematic analysis of signal volatility’s impact on generalization. The system supports two clinically oriented alarm modes: a fixed-baseline mode for stable conditions and a dynamic-baseline mode for detecting trends and drift. Additionally, a self-optimization module identifies 18 predefined false alarm features and recommends corresponding mitigation strategies through a closed-loop learning pipeline. By integrating multi-model deep learning, adaptive alarm logic, and self-optimization, we developed a flexible and intelligent physiological monitoring system. By tailoring alarm modes to clinical context and dynamically adjusting to signal characteristics, the system offers a reliable solution to enhance perioperative safety. Health sciences/Health care Health sciences/Signs and symptoms Vital Sign Forecasting Deep Learning Real-Time Alarm Signal Volatility Clinical Monitoring System Full Text Additional Declarations There is NO Competing Interest. Supplementary Files realtime.gif Predictive video Appendix.pdf Supplementary documents 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. 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-7260853","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Biological Sciences - Article","associatedPublications":[],"authors":[{"id":494294366,"identity":"60b5aa45-4636-43c2-9884-d2d68f5c124a","order_by":0,"name":"Ayang 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