Research on the Analysis Method of Mental Fatigue in Medical Personnel Based on EEG Signals

Research on the Analysis Method of Mental Fatigue in Medical Personnel Based on EEG Signals | 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 Research on the Analysis Method of Mental Fatigue in Medical Personnel Based on EEG Signals First Zelong Chen, Second Zhenchang Zhang, zhanghuaiyong Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3848113/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 Study the correlation between the multi-scale entropy values of electroencephalo-gram (EEG) signals in medical personnel and the degree of mental fatigue, and analyze the detection of mental fatigue status in healthcare workers. Utilizing singular value analysis to eliminate noise in EEG signal data, calculating multi-scale entropy values of the original EEG signals, referencing the FS-4 fatigue scale, and analyzing entropy value stability; proposing three fatigue analysis algorithms?random forest, BP neural network, and XGBoost comparing them with traditional machine learning methods and artificial neural network methods , investigating the relationship between multi-scale entropy values of EEG 1 signals and fatigue in healthcare personnel, and analyzing the determination of mental fatigue status. This paper employs the multi-scale entropy method to extract multi-scale information from original EEG signals. The combination of multi-scale information with three fatigue analysis algorithms enables the detection and analysis of mental fatigue status in healthcare personnel. Experimental results indicate that the XGBoost algorithm exhibits the highest sensitivity at 75%, while the random forest algorithm demonstrates the best specificity at 80% and an AUC value of 95%. The random forest algorithm demonstrates superior performance in studying mental fatigue. Electroencephalography Fatigue Random forest BP neural network Singular value 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-3848113","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":266210444,"identity":"35e19e3e-650d-498e-8737-f64251455dc6","order_by":0,"name":"First Zelong Chen","email":"","orcid":"","institution":"No. 900 Hospital of Joint Logistics Support Force","correspondingAuthor":false,"prefix":"","firstName":"First","middleName":"Zelong","lastName":"Chen","suffix":""},{"id":266210445,"identity":"cb293343-10b2-495f-a02a-713bc19ebcef","order_by":1,"name":"Second Zhenchang Zhang","email":"","orcid":"","institution":"Fujian Agriculture and 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