{"paper_id":"2b8f92fa-9d92-4a96-8df1-e3b9589b0e7a","body_text":"Detection of Alzheimer and Mild Cognitive Impairment Patients by Poincare and Entropy Methods based on Electroencephalography 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 Detection of Alzheimer and Mild Cognitive Impairment Patients by Poincare and Entropy Methods based on Electroencephalography Signals Umut Aslan, Mehmet Feyzi Akşahin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3797783/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Apr, 2025 Read the published version in BioMedical Engineering OnLine → Version 1 posted 9 You are reading this latest preprint version Abstract Alzheimer's disease (AD) is characterized by deficits in cognition, behavior, and intellectual functioning, and Mild Cognitive Impairment (MCI) refers to individuals whose cognitive impairment deviates from what is expected for their age but does not significantly interfere with daily activities. Because there is no treatment for AD, early prediction of AD can be helpful to reducing the progression of this disease. This study examines the Electroencephalography (EEG) signal of 3 distinct groups including AD, MCI, and healthy individuals. Recognizing the non-stationary nature of EEG signals, two nonlinear approaches, Poincare and Entropy, are employed for meaningful feature extraction. To extract features from EEG signal, data should segmented into epochs and for each one, feature extraction approaches are implemented. The obtained features are given to machine learning algorithms to classify the subjects. Extensive experiments were conducted to analyze the features comprehensively The results demonstrate that, our proposed method surpasses previous studies in terms of accuracy, sensitivity, and specificity, indicating its effectiveness in classifying individuals with AD, MCI, and those without cognitive impairment. Electroencephalography Alzheimer Mild cognitive impairment Entropy Poincare Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 25 Apr, 2025 Read the published version in BioMedical Engineering OnLine → Version 1 posted Editorial decision: Revision requested 09 Apr, 2024 Reviews received at journal 01 Apr, 2024 Reviews received at journal 06 Feb, 2024 Reviewers agreed at journal 13 Jan, 2024 Reviewers agreed at journal 11 Jan, 2024 Reviewers invited by journal 11 Jan, 2024 Editor assigned by journal 28 Dec, 2023 Submission checks completed at journal 28 Dec, 2023 First submitted to journal 23 Dec, 2023 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. 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