Automated Detection of Sleep Apnea via Image-Based Deep Learning Using VGG19 and LSTM Networks

Automated Detection of Sleep Apnea via Image-Based Deep Learning Using VGG19 and LSTM Networks | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 27 May 2025 V1 Latest version Share on Automated Detection of Sleep Apnea via Image-Based Deep Learning Using VGG19 and LSTM Networks Authors : Veeramalla Anitha [email protected] and Suma lakshmi Ch Authors Info & Affiliations https://doi.org/10.22541/au.174835465.51311352/v1 172 views 82 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Sleep apnea, a prevalent sleep disorder characterized by recurrent episodes of upper airway obstruction during sleep, poses significant health risks, including cardiovascular complications, cognitive impairment, and increased mortality. The timely and accurate detection of sleep apnea is crucial for initiating appropriate treatment and mitigating these adverse outcomes. Traditional methods for sleep apnea diagnosis, such as polysomnography, are resource-intensive, time-consuming, and often inconvenient for patients. Consequently, there is a growing need for automated and accessible sleep apnea detection techniques that can be readily deployed in clinical and home settings Deep learning approaches have emerged as promising tools for analyzing physiological signals and identifying complex patterns indicative of sleep apnea. Leveraging the power of deep learning, researchers are developing innovative solutions to improve the accuracy and efficiency of sleep apnea detection, ultimately leading to better patient care and management. This research explores the application of deep learning techniques, specifically Long Short-Term Memory networks and VGG networks, for the automated detection of sleep apnea using physiological signals. The proposed approach aims to leverage the temporal dependencies captured by LSTM networks and the feature extraction capabilities of VGG networks to develop a robust and accurate sleep apnea detection system. The utilization of wearable sensor data presents a non-invasive and convenient method for monitoring individuals, athletes, and high-risk patients in real-time. Supplementary Material File (submitted copy.docx) Download 238.63 KB Information & Authors Information Version history V1 Version 1 27 May 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords clinical decision support convolutional neural networks (cnn) deep learning feature extraction long short-term memory (lstm) Authors Affiliations Veeramalla Anitha [email protected] Koneru Lakshmaiah Education Foundation View all articles by this author Suma lakshmi Ch Koneru Lakshmaiah Education Foundation View all articles by this author Metrics & Citations Metrics Article Usage 172 views 82 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Veeramalla Anitha, Suma lakshmi Ch. Automated Detection of Sleep Apnea via Image-Based Deep Learning Using VGG19 and LSTM Networks. Authorea . 27 May 2025. DOI: https://doi.org/10.22541/au.174835465.51311352/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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