Deep learning method for epilepsy detection using embedded zero tree wavelet transform and FRCNN-AIV3 classifier

Deep learning method for epilepsy detection using embedded zero tree wavelet transform and FRCNN-AIV3 classifier | 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 Deep learning method for epilepsy detection using embedded zero tree wavelet transform and FRCNN-AIV3 classifier P. Padmapriya, V. Rajamani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7485527/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 The protracted diagnostic process for Epilepsy, a neurological illness that is now incurable, begins in childhood, when the symptoms first appear. Early diagnosis is crucial in reducing adverse outcomes, as it allows for the immediate initiation of compensatory instruction. Timely action is crucial in resolving these situations due to the seriousness of the situation and the potential for severe consequences. Deep Learning (DL), a novel method of soft computing, has proven beneficial in various fields, including pattern recognition and healthcare diagnostics. The use of deep learning techniques in the treatment of neurological and neuropsychiatric diseases, particularly Epilepsy, is the subject of this study's in-depth investigation. This research delves into the inner workings of DL algorithms and how they diagnose various neurological illnesses in humans. Epilepsy problems can be detected using electroencephalogram (EEG) data. To remove extraneous data and analyze the same data in both temporal and frequency domains, the EEG dataset is segmented and filtered. The next step is to decompose the EEG signals into their component bands and extract characteristics from them using the Embedded Zero tree Wavelet (EZW) Transform. Afterwards, five statistical methods are used to extract information from these EEG sub-bands: mean, variance, entropy, skewness, and kurtosis. Several classifiers, such as the Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN) with Visual Geometry Group (VGG) net (CNN-VGG Net), and Support Vector Machine (SVM) enhanced with Feed Forward (FF) Neural Network (SVM-FF), are fed these extracted features. For feature categorisation according to their respective classes, a sophisticated deep learning approach is applied, specifically the CNN with optimised Adam optimiser-optimised Inception V3 architecture (CNN-AVI3). Notably, the suggested model obtains an outstanding accuracy rate of 97.28% by combining EWZ and CNN-AVI3. In addition, it surpasses current state-of-the-art techniques with a specificity of 97.86%, an F-score of 93.74%, a recall of 95.14%, and an accuracy of 92.39%. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Physical sciences/Engineering Physical sciences/Mathematics and computing Health sciences/Neurology Biological sciences/Neuroscience Adam optimizer Convolutional Neural Network Deep learning techniques Electroencephalogram Embedded Zero tree Wavelet Epilepsy Inception Support Vector Machine 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. 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-7485527","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":513217615,"identity":"074a8101-2dc6-4897-afcb-dc1f5b3e701e","order_by":0,"name":"P. 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The next step is to decompose the EEG signals into their component bands and extract characteristics from them using the Embedded Zero tree Wavelet (EZW) Transform. Afterwards, five statistical methods are used to extract information from these EEG sub-bands: mean, variance, entropy, skewness, and kurtosis. Several classifiers, such as the Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN) with Visual Geometry Group (VGG) net (CNN-VGG Net), and Support Vector Machine (SVM) enhanced with Feed Forward (FF) Neural Network (SVM-FF), are fed these extracted features. For feature categorisation according to their respective classes, a sophisticated deep learning approach is applied, specifically the CNN with optimised Adam optimiser-optimised Inception V3 architecture (CNN-AVI3). Notably, the suggested model obtains an outstanding accuracy rate of 97.28% by combining EWZ and CNN-AVI3. 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