Artificial neural network-based automatic detection of food intake via heart rate variability signal

Artificial neural network-based automatic detection of food intake via heart rate variability signal | 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 Artificial neural network-based automatic detection of food intake via heart rate variability signal M. Khawar Ali, Tong Zhou, Lei Guo, Qi Lin, Jiande DZ Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3943447/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 Intestinal electrical stimulation (IES) and vagus nerve stimulation have been proposed for the treatment of obesity and diabetes. The treatment can be improved if the stimulations are applied immediately after the food intake. The purpose of this study was to develop and enhance the automated food intake detection system using dynamic analysis of heart rate variability via artificial neural network (ANN). The ECG signal was recorded from 34 healthy subjects for 20 min each during following four events: sitting silently, reading, watching emotional movie, intaking food. The HRV parameters were generated from the recorded ECG signal and used to train and test as well as to optimize the ANN for the detection of food intake event. The results of Leave One Subject Out-Leave One Out (LOSO-LOO) with linear, tanh and ReLU were compared in the first step. The best ANN was tested for optimization by removing the input HRV parameters with mutual information score of less than 0.01 and with a decreased number of neurons in the hidden layer. LOSO-LOO, Leave One Subject Out (LOSO), Support Vector Machine (SVM) and Random Forest (RF) algorithms were also compared to identify the best ANN for automatic detection of food intake. The results indicated that (i) tanh algorithm outperformed both the linear and ReLU algorithms. (ii) Removing the input features with low mutual information score (<0.01) increased the performance of the ANN. (iii) The performance of ANN improved further by decreasing the number of neurons in the hidden layer from 10 to 8. (iv) LOSO outperformed both SVM and RF methods. However, LOSO-LOO was even better than LOSO in terms of sensitivity. In conclusion, the ANN using LOSO-LOO with 8 neurons in the hidden layer and 11 HRV features can be used to effectively detect food intake and may be used in a real-time IES system for treating obesity and diabetes. Physical sciences/Engineering/Biomedical engineering Biological sciences/Biological techniques/Software 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-3943447","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":287664739,"identity":"9b13d218-acd6-4a78-ac59-c3f6abfb81ed","order_by":0,"name":"M. 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The treatment can be improved if the stimulations are applied immediately after the food intake. The purpose of this study was to develop and enhance the automated food intake detection system using dynamic analysis of heart rate variability via artificial neural network (ANN). The ECG signal was recorded from 34 healthy subjects for 20 min each during following four events: sitting silently, reading, watching emotional movie, intaking food. The HRV parameters were generated from the recorded ECG signal and used to train and test as well as to optimize the ANN for the detection of food intake event. The results of Leave One Subject Out-Leave One Out (LOSO-LOO) with linear, tanh and ReLU were compared in the first step. The best ANN was tested for optimization by removing the input HRV parameters with mutual information score of less than 0.01 and with a decreased number of neurons in the hidden layer. LOSO-LOO, Leave One Subject Out (LOSO), Support Vector Machine (SVM) and Random Forest (RF) algorithms were also compared to identify the best ANN for automatic detection of food intake. The results indicated that (i) tanh algorithm outperformed both the linear and ReLU algorithms. (ii) Removing the input features with low mutual information score (\u003c0.01) increased the performance of the ANN. (iii) The performance of ANN improved further by decreasing the number of neurons in the hidden layer from 10 to 8. (iv) LOSO outperformed both SVM and RF methods. However, LOSO-LOO was even better than LOSO in terms of sensitivity. In conclusion, the ANN using LOSO-LOO with 8 neurons in the hidden layer and 11 HRV features can be used to effectively detect food intake and may be used in a real-time IES system for treating obesity and diabetes.","manuscriptTitle":"Artificial neural network-based automatic detection of food intake via heart rate variability signal","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-05 08:25:52","doi":"10.21203/rs.3.rs-3943447/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8b518975-db14-4c73-8970-1a2bd0150844","owner":[],"postedDate":"April 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":30286540,"name":"Physical sciences/Engineering/Biomedical engineering"},{"id":30286541,"name":"Biological sciences/Biological techniques/Software"}],"tags":[],"updatedAt":"2024-07-18T05:36:06+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-05 08:25:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3943447","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3943447","identity":"rs-3943447","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}