Abnormal Heart Sound Recognition using SVM and LSTM Models in Real-time Mode

Abnormal Heart Sound Recognition using SVM and LSTM Models in Real-time Mode | 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 Abnormal Heart Sound Recognition using SVM and LSTM Models in Real-time Mode Moy'awiah A. Al-Shannaq, Areen Nasrawi, Abed Al-Raouf Bsoul, Ahmad A. Saifan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4673107/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Cardiovascular diseases are non-communicable diseases that are considered the leading cause of death worldwide accounting for 17.9 million fatalities. Auscultation of heart sounds is the most common and valuable way of diagnosing heart diseases. Normal heart sounds have a special rhythmic pattern as an indicator of heart integrity. Many experts concentrate on diagnosing the heart by automatic digital auscultation systems which find various distinguishable characteristics for heart sound classifications. This can decrease the mortality rate for cardiovascular diseases and enhance the patient’s quality of life. This study aims to propose a real-time heart sound recognition system to classify both normal and abnormal phonocardiograms with the ability to define the abnormality type if existed. Digital signal processing methods, by applying the fast Fourier transform, filtering techniques, and the dual-tree complex wavelet transform, with machine learning classification algorithms are employed to segment the input phonocardiogram signal, extract meaningful features, and find the appropriate class for the input signal. We utilized three datasets, the PhysioNet of 1,395, the GitHub of 800, and the PASCAL of 100 files segmented into three cardiac cycles. The proposed solution relies on the support vector machine and the long-short term memory neural network to distinguish between normal and abnormal heartbeat sounds and to recognize the type of abnormality (in the case distinguished) respectively. The results show that the proposed approach for normal/abnormal classification achieves an overall accuracy of 96.0% and 98.1%, sensitivity of 94.4% and 84.2%, and specificity of 64.9% and 98.4% for two and one support vector machines respectively among the state-of-the-art solutions. The long short-term memory model is also a well-known efficient classifier for temporal data, and the results show the accuracy of 99.2%, 99.5%, 98.6%, and 99.4% for four, five, six, and seven classes. Furthermore, we found an efficient automatic segmentation method that was tested with the PASCAL database achieving a total error of 867,525.6 and 23,590.3 for datasets A and B respectively, with a computational time of 0.04 seconds to segment one cardiac cycle. Deep Learning Heart Sound Long short-term memory Machine Learning Phonocardiogram Segmentation Support Vector Machine Classification Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 17 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 30 Aug, 2024 Reviews received at journal 24 Aug, 2024 Reviewers agreed at journal 14 Aug, 2024 Reviews received at journal 09 Aug, 2024 Reviewers agreed at journal 09 Aug, 2024 Reviewers invited by journal 09 Aug, 2024 Editor assigned by journal 09 Aug, 2024 Editor invited by journal 08 Jul, 2024 Submission checks completed at journal 05 Jul, 2024 First submitted to journal 02 Jul, 2024 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-4673107","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":332651700,"identity":"7ebab2d2-f63b-47fa-96c4-82abcd298d3c","order_by":0,"name":"Moy'awiah A. 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Auscultation of heart sounds is the most common and valuable way of diagnosing heart diseases. Normal heart sounds have a special rhythmic pattern as an indicator of heart integrity. Many experts concentrate on diagnosing the heart by automatic digital auscultation systems which find various distinguishable characteristics for heart sound classifications. This can decrease the mortality rate for cardiovascular diseases and enhance the patient\u0026rsquo;s quality of life. This study aims to propose a real-time heart sound recognition system to classify both normal and abnormal phonocardiograms with the ability to define the abnormality type if existed. Digital signal processing methods, by applying the fast Fourier transform, filtering techniques, and the dual-tree complex wavelet transform, with machine learning classification algorithms are employed to segment the input phonocardiogram signal, extract meaningful features, and find the appropriate class for the input signal. We utilized three datasets, the PhysioNet of 1,395, the GitHub of 800, and the PASCAL of 100 files segmented into three cardiac cycles. The proposed solution relies on the support vector machine and the long-short term memory neural network to distinguish between normal and abnormal heartbeat sounds and to recognize the type of abnormality (in the case distinguished) respectively. The results show that the proposed approach for normal/abnormal classification achieves an overall accuracy of 96.0% and 98.1%, sensitivity of 94.4% and 84.2%, and specificity of 64.9% and 98.4% for two and one support vector machines respectively among the state-of-the-art solutions. The long short-term memory model is also a well-known efficient classifier for temporal data, and the results show the accuracy of 99.2%, 99.5%, 98.6%, and 99.4% for four, five, six, and seven classes. Furthermore, we found an efficient automatic segmentation method that was tested with the PASCAL database achieving a total error of 867,525.6 and 23,590.3 for datasets A and B respectively, with a computational time of 0.04 seconds to segment one cardiac cycle.\u003c/p\u003e","manuscriptTitle":"Abnormal Heart Sound Recognition using SVM and LSTM Models in Real-time Mode","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-30 18:36:18","doi":"10.21203/rs.3.rs-4673107/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-30T05:54:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-24T10:23:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"139107219089232821405945703396197085447","date":"2024-08-15T02:57:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-09T08:01:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"217967215324997108270446062088656327242","date":"2024-08-09T07:25:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-09T06:48:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-09T06:46:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-08T10:01:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-05T08:24:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-07-02T09:28:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c813825e-3a95-4e86-a146-8e82cfd81fce","owner":[],"postedDate":"July 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-24T16:10:21+00:00","versionOfRecord":{"articleIdentity":"rs-4673107","link":"https://doi.org/10.1038/s41598-025-89647-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-03-17 15:56:58","publishedOnDateReadable":"March 17th, 2025"},"versionCreatedAt":"2024-07-30 18:36:18","video":"","vorDoi":"10.1038/s41598-025-89647-0","vorDoiUrl":"https://doi.org/10.1038/s41598-025-89647-0","workflowStages":[]},"version":"v1","identity":"rs-4673107","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4673107","identity":"rs-4673107","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}