StellarisPat: A Novel Signum and LU-Ternary Feature Extraction Approach for Speech-Based Parkinson’s Disease Detection

StellarisPat: A Novel Signum and LU-Ternary Feature Extraction Approach for Speech-Based Parkinson’s Disease Detection | 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 StellarisPat: A Novel Signum and LU-Ternary Feature Extraction Approach for Speech-Based Parkinson’s Disease Detection BURAK CELİK, AYHAN AKBAL This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6831689/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 Parkinson’s disease classification (PDC) is a critical research area in digital health and machine learning. This study introduces a new textural feature extractor, StellarisPat, which leverages signum and ternary kernels for feature extraction. A multi-leveled feature extraction approach is employed, where levels are generated using multi level discrete wavelet transform (MDWT). StellarisPat extracts features from each level, and the most discriminative features are selected using a hybrid feature selector that combines neighborhood component analysis (NCA) and chi-square (Chi2) methods, termed NCA-Chi2. A PDC dataset was collected, comprising speech signals from Parkinson’s patients (Med On and Med Off) and healthy controls, with a total of three classes. Evaluation of the proposed technique was carried out using a two shallow classifiers: k-nearest neighbors and support vector machine. Additionally, an iterative majority voting (IMV) is applied for feature fusion to enhance classification performance. The proposed StellarisPat-based hybrid model achieved classification accuracies of 96.09% and 95.22% using k-NN and SVM, respectively, demonstrating its effectiveness for PDC. Parkinson’s disease Speech analysis Machine learning LU-Ternary feature extraction 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-6831689","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":472510862,"identity":"1d7cacac-352d-4fc9-913a-bb701f14ff48","order_by":0,"name":"BURAK CELİK","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIie2RMUvDQBTH3/Hg3RLb1SDUr9DQQYLYfpWWg3RycCk6GShcl4Dr+S3iJ/Ak0Cl0NluzONtNB8F32kmS2LHD/eCO94Yf9+d/AB7PETLkI1K+ekiidgNIt9MBCiFh9KPgXgn+VYAo/FUAOpWL0+t6Z2A8IEnJnbkdn/cRU3hfFDA5s41KbOajMAc14mDrKi9V9LgUqTCbAoLetDnYawLhFuxMo9RVra3IC5HiiWalJRkr+MnKvVNuWJk8O+WrWyEOZqcumHjSdpZzYyg6lDh7o9gMVaSRVGhKpQy/8pJt5kFQtjQmE6wyV1R/He14uHpYrertx+JyILNmZR/vz26h/Vs8Ho/HcwDfSeBUj4RhQrUAAAAASUVORK5CYII=","orcid":"","institution":"Kocaeli University","correspondingAuthor":true,"prefix":"","firstName":"BURAK","middleName":"","lastName":"CELİK","suffix":""},{"id":472510863,"identity":"415340af-351c-4501-bb5f-594b732e29d9","order_by":1,"name":"AYHAN AKBAL","email":"","orcid":"","institution":"Fırat University","correspondingAuthor":false,"prefix":"","firstName":"AYHAN","middleName":"","lastName":"AKBAL","suffix":""}],"badges":[],"createdAt":"2025-06-05 19:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6831689/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6831689/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105775660,"identity":"8489c6f4-2cf8-4356-b735-c532c1a7da91","added_by":"auto","created_at":"2026-03-31 03:11:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1228097,"visible":true,"origin":"","legend":"","description":"","filename":"StellarisPat.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6831689/v1_covered_1c1015aa-6429-4d46-b0fe-41bd420efd5e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"StellarisPat: A Novel Signum and LU-Ternary Feature Extraction Approach for Speech-Based Parkinson’s Disease Detection","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"Parkinson’s disease, Speech analysis, Machine learning, LU-Ternary feature extraction","lastPublishedDoi":"10.21203/rs.3.rs-6831689/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6831689/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eParkinson\u0026rsquo;s disease classification (PDC) is a critical research area in digital health and machine learning. This study introduces a new textural feature extractor, StellarisPat, which leverages signum and ternary kernels for feature extraction. A multi-leveled feature extraction approach is employed, where levels are generated using multi level discrete wavelet transform (MDWT). StellarisPat extracts features from each level, and the most discriminative features are selected using a hybrid feature selector that combines neighborhood component analysis (NCA) and chi-square (Chi2) methods, termed NCA-Chi2. A PDC dataset was collected, comprising speech signals from Parkinson\u0026rsquo;s patients (Med On and Med Off) and healthy controls, with a total of three classes. Evaluation of the proposed technique was carried out using a two shallow classifiers: k-nearest neighbors and support vector machine. Additionally, an iterative majority voting (IMV) is applied for feature fusion to enhance classification performance. The proposed StellarisPat-based hybrid model achieved classification accuracies of 96.09% and 95.22% using k-NN and SVM, respectively, demonstrating its effectiveness for PDC.\u003c/p\u003e","manuscriptTitle":"StellarisPat: A Novel Signum and LU-Ternary Feature Extraction Approach for Speech-Based Parkinson’s Disease Detection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-19 12:00:52","doi":"10.21203/rs.3.rs-6831689/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":"3b1bebc9-818c-471c-946f-b36c84931f80","owner":[],"postedDate":"June 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-31T03:10:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-19 12:00:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6831689","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6831689","identity":"rs-6831689","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}