PSGMiner: A modular software for polysomnographic analysis

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dc.authoridumut, ilhan/0000-0002-5269-1128
dc.authorwosidumut, ilhan/A-2772-2017
dc.contributor.authorUmut, Ilhan
dc.date.accessioned2024-06-12T10:58:20Z
dc.date.available2024-06-12T10:58:20Z
dc.date.issued2016
dc.departmentTrakya Üniversitesien_US
dc.description.abstractPurpose: Sleep disorders affect a great percentage of the population. The diagnosis of these disorders is usually made by polysomnography. This paper details the development of new software to carry out feature extraction in order to perform robust analysis and classification of sleep events using polysomnographic data. The software, called PSGMiner, is a tool, which visualizes, processes and classifies bioelectrical data. The purpose of this program is to provide researchers with a platform with which to test new hypotheses by creating tests to check for correlations that are not available in commercially available software. The software is freely available under the GPL3 License. Method: PSGMiner is composed of a number of diverse modules such as feature extraction, annotation, and machine learning modules, all of which are accessible from the main module. Using the software, it is possible to extract features of polysomnography using digital signal processing and statistical methods and to perform different analyses. The features can be classified through the use of five classification algorithms. PSGMiner offers an architecture designed for integrating new methods. Comparison with existing methods: Automatic scoring, which is available in almost all commercial PSG software, is not inherently available in this program, though it can be implemented by two different methodologies (machine learning and algorithms). While similar software focuses on a certain signal or event composed of a small number of modules with no expansion possibility, the software introduced here can handle all polysomnographic signals and events. Conclusions: The software simplifies the processing of polysomnographic signals for researchers and physicians that are not experts in computer programming. It can find correlations between different events which could help predict an oncoming event such as sleep apnea. The software could also be used for educational purposes. (C) 2016 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.compbiomed.2016.03.023
dc.identifier.endpage9en_US
dc.identifier.issn0010-4825
dc.identifier.issn1879-0534
dc.identifier.pmid27058436en_US
dc.identifier.scopus2-s2.0-84962745749en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1016/j.compbiomed.2016.03.023
dc.identifier.urihttps://hdl.handle.net/20.500.14551/20019
dc.identifier.volume73en_US
dc.identifier.wosWOS:000378455700001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofComputers In Biology And Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectComputer Softwareen_US
dc.subjectDigital Signal Processingen_US
dc.subjectMachine Learningen_US
dc.subjectPolysomnographyen_US
dc.subjectHeart-Rate-Variabilityen_US
dc.subjectOpen Source Toolboxen_US
dc.subjectClassificationen_US
dc.subjectProgramen_US
dc.titlePSGMiner: A modular software for polysomnographic analysisen_US
dc.typeArticleen_US

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