Identification of Pulmonary Disorders by Using Different Spectral Analysis Methods

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dc.authorid3465en_US
dc.authorid183800en_US
dc.authorid136770en_US
dc.contributor.authorKarlık, Bekir
dc.contributor.authorGöğüş, Fama Zehra
dc.contributor.authorHarman, Güneş
dc.date.accessioned2019-07-22T06:49:47Z
dc.date.available2019-07-22T06:49:47Z
dc.date.issued2016
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description.abstractThis study presents detection of pulmonary disorders using different spectral analysis methods such as fast Fourier transform, autoregressive and the autoregressive moving average. Power spectral densities of the sounds were estimated through these methods. Feature vectors were constructed by extracting statistical features from the PSDs. Created feature vectors were used as inputs into the artificial neural networks. Then performances of spectral analysis methods were compared according to classification accuracies, sensitivities and specificities. In this aspect, the study is a comparative study of different spectral analysis methods.en_US
dc.identifier.doi10.1080/18756891.2016.1204110
dc.identifier.issn1875-6891
dc.identifier.scopus2-s2.0-84980000573en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1080/18756891.2016.1204110
dc.identifier.wosWOS:000379938400001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherATLANTIS PRESStr_TR
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.subjectArtificial Neural Networktr_TR
dc.subjectClassification Accuracytr_TR
dc.subjectFeature Extractiontr_TR
dc.subjectPower Spectrum Densitytr_TR
dc.subjectSpectral Analysistr_TR
dc.titleIdentification of Pulmonary Disorders by Using Different Spectral Analysis Methodsen_US
dc.typeArticleen_US

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