Identification of Pulmonary Disorders by Using Different Spectral Analysis Methods

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dc.contributor.authorGogus, F. Z.
dc.contributor.authorKarlik, B.
dc.contributor.authorHarman, G.
dc.date.accessioned2020-03-26T19:24:36Z
dc.date.available2020-03-26T19:24:36Z
dc.date.issued2016
dc.departmentSelçuk 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.doi10.1080/18756891.2016.1204110en_US
dc.identifier.endpage611en_US
dc.identifier.issn1875-6891en_US
dc.identifier.issn1875-6883en_US
dc.identifier.issue4en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage595en_US
dc.identifier.urihttps://dx.doi.org/10.1080/18756891.2016.1204110
dc.identifier.urihttps://hdl.handle.net/20.500.12395/33687
dc.identifier.volume9en_US
dc.identifier.wosWOS:000379938400001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherATLANTIS PRESSen_US
dc.relation.ispartofINTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectArtificial Neural Networken_US
dc.subjectClassification Accuracyen_US
dc.subjectFeature Extractionen_US
dc.subjectPower Spectrum Densityen_US
dc.subjectSpectral Analysisen_US
dc.titleIdentification of Pulmonary Disorders by Using Different Spectral Analysis Methodsen_US
dc.typeArticleen_US

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