Extraction of low-dimensional features for single-channel common lung sound classification

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dc.authoridARAS, Selim/0000-0003-1231-5782
dc.contributor.authorEngin, M. Alptekin
dc.contributor.authorAras, Selim
dc.contributor.authorGangal, Ali
dc.date.accessioned2024-10-04T18:51:00Z
dc.date.available2024-10-04T18:51:00Z
dc.date.issued2022
dc.departmentBayburt Üniversitesien_US
dc.description.abstractIn this study, feature extraction methods used in the classification of single-channel lung sounds obtained by automatic identification of respiratory cycles were examined in detail in order to extract distinctive features at the lowest size. In this way, it will be possible to design a system for the detection of lung diseases, completely autonomously. In the study, automatic separation and classification of 400 respiratory cycles were performed from the single-channel common lung sounds obtained from 94 people. Leave one out cross validation (LOOCV) was used for the calibration and validation of the classification model. The Mel frequency cepstrum coefficients (MFCC), time domain features, frequency domain features, and linear predictive coding (LPC) were used for classification. The performance of the features was tested using linear discriminant analysis (LDA), k-nearest neighbors (k-NN), support vector machines (SVM), and naive Bayes (NB) classification algorithms. The success of combinations of features was explored and enhanced using the sequential forward selection (SFS). As a result, the best accuracy (90.14% in the training set and 90.63% in the test set) was acquired using the k-NN for the triple combination, which included the standard deviation of LPC and the standard deviation and the mean of MFCC.en_US
dc.description.sponsorshipTUBITAK (The Scientific and Technological Research Council of Turkey) [116E003]en_US
dc.description.sponsorshipThe authors would like to thank TUBITAK (The Scientific and Technological Research Council of Turkey) for supporting this work with project number 116E003.en_US
dc.identifier.doi10.1007/s11517-022-02552-w
dc.identifier.endpage1568en_US
dc.identifier.issn0140-0118
dc.identifier.issn1741-0444
dc.identifier.issue6en_US
dc.identifier.pmid35378678en_US
dc.identifier.scopus2-s2.0-85127575688en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1555en_US
dc.identifier.urihttps://doi.org/10.1007/s11517-022-02552-w
dc.identifier.urihttp://hdl.handle.net/20.500.12403/3319
dc.identifier.volume60en_US
dc.identifier.wosWOS:000778055300001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofMedical & Biological Engineering & Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLung soundsen_US
dc.subjectRespiratory cycleen_US
dc.subjectAutomatic recognitionen_US
dc.subjectFeature extractionen_US
dc.subjectClassificationen_US
dc.subjectSequential forward selectionen_US
dc.titleExtraction of low-dimensional features for single-channel common lung sound classificationen_US
dc.typeArticleen_US

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