@inproceedings{serbes_automated_2017,
	address = {Singapore},
	title = {An {Automated} {Lung} {Sound} {Preprocessing} and {Classification} {System} {Based} {OnSpectral} {Analysis} {Methods}},
	isbn = {978-981-10-7419-6},
	doi = {10.1007/978-981-10-7419-6_8},
	abstract = {In this work, respiratory sounds are classified into four classes in the presence of various noises (talking, coughing, motion artefacts, heart and intestinal sounds) using support vector machine classifier with radial basis function kernel. The four classes can be listed as normal, wheeze, crackle and crackle plus wheeze. Crackle and wheeze adventitious sounds have opposite behavior in the time-frequency domain. In order to better represent and resolve the discriminative characteristics of adventitious sounds, non-linear novel spectral feature extraction algorithms are proposed to be employed in four class classification problem. The proposed algorithm, which has achieved 49.86\% accuracy on a very challenging and rich dataset, is a promising tool to be used as preprocessor in lung disease decision support systems.},
	language = {en},
	booktitle = {International {Conference} on {Biomedical} and {Health} {Informatics}},
	publisher = {Springer},
	author = {Serbes, Gorkem and Ulukaya, Sezer and Kahya, Yasemin P.},
	editor = {Maglaveras, Nicos and Chouvarda, Ioanna and de Carvalho, Paulo},
	year = {2017},
	keywords = {\#nosource},
	pages = {45--49},
}

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