@inproceedings{27440c06530e4a748df58bb35e5b010b,
title = "Saǧlikli ve saǧliksiz akciǧer seslerinin dalgacik katsayilari kullanilarak siniflandirilmasi",
abstract = "Auscultation and analysing of lung sound is widely used in clinical area for diagnosis of lung diseases. Due to the non-stationary nature of lung sounds conventional frequency analysis technique is not a successful method for respiratory sound analysis. In this paper, classification of normal and abnormal lung sound using wavelet coefficient intended. Respiratory sounds are decomposed into the frequency subbands using wavelet transform and a set of statistical features are inspected from the sub-bands. Then, lung sounds classified as normal and abnormal using these statistical features. Artificial neural network and support vector machine are used for classification process.",
keywords = "artificial neural network, respiratory sounds, support vector machine, wavelet coefficient",
author = "Sinem Uysal and Husamettin Uysal and Bulent Bolat and Tulay Yildirim",
year = "2014",
doi = "10.1109/SIU.2014.6830685",
language = "T{\"u}rk{\c c}e",
isbn = "9781479948741",
series = "2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings",
publisher = "IEEE Computer Society",
pages = "2138--2141",
booktitle = "2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings",
address = "United States",
note = "2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 ; Conference date: 23-04-2014 Through 25-04-2014",
}
