Classification of Normal and Abnormal Lung Sounds Using Wavelet Coefficients

被引：0
|
作者：
Uysal, Sinem [1 ]
Uysal, Husamettin [1 ]
Bolat, Bulent [1 ]
Yildirim, Tulay [1 ]
机构：
[1] Yildiz Tekn Univ, Elekt & Haberlesme Muhendisligi Bolumu, Istanbul, Turkey
来源：
2014 22ND SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU) | 2014年
关键词：
respiratory sounds; wavelet coefficient; artificial neural network; support vector machine;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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 sub-bands 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.
引用
收藏
页码：2138 / 2141
页数：4
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