Distinctive features for classification of respiratory sounds between normal and crackles using cepstral coefficients
Classification of respiratory sounds between normal and abnormal is very crucial for screening and diagnosis purposes. Lung associated diseases can be detected through this technique. With the advancement of computerized auscultation technology, the adventitious sounds such as crackles can be d...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
IEEE
2018
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/67988/ http://irep.iium.edu.my/67988/ http://irep.iium.edu.my/67988/ http://irep.iium.edu.my/67988/7/67988%20Distinctive%20Features%20for%20Classification%20of.pdf http://irep.iium.edu.my/67988/13/67988%20Distinctive%20Features%20for%20Classification%20_scopus.pdf |
| Summary: | Classification of respiratory sounds between
normal and abnormal is very crucial for screening and diagnosis
purposes. Lung associated diseases can be detected through this
technique. With the advancement of computerized auscultation
technology, the adventitious sounds such as crackles can be
detected and therefore diagnostic test can be performed earlier.
In this paper, Mel-frequency Cepstral Coefficient (MFCC) is
used to extract features from normal and crackles respiratory
sounds. By using statistical computation such as mean and
standard deviation (SD) of cepstral based coefficients it can
differentiate between crackles and normal sounds. The result
shows that the first three statistical values of SD of coefficients
provide distinctive feature between normal and crackles
respiratory sounds. Hence, MFCCs can be used as feature
extraction method of respiratory sounds to classify between
normal and crackles as screening and diagnostic tool. |
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