Lung sound classification using wavelet transform and entropy to detect lung abnormality
Lung sounds provide essential information about the health of the lungs and respiratory tract. They have unique and distinguishable patterns associated with the abnormalities in these organs. Many studies attempted to develop various methods to classify lung sounds automatically. Wavelet tr...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Faculty of Technical Sciences in Cacak
2022-01-01
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| Series: | Serbian Journal of Electrical Engineering |
| Subjects: | |
| Online Access: | http://www.doiserbia.nb.rs/img/doi/1451-4869/2022/1451-48692201079R.pdf |
| Summary: | Lung sounds provide essential information about the health of the lungs and
respiratory tract. They have unique and distinguishable patterns associated
with the abnormalities in these organs. Many studies attempted to develop
various methods to classify lung sounds automatically. Wavelet transform is
one of the approaches widely utilized for physiological signal analysis.
Commonly, wavelet in feature extraction is used to break down the lung
sounds into several sub-bands before calculating some parameters. This study
used five lung sound classes obtained from various sources. Furthermore, the
wavelet analysis process was carried out using Discrete Wavelet Transform
(DWT) and Wavelet Package Decomposition (WPD) analysis and entropy
calculation as feature extraction. In the DWT process, the highest accuracy
obtained was 97.98% using Permutation Entropy (PE), Renyi Entropy (RE), and
Spectral Entropy (SEN). In WPD, the best accuracy achieved is 98.99 % when 8
sub-bands and RE are used. These results are relatively competitive compared
with previous studies using the wavelet method with the same datasets. |
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| ISSN: | 1451-4869 2217-7183 |