MATEC Web Conf.
Volume 154, 2018The 2nd International Conference on Engineering and Technology for Sustainable Development (ICET4SD 2017)
|Number of page(s)||4|
|Section||Engineering and Technology|
|Published online||28 February 2018|
Fractality evaluation for pulmonary crackle sound using the Degree of Self-Similarity
Universitas Gadjah Mada, Department of Electrical Engineering and Information Technology, 55281, Yogyakarta, Indonesia
2 Telkom University, School of Electrical Engineering, 40287, Bandung, Indonesia
* Corresponding author: email@example.com
Lung sound is a complex signal produced by the respiratory process. The complex signal has several properties including a chaotic behavior, fractality or self-similarity property. One of lung sounds that arise from abnormalities occurred in the respiratory tract is pulmonary crackle sound. In this study, we tested the degree of self-similarity of pulmonary crackle sound and examined whether the degree of similarity can be used as a feature to differentiate the pulmonary lung crackle sound with normal lung sound. The results showed the sufficient strength of the self-similarity nature of the pulmonary crackle sound. Meanwhile, a test using K-mean clustering produced an accuracy of 87.5% to differentiate between the pulmonary crackle sound and normal lung sound. It can be stated then that it is deemed important to take another feature to obtain higher accuracy. The high self-similarity degree indicates that a pulmonary crackle sound has fractals properties.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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