Application of Clustering Techniques for Lung Sounds to Improve Interpretability and Detection of Crackles
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| Natura: | Artículo científico |
| Lingua: | en |
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Corporación Universitaria de la Costa
2015
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| _version_ | 1876479775458983936 |
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| author | Germán D. Sosa |
| author_facet | Germán D. Sosa |
| contents | Application of Clustering Techniques for Lung Sounds to Improve Interpretability and Detection of Crackles Germán D. Sosa Fabián Velásquez Clavijo Ingeniería Log means Symlet DBSCAN energy Due to the subjectivity involved currently in pulmonary auscultation process and its diagnostic to evaluate the condition of respiratory airways, this work pretends to evaluate the performance of clustering algorithms such as k-means and DBSCAN to perform a computational analysis of lung sounds aiming to visualize a representation of such sounds that highlights the presence of crackles and the energy associated with them. In order to achieve that goal, Wavelet analysis techniques were used in contrast to traditional frequency analysis given the similarity between the typical waveform for a crackle and the wavelet sym4. Once the lung sound signal with isolated crackles is obtained, the clustering process groups crackles in regions of high density and provides visualization that might be useful for the diagnostic made by an expert. Evaluation suggests that k-means groups crackle more effective than DBSCAN in terms of generated clusters. 2015 artículo científico 0122-6517 https://www.redalyc.org/articulo.oa?id=497779324005 en http://www.redalyc.org/revista.oa?id=4977 INGE CUC application/pdf Corporación Universitaria de la Costa INGE CUC (Colombia) Num.1 Vol.11 |
| format | Artículo científico |
| id | redalyc_497779324005 |
| institution | Redalyc |
| language | en |
| publishDate | 2015 |
| publisher | Corporación Universitaria de la Costa |
| spellingShingle | Application of Clustering Techniques for Lung Sounds to Improve Interpretability and Detection of Crackles Germán D. Sosa Ingeniería Log means Symlet DBSCAN energy Application of Clustering Techniques for Lung Sounds to Improve Interpretability and Detection of Crackles Germán D. Sosa Fabián Velásquez Clavijo Ingeniería Log means Symlet DBSCAN energy Due to the subjectivity involved currently in pulmonary auscultation process and its diagnostic to evaluate the condition of respiratory airways, this work pretends to evaluate the performance of clustering algorithms such as k-means and DBSCAN to perform a computational analysis of lung sounds aiming to visualize a representation of such sounds that highlights the presence of crackles and the energy associated with them. In order to achieve that goal, Wavelet analysis techniques were used in contrast to traditional frequency analysis given the similarity between the typical waveform for a crackle and the wavelet sym4. Once the lung sound signal with isolated crackles is obtained, the clustering process groups crackles in regions of high density and provides visualization that might be useful for the diagnostic made by an expert. Evaluation suggests that k-means groups crackle more effective than DBSCAN in terms of generated clusters. 2015 artículo científico 0122-6517 https://www.redalyc.org/articulo.oa?id=497779324005 en http://www.redalyc.org/revista.oa?id=4977 INGE CUC application/pdf Corporación Universitaria de la Costa INGE CUC (Colombia) Num.1 Vol.11 |
| title | Application of Clustering Techniques for Lung Sounds to Improve Interpretability and Detection of Crackles |
| topic | Ingeniería Log means Symlet DBSCAN energy |
| url | https://www.redalyc.org/articulo.oa?id=497779324005 |