Aplicación de técnicas de clustering en sonidos adventicios para mejorar la interpretabilidad y detección de estertores
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 aimi...
- Autores:
-
Sosa, Germán D
Velásquez Clavijo, Fabián
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2015
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/1564
- Acceso en línea:
- https://hdl.handle.net/11323/1564
https://doi.org/10.17981/ingecuc.11.1.2015.05
https://repositorio.cuc.edu.co/
- Palabra clave:
- Sonido Pulmonar
Estertores
Sonidos Vesiculares
Sonidos Adventicios
Transformada Wavelet
Descomposición Wavelet
symlet
Clustering
k-means
DBSCAN
log-ennergy
Pulmonary sound
Rales
Vesicular sounds
Adventitious Sounds
Wavelet Transform
Wavelet decomposition
- Rights
- openAccess
- License
- http://purl.org/coar/access_right/c_abf2
Summary: | 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. |
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