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...

Full description

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
Description
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.