Acoustic lung signals analysis based on Mel frequency cepstral coefficients and self-organizing maps

This study analyzes acoustic lung signals with different abnormalities, using Mel Frequency Cepstral Coefficients (MFCC), Self-Organizing Maps (SOM), and K-means clustering algorithm. SOM models are known as artificial neural networks than can be trained in an unsupervised or supervised manner. Both...

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Bibliographic Details
Published inRevista FI-UPTC Vol. 25; no. 43; pp. 73 - 82
Main Authors Posada Quintero, Hugo Fernando, Orjuela Cañón, Álvaro David
Format Journal Article
LanguageEnglish
Published Universidad Pedagógica y Tecnológica de Colombia 01.09.2016
Subjects
acoustic lung signals
aided decision making
computer
computer-aided decision making
Electronics Engineering
Ingeniería Electrónica
mapas auto
organizados
organizing maps
self
self-organizing maps
señales acústicas de pulmón
sistemas de apoyo a decisión
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Summary:This study analyzes acoustic lung signals with different abnormalities, using Mel Frequency Cepstral Coefficients (MFCC), Self-Organizing Maps (SOM), and K-means clustering algorithm. SOM models are known as artificial neural networks than can be trained in an unsupervised or supervised manner. Both approaches were used in this work to compare the utility of this tool in lung signals studies. Results showed that with a supervised training, the classification reached rates of 85 % in accuracy. Unsupervised training was used for clustering tasks, and three clusters was the most adequate number for both supervised and unsupervised training. In general, SOM models can be used in lung signals as a strategy to diagnose systems, finding number of clusters in data, and making classifications for computer-aided decision making systems.
ISSN:0121-1129
2357-5328
DOI:10.19053/01211129.v25.n43.2016.5300