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| Format: | Artículo científico |
| Language: | en |
| Published: |
Universidad Pedagógica y Tecnológica de Colombia
2016
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| Online Access: | https://www.redalyc.org/articulo.oa?id=413948045006 https://www.redalyc.org/journal/4139/413948045006/ https://www.redalyc.org/journal/4139/413948045006/html/ https://www.redalyc.org/journal/4139/413948045006/413948045006.epub https://www.redalyc.org/journal/4139/413948045006/movil |
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Table of Contents:
- Acoustic lung signals analysis based on Mel frequency cepstral coefficients and self-organizing maps Álvaro David Orjuela-Cañón Hugo Fernando Posada-Quintero Ingeniería self computer organizing maps Acoustic lung signals aided decision making 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. 2016 artículo científico 0121-1129 https://www.redalyc.org/articulo.oa?id=413948045006 https://www.redalyc.org/journal/4139/413948045006/ https://www.redalyc.org/journal/4139/413948045006/html/ https://www.redalyc.org/journal/4139/413948045006/413948045006.epub https://www.redalyc.org/journal/4139/413948045006/movil en http://www.redalyc.org/revista.oa?id=4139 Facultad de Ingeniería application/pdf Universidad Pedagógica y Tecnológica de Colombia Facultad de Ingeniería (Colombia) Num.43 Vol.25