Feature Extraction for Murmur Detection Based on Support Vector Regression of Time-Frequency Representations

Jaramillo-Garzon, J. and Quiceno-Manrique, A. and Godino Llorente, Juan Ignacio and Castellanos Domínguez, César Germán (2008). Feature Extraction for Murmur Detection Based on Support Vector Regression of Time-Frequency Representations. In: "30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE EMBC'08", 20/08/2008-24/08/2008, British Columbia, Canada. ISBN 978-1-4244-1814-5. https://doi.org/10.1109/IEMBS.2008.4649484.

Description

Title: Feature Extraction for Murmur Detection Based on Support Vector Regression of Time-Frequency Representations
Author/s:
  • Jaramillo-Garzon, J.
  • Quiceno-Manrique, A.
  • Godino Llorente, Juan Ignacio
  • Castellanos Domínguez, César Germán
Item Type: Presentation at Congress or Conference (Article)
Event Title: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE EMBC'08
Event Dates: 20/08/2008-24/08/2008
Event Location: British Columbia, Canada
Title of Book: Proceedings of the 30th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society, IEEE EMBC'08
Date: 2008
ISBN: 978-1-4244-1814-5
Subjects:
Faculty: E.U.I.T. Telecomunicación (UPM)
Department: Ingeniería de Circuitos y Sistemas [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

This paper presents a nonlinear approach for time-frequency representations (TFR) data analysis, based on a statistical learning methodology - support vector regression(SVR), that being a nonlinear framework, matches recent findings on the underlying dynamics of cardiac mechanic activity and phonocardiographic (PCG) recordings. The proposed methodology aims to model the estimated TFRs, and extract relevant features to perform classification between normal and pathologic PCG recordings (with murmur). Modeling of TFR is done by means of SVR, and the distance between regressions is calculated through dissimilarity measures based on dot product. Finally, a k-nn classifier is used for the classification stage, obtaining a validation performance of 97.85%.

More information

Item ID: 4665
DC Identifier: http://oa.upm.es/4665/
OAI Identifier: oai:oa.upm.es:4665
DOI: 10.1109/IEMBS.2008.4649484
Official URL: http://www.embc2008.com/
Deposited by: Memoria Investigacion
Deposited on: 27 Oct 2010 09:15
Last Modified: 20 Apr 2016 13:47
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