Memorias de investigación
Capítulo de libro:
Feature Extraction for Murmur Detection Based on Support Vector Regression of Time-Frequency Representations
Año:2008
Áreas de investigación
  • Procesado y análisis de la señal
Datos
Descripción
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%.
Internacional
Si
DOI
10.1109/IEMBS.2008.4649484
Edición del Libro
0
Editorial del Libro
ISBN
978-1-4244-1814-5
Serie
Título del Libro
Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE
Desde página
1623
Hasta página
1626
Esta actividad pertenece a memorias de investigación
Participantes
  • Participante: A. Quiceno-Manrique
  • Participante: C. G. Castellanos-Dominguez
  • Autor: Juan Ignacio Godino Llorente UPM
  • Participante: J. Jaramillo-Garzon
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Informática Aplicada al Procesado de Señal e Imagen
  • Departamento: Ingeniería de Circuitos y Sistemas