Time-Frequency based Feature Selection for Discrimination of non stationary Biosignals.

Godino Llorente, Juan Ignacio; Martínez Vargas, Juan y Castellanos Domínguez, César Germán (2012). Time-Frequency based Feature Selection for Discrimination of non stationary Biosignals.. "EURASIP Journal on Advances in Signal Processing" (n. 1); pp. 1-18. ISSN 1687-6180. https://doi.org/10.1186/1687-6180-2012-219.

Descripción

Título: Time-Frequency based Feature Selection for Discrimination of non stationary Biosignals.
Autor/es:
  • Godino Llorente, Juan Ignacio
  • Martínez Vargas, Juan
  • Castellanos Domínguez, César Germán
Tipo de Documento: Artículo
Título de Revista/Publicación: EURASIP Journal on Advances in Signal Processing
Fecha: 9 Octubre 2012
Materias:
Escuela: E.U.I.T. Telecomunicación (UPM) [antigua denominación]
Departamento: Ingeniería de Circuitos y Sistemas [hasta 2014]
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

This research proposes a generic methodology for dimensionality reduction upon time-frequency representations applied to the classification of different types of biosignals. The methodology directly deals with the highly redundant and irrelevant data contained in these representations, combining a first stage of irrelevant data removal by variable selection, with a second stage of redundancy reduction using methods based on linear transformations. The study addresses two techniques that provided a similar performance: the first one is based on the selection of a set of the most relevant time?frequency points, whereas the second one selects the most relevant frequency bands. The first methodology needs a lower quantity of components, leading to a lower feature space; but the second improves the capture of the time-varying dynamics of the signal, and therefore provides a more stable performance. In order to evaluate the generalization capabilities of the methodology proposed it has been applied to two types of biosignals with different kinds of non-stationary behaviors: electroencephalographic and phonocardiographic biosignals. Even when these two databases contain samples with different degrees of complexity and a wide variety of characterizing patterns, the results demonstrate a good accuracy for the detection of pathologies, over 98%.The results open the possibility to extrapolate the methodology to the study of other biosignals.

Más información

ID de Registro: 16435
Identificador DC: http://oa.upm.es/16435/
Identificador OAI: oai:oa.upm.es:16435
Identificador DOI: 10.1186/1687-6180-2012-219
URL Oficial: http://asp.eurasipjournals.com/content/2012/1/219
Depositado por: Memoria Investigacion
Depositado el: 18 Jul 2013 11:30
Ultima Modificación: 21 Abr 2016 16:44
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