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

Godino Llorente, Juan Ignacio and Martínez Vargas, Juan and 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.

Description

Title: Time-Frequency based Feature Selection for Discrimination of non stationary Biosignals.
Author/s:
  • Godino Llorente, Juan Ignacio
  • Martínez Vargas, Juan
  • Castellanos Domínguez, César Germán
Item Type: Article
Título de Revista/Publicación: EURASIP Journal on Advances in Signal Processing
Date: 9 October 2012
ISSN: 1687-6180
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 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.

More information

Item ID: 16435
DC Identifier: http://oa.upm.es/16435/
OAI Identifier: oai:oa.upm.es:16435
DOI: 10.1186/1687-6180-2012-219
Official URL: http://asp.eurasipjournals.com/content/2012/1/219
Deposited by: Memoria Investigacion
Deposited on: 18 Jul 2013 11:30
Last Modified: 21 Apr 2016 16:44
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