Non uniform embedding based on relevance analysis with reduced computational complexity: application to the detection of pathologies from biosignal recordings

Godino Llorente, Juan Ignacio and Gómez García, Jorge Andrés and Castellanos Domínguez, Germán (2014). Non uniform embedding based on relevance analysis with reduced computational complexity: application to the detection of pathologies from biosignal recordings. "Neurocomputing", v. 132 (n. null); pp. 148-158. ISSN 0925-2312. https://doi.org/10.1016/j.neucom.2013.01.059.

Description

Title: Non uniform embedding based on relevance analysis with reduced computational complexity: application to the detection of pathologies from biosignal recordings
Author/s:
  • Godino Llorente, Juan Ignacio
  • Gómez García, Jorge Andrés
  • Castellanos Domínguez, Germán
Item Type: Article
Título de Revista/Publicación: Neurocomputing
Date: May 2014
ISSN: 0925-2312
Volume: 132
Subjects:
Freetext Keywords: Nonlinear dynamics; Non uniform embedding; Time-delay embedding
Faculty: E.T.S.I. y Sistemas de Telecomunicación (UPM)
Department: Teoría de la Señal y Comunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (14MB) | Preview

Abstract

Nonlinear analysis tools for studying and characterizing the dynamics of physiological signals have gained popularity, mainly because tracking sudden alterations of the inherent complexity of biological processes might be an indicator of altered physiological states. Typically, in order to perform an analysis with such tools, the physiological variables that describe the biological process under study are used to reconstruct the underlying dynamics of the biological processes. For that goal, a procedure called time-delay or uniform embedding is usually employed. Nonetheless, there is evidence of its inability for dealing with non-stationary signals, as those recorded from many physiological processes. To handle with such a drawback, this paper evaluates the utility of non-conventional time series reconstruction procedures based on non uniform embedding, applying them to automatic pattern recognition tasks. The paper compares a state of the art non uniform approach with a novel scheme which fuses embedding and feature selection at once, searching for better reconstructions of the dynamics of the system. Moreover, results are also compared with two classic uniform embedding techniques. Thus, the goal is comparing uniform and non uniform reconstruction techniques, including the one proposed in this work, for pattern recognition in biomedical signal processing tasks. Once the state space is reconstructed, the scheme followed characterizes with three classic nonlinear dynamic features (Largest Lyapunov Exponent, Correlation Dimension and Recurrence Period Density Entropy), while classification is carried out by means of a simple k-nn classifier. In order to test its generalization capabilities, the approach was tested with three different physiological databases (Speech Pathologies, Epilepsy and Heart Murmurs). In terms of the accuracy obtained to automatically detect the presence of pathologies, and for the three types of biosignals analyzed, the non uniform techniques used in this work lightly outperformed the results obtained using the uniform methods, suggesting their usefulness to characterize non-stationary biomedical signals in pattern recognition applications. On the other hand, in view of the results obtained and its low computational load, the proposed technique suggests its applicability for the applications under study.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTEC2009-14123-C04-04UnspecifiedUnspecifiedSistemas de reconocimiento biométrico en entornos reales de funcionamiento operativo

More information

Item ID: 35803
DC Identifier: http://oa.upm.es/35803/
OAI Identifier: oai:oa.upm.es:35803
DOI: 10.1016/j.neucom.2013.01.059
Official URL: http://www.sciencedirect.com/science/article/pii/S0925231213009016
Deposited by: Memoria Investigacion
Deposited on: 19 Feb 2016 19:28
Last Modified: 07 Jun 2019 08:29
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM