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

Godino Llorente, Juan Ignacio; Gómez García, Jorge Andrés y 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.

Descripción

Título: Non uniform embedding based on relevance analysis with reduced computational complexity: application to the detection of pathologies from biosignal recordings
Autor/es:
  • Godino Llorente, Juan Ignacio
  • Gómez García, Jorge Andrés
  • Castellanos Domínguez, Germán
Tipo de Documento: Artículo
Título de Revista/Publicación: Neurocomputing
Fecha: Mayo 2014
Volumen: 132
Materias:
Palabras Clave Informales: Nonlinear dynamics; Non uniform embedding; Time-delay embedding
Escuela: E.T.S.I. y Sistemas de Telecomunicación (UPM)
Departamento: Teoría de la Señal y Comunicaciones
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Más información

ID de Registro: 35803
Identificador DC: http://oa.upm.es/35803/
Identificador OAI: oai:oa.upm.es:35803
Identificador DOI: 10.1016/j.neucom.2013.01.059
URL Oficial: http://www.sciencedirect.com/science/article/pii/S0925231213009016
Depositado por: Memoria Investigacion
Depositado el: 19 Feb 2016 19:28
Ultima Modificación: 19 Feb 2016 19:28
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