A new feature extraction method for signal classification applied to cat spinal cord signals

Vidaurre Henche, Diego and Bielza Lozoya, Maria Concepcion and Larrañaga Múgica, Pedro and Rudomín Zevnovaty, Pablo and Rodríguez Torres, Erika Elizabeth (2012). A new feature extraction method for signal classification applied to cat spinal cord signals. "Journal of Neural Engineering", v. 9 (n. 5); ISSN 1741-2560. https://doi.org/10.1088/1741-2560/9/5/056009.

Description

Title: A new feature extraction method for signal classification applied to cat spinal cord signals
Author/s:
  • Vidaurre Henche, Diego
  • Bielza Lozoya, Maria Concepcion
  • Larrañaga Múgica, Pedro
  • Rudomín Zevnovaty, Pablo
  • Rodríguez Torres, Erika Elizabeth
Item Type: Article
Título de Revista/Publicación: Journal of Neural Engineering
Date: October 2012
ISSN: 1741-2560
Volume: 9
Subjects:
Faculty: Facultad de Informática (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

In the spinal cord of the anesthetized cat, spontaneous cord dorsum potentials (CDPs) appear synchronously along the lumbo-sacral segments. These CDPs have different shapes and magnitudes. Previous work has indicated that some CDPs appear to be specially associated with the activation of spinal pathways that lead to primary afferent depolarization and presynaptic inhibition. Visual detection and classification of these CDPs provides relevant information on the functional organization of the neural networks involved in the control of sensory information and allows the characterization of the changes produced by acute nerve and spinal lesions. We now present a novel feature extraction approach for signal classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Then, we assign a coefficient for each main local maximum of the signal using its amplitude and distance to the most important maximum of the signal. These coefficients will be the input for the subsequent classification algorithm. In particular, we employ gradient boosting classification trees. This combination of approaches allows a faster and more accurate discrimination of CDPs than is obtained by other methods.

More information

Item ID: 16449
DC Identifier: http://oa.upm.es/16449/
OAI Identifier: oai:oa.upm.es:16449
DOI: 10.1088/1741-2560/9/5/056009
Official URL: http://iopscience.iop.org/1741-2552/9/5/056009/
Deposited by: Memoria Investigacion
Deposited on: 15 Jul 2013 16:49
Last Modified: 21 Apr 2016 16:45
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