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

Vidaurre Henche, Diego; Bielza Lozoya, Maria Concepcion; Larrañaga Múgica, Pedro; Rudomín Zevnovaty, Pablo y 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.

Descripción

Título: A new feature extraction method for signal classification applied to cat spinal cord signals
Autor/es:
  • Vidaurre Henche, Diego
  • Bielza Lozoya, Maria Concepcion
  • Larrañaga Múgica, Pedro
  • Rudomín Zevnovaty, Pablo
  • Rodríguez Torres, Erika Elizabeth
Tipo de Documento: Artículo
Título de Revista/Publicación: Journal of Neural Engineering
Fecha: Octubre 2012
Volumen: 9
Materias:
Escuela: Facultad de Informática (UPM) [antigua denominación]
Departamento: Inteligencia Artificial
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

Texto completo

[img]
Vista Previa
PDF (Document Portable Format) - Se necesita un visor de ficheros PDF, como GSview, Xpdf o Adobe Acrobat Reader
Descargar (920kB) | Vista Previa

Resumen

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.

Más información

ID de Registro: 16449
Identificador DC: http://oa.upm.es/16449/
Identificador OAI: oai:oa.upm.es:16449
Identificador DOI: 10.1088/1741-2560/9/5/056009
URL Oficial: http://iopscience.iop.org/1741-2552/9/5/056009/
Depositado por: Memoria Investigacion
Depositado el: 15 Jul 2013 16:49
Ultima Modificación: 21 Abr 2016 16:45
  • Open Access
  • Open Access
  • Sherpa-Romeo
    Compruebe si la revista anglosajona en la que ha publicado un artículo permite también su publicación en abierto.
  • Dulcinea
    Compruebe si la revista española en la que ha publicado un artículo permite también su publicación en abierto.
  • Recolecta
  • e-ciencia
  • Observatorio I+D+i UPM
  • OpenCourseWare UPM