Research Publications in journals:
A new feature extraction method for signal classification applied to cat spinal cord signals
Year:2012
Research Areas
- Artificial intelligence
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Abstract
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| 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. | |
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Journal of Neural Engineering |
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1741-2560 |
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3,837 |
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Open Access publication
Participants
- Autor: Diego Vidaurre Henche UPM
- Autor: E E Rodríguez Departamento de Fisiología, Biofísica y Neurociencias, Centro de Investigación y Estudios Avanzados; Centro de Investigación en Matemáticas, Universidad Autónoma de Hidalgo, Mexico
- Autor: Maria Concepcion Bielza Lozoya UPM
- Autor: Pedro Maria Larrañaga Mugica UPM
- Autor: P Rudomin Departamento de Fisiología, Biofísica y Neurociencias, Centro de Investigación y Estudios Avanzados
Research Group, Departaments and Institutes related
- Creador: Grupo de Investigación: COMPUTATIONAL INTELLIGENCE GROUP
- Departamento: Inteligencia Artificial