Observatorio de I+D+i UPM

Memorias de investigación
Capítulo de libro:
Adaptive Fuzzy Inference Neural Network System for EEG and Stabilometry Signals Classification
Año:2011
Áreas de investigación
  • Ciencias de la computación y tecnología informática
Datos
Descripción
The focus of this chapter is to study feature extraction and pattern classification methods from two medical areas, Stabilometry and Electroencephalography (EEG). Stabilometry is the branch of medicine responsible for examining balance in human beings. Balance and dizziness disorders are probably two of the most common illnesses that physicians have to deal with. In Stabilometry, the key nuggets of information in a time series signal are concentrated within definite time periods are known as events. In this chapter, two feature extraction schemes have been developed to identify and characterise the events in Stabilometry and EEG signals. Based on these extracted features, an Adaptive Fuzzy Inference Neural network has been applied for classification of Stabilometry and EEG signals. The model constructs its initial rules by a hybrid supervised/unsupervised clustering scheme while its final fuzzy rule base is optimised through competitive learning. A two-stage learning methodology is applied to this Neuro-Fuzzy structure, by incorporating gradient descent and recursive least squares estimations. The proposed modelling scheme is characterised by its high performance accuracy, high training speed and provides an efficient solution to the ?curse of dimensionality? problem inherited in traditional neuro-fuzzy schemes. In order to classify Stabilometric time series, a set of balance-related features have been extracted according to the expert?s criteria. The proposed Stabilometric medical diagnostic system is based on a method for generating reference models from a set of time series. The experimental results validated the proposed methodology.
Internacional
Si
DOI
10.1007/978-3-642-20344-2
Edición del Libro
1
Editorial del Libro
Springer
ISBN
978-3-642-20343-5
Serie
Título del Libro
Next Generation Data Technologies for Collective Computational Intelligence
Desde página
329
Hasta página
355
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Aurora Perez Perez (UPM)
  • Autor: Juan Pedro Caraca-Valente Hernandez (UPM)
  • Autor: Pari Jahankhani (University of Westminster, Reino Unido)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Grupo de Investigación en Tecnología Informática y de las Comunicaciones: CETTICO
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