Observatorio de I+D+i UPM

Memorias de investigación
Book chapters:
Adaptive Fuzzy Inference Neural Network System for EEG and Stabilometry Signals Classification
Year:2011
Research Areas
  • Information technology and adata processing
Information
Abstract
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.
International
Si
10.1007/978-3-642-20344-2
Book Edition
1
Book Publishing
Springer
ISBN
978-3-642-20343-5
Series
Book title
Next Generation Data Technologies for Collective Computational Intelligence
From page
329
To page
355
Participants
  • Autor: Aurora Perez Perez (UPM)
  • Autor: Juan Pedro Caraca-Valente Hernandez (UPM)
  • Autor: Pari Jahankhani (University of Westminster, Reino Unido)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Grupo de Investigación en Tecnología Informática y de las Comunicaciones: CETTICO
S2i 2019 Observatorio de investigación @ UPM con la colaboración del Consejo Social UPM
Cofinanciación del MINECO en el marco del Programa INNCIDE 2011 (OTR-2011-0236)
Cofinanciación del MINECO en el marco del Programa INNPACTO (IPT-020000-2010-22)