Result: Multiple feature domains information fusion for computer-aided clinical electromyography

Title:
Multiple feature domains information fusion for computer-aided clinical electromyography
Authors:
HONGBO XIE, HAI HUANG, ZHIZHONG WANG
Source:
CAIP 2005 : computer analysis of images and patterns (Versailles, 5-8 September 2005)Lecture notes in computer science. :304-312
Publisher Information:
Berlin: Springer, 2005.
Publication Year:
2005
Physical Description:
print, 11 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Reconnaissance des formes. Traitement numérique des images. Géométrie algorithmique, Pattern recognition. Digital image processing. Computational geometry, Algorithme rétropropagation, Backpropagation algorithm, Algoritmo retropropagación, Analyse forme, Pattern analysis, Análisis forma, Analyse image, Image analysis, Análisis imagen, Application médicale, Medical application, Aplicación medical, Classification, Clasificación, Consensus, Consenso, Diagnostic, Diagnosis, Diagnóstico, Electromyographie, Electromyography, Electromiografía, Fusion donnée, Data fusion, Fusión datos, Modélisation, Modeling, Modelización, Méthode domaine fréquence, Frequency domain method, Método dominio frecuencia, Méthode intégrale, Integral method, Método integral, Potentiel action, Action potential, Potencial acción, Réseau neuronal, Neural network, Red neuronal, Système aide décision, Decision support system, Sistema ayuda decisíon, Système hybride, Hybrid system, Sistema híbrido, Théorie mesure, Measure theory, Teoría medida, Traitement image, Image processing, Procesamiento imagen, Transformation ondelette, Wavelet transformation, Transformación ondita
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Computation Science, Huaiyin Institute of Technology, Huaian, 223300, China
Department of Biomedical Engineering, Shanghai Jiao Tong University, 200030, China
ISSN:
0302-9743
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=17182635
Rights:
Copyright 2005 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.17182635
Database:
PASCAL Archive

Further Information

The conventional neural networks methods of motor unit action potential analysis in clinical Electromyography are mainly based on single feature set model, the diagnosis accuracy of which is not always satisfactory. In order to utilize multiple feature sets to improve diagnosis accuracy, a hybrid decision support system based on fusion of multiple feature sets classification outputs is presented. Back-propagation (BP) neural network is used as single diagnosis model in every feature set, i.e. i) time domain morphological measures, ii) frequency parameters, and iii) time-frequency domain wavelet transform feature set. Then these outputs are combined by a modified fuzzy integral method to obtain the consensus diagnosis result. More excellent diagnosis yield indicates the potential of the proposed multiple feature domain strategies for aiding the neurophysiologist in the early and accurate diagnosis of neuromuscular disorders. The method is also compared with the majority vote combination scheme.