DocumentCode
2663079
Title
Human magnetocardiogram (MCG) modeling using evolutionary artificial neural networks
Author
Georgopoulos, E.F. ; Likothanassis, S.D.
Author_Institution
Dept. of Comput. Eng. & Inf., Patras Univ.
fYear
2000
fDate
2000
Firstpage
110
Lastpage
120
Abstract
In the present work magnetocardiogram (MCG) recordings of normal subjects were analyzed using a hybrid training algorithm. This algorithm combines genetic algorithms and a training method based on the localized Extended Kalman Filter (EKF), in order to evolve the structure and train Multi-Layered Perceptrons (MLP) networks. Our goal is to examine the predictability of the MCG signal on a short predicting horizon
Keywords
genetic algorithms; learning (artificial intelligence); magnetocardiography; medical signal processing; multilayer perceptrons; Multi-Layered Perceptrons; evolutionary artificial neural networks; genetic algorithms; hybrid training; localized Extended Kalman Filter; magnetocardiogram; Artificial neural networks; Biomedical engineering; Biomedical informatics; Filtering algorithms; Humans; Neural networks; Pattern recognition; Physics computing; SQUIDs; Superconducting magnets;
fLanguage
English
Publisher
ieee
Conference_Titel
Combinations of Evolutionary Computation and Neural Networks, 2000 IEEE Symposium on
Conference_Location
San Antonio, TX
Print_ISBN
0-7803-6572-0
Type
conf
DOI
10.1109/ECNN.2000.886226
Filename
886226
Link To Document