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Application of neural network and genetic algorithms in computer aided gastric diagnostic system

Świerczyński Z., Zagańczyk A.

Biocybernetics and Biomedical Engineering

2005

Vol. 25, no. 1

49-58

In the paper, computer aided stomach diagnosis problems are presented. The subject of the study is electrical signal generated by human stomach, called the electrogastrographic (EGG) signal. The non-invasively measured signals were subjected to parametrization and then neural network based classification. The parametrization was performed with one of the time series modelling methods, with linear autoregressive models (AR). A special feature of the presented methodology of classification is its hybrid approach. The idea of this specific combination is that a genetic algorithm is used as the evolutionary method of training of the neural network. The structure and parameters of the system (NEUROGEN v.02), used for classification of the parameterized EGG data, are described. The finally obtained effectiveness of the whole system (NEUROGEN v.02 with the parametrization method applied), amounting to 74%, is quite high and according to the authors' analysis, can be improved. A way of improvement of the effectiveness are also outlined in the summary.

Application of SVM in computer aided gastric diagnostic system

Świerczyński Z., Mazur J.

Biocybernetics and Biomedical Engineering

2004

Vol. 24, no. 4

19-30

In the paper computer aided stomach diagnosis problems are presented. The subject of the study is electrical signal generated by human stomach and called electrogastrographic (EGG) signal. The non-invasively measured signals were subjected to parametrization, which was performed with one of the time series modelling methods, with linear autoregressive models (AR). Then the obtained sets of numbers were classified with the Support Vector Machine (SVM), which is a relatively new pattern recognition technique and is based on the idea of structural risk minimization, The structure and parameters of algorithm used for classification of the parameterized EGG data are described. The finally obtained effectiveness of the whole system (SVM with the parametrization method applied), amounting to 81%, is promising and, according to the authors' analysis can be improved. The ways of improving of the effectiveness are also outlined in the conclusions.