Author:

Keywords:

SISTA, Science & Technology, Technology, Life Sciences & Biomedicine, Computer Science, Interdisciplinary Applications, Engineering, Biomedical, Mathematical & Computational Biology, Medical Informatics, Computer Science, Engineering, neural networks, conjugate gradient algorithms, pattern classification, spectral analysis, stomach, electrogastrography, Algorithms, Electromyography, Humans, Neural Networks, Computer, Signal Processing, Computer-Assisted, Stomach, Stomach Diseases, 0903 Biomedical Engineering, 0906 Electrical and Electronic Engineering, Biomedical Engineering, 4003 Biomedical engineering, 4603 Computer vision and multimedia computation, 4611 Machine learning

Abstract:

A neural network approach is proposed for the automated classification of the normal and abnormal EGG. Two learning algorithms, the quasi-Newton and the scaled conjugate gradient method for the multilayer feedforward neural networks (MFNN), are introduced and compared with the error backpropagation algorithm. The configurations of the MFNN are determined by experiment. The raw EGG data, its power spectral data, and its autoregressive moving average (ARMA) modelling parameters are used as the input to the MFNN and compared with each other. Three indexes (the percent correct, sum-squared error and complexity per iteration) are used to evaluate the performance of each learning algorithm. The results show that the scaled conjugate gradient algorithm performs best, in that it is robust and provides a super-linear convergence rate. The power spectral representation and the ARMA modelling parameters of the EGG are found to be better types of the input to the network for this specific application, both yielding a percent correctness of 95% on the test set. Although the results are focused on the classification of the EGG, this paper should provide useful information for the classification of other biomedical signals.