Classification of normal and abnormal electrogastrograms using multilayer feedforward neural networks

@article{Lin1997ClassificationON,
  title={Classification of normal and abnormal electrogastrograms using multilayer feedforward neural networks},
  author={Zhiping Lin and Jan Maris and Luc Hermans and J. Vandewalle and Dr. J. D. Z. Chen},
  journal={Medical and Biological Engineering and Computing},
  year={1997},
  volume={35},
  pages={199-206}
}
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… CONTINUE READING