Recurrent neural networks with composite features for detection of electrocardiographic changes in partial epileptic patients

@article{beyli2008RecurrentNN,
  title={Recurrent neural networks with composite features for detection of electrocardiographic changes in partial epileptic patients},
  author={Elif Derya {\"U}beyli},
  journal={Computers in biology and medicine},
  year={2008},
  volume={38 3},
  pages={401-10}
}
The aim of this study is to evaluate the diagnostic accuracy of the recurrent neural networks (RNNs) with composite features (wavelet coefficients and Lyapunov exponents) on the electrocardiogram (ECG) signals. Two types of ECG beats (normal and partial epilepsy) were obtained from the MIT-BIH database. The multilayer perceptron neural networks (MLPNNs) were also tested and benchmarked for their performance on the classification of the ECG signals. Decision making was performed in two stages… CONTINUE READING
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