Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Recurrent neural networks employing Lyapunov exponents for analysis of doppler ultrasound signals
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
The wavelet transform, time-frequency localization and signal analysis
IEEE Transactions on Information Theory
IEEE Transactions on Neural Networks
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
Artificial Intelligence in Medicine
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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: computing composite features which were then input into the classifiers and classification using the classifiers trained with the Levenberg-Marquardt algorithm. The research demonstrated that the wavelet coefficients and the Lyapunov exponents are the features which well represent the ECG signals and the RNN trained on these features achieved high classification accuracies.