Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Computing and Applications
Implementing automated diagnostic systems for breast cancer detection
Expert Systems with Applications: An International Journal
Recurrent neural networks employing Lyapunov exponents for analysis of doppler ultrasound signals
Expert Systems with Applications: An International Journal
Feature extraction from Doppler ultrasound signals for automated diagnostic systems
Computers in Biology and Medicine
IEEE Transactions on Neural Networks
Input feature selection for classification problems
IEEE Transactions on Neural Networks
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
Fractal QRS-complexes pattern recognition for imperative cardiac arrhythmias
Digital Signal Processing
ECG beat classification using particle swarm optimization and radial basis function neural network
Expert Systems with Applications: An International Journal
Sparse Representation-Based Heartbeat Classification Using Independent Component Analysis
Journal of Medical Systems
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The purpose of this study is to evaluate the accuracy of the recurrent neural networks (RNNs) trained with Levenberg-Marquardt algorithm on the electrocardiogram (ECG) beats. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database were analyzed. Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the RNN trained on the extracted features. The RNNs were implemented for classification of the ECG beats using the statistical features as inputs. The ability of designed and trained Elman RNNs, combined with eigenvector methods, were explored to classify the ECG beats. The classification results demonstrated that the combined eigenvector methods/RNN approach can be useful in analyzing the ECG beats.