Detection of electrocardiographic changes in partial epileptic patients using Lyapunov exponents with multilayer perceptron neural networks

Authors:
Elif Derya íbeyli;İnan Güler
Affiliations:
Department of Electrical and Electronics Engineering, Faculty of Engineering, TOBB Ekonomi ve Teknoloji íniversitesi, Sögütözü, Ankara, Turkey;Department of Electronics and Computer Education, Faculty of Technical Education, Gazi University, 06500 Teknikokullar, Ankara, Turkey
Venue:
Engineering Applications of Artificial Intelligence
Year:
2004

Citing 3
Cited 12

First- and second-order methods for learning: between steepest descent and Newton's method

Neural Computation
An ECG classifier designed using modified decision based neural networks

Computers and Biomedical Research
Training feedforward networks with the Marquardt algorithm

IEEE Transactions on Neural Networks

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

Computers in Biology and Medicine
Time-varying biomedical signals analysis with multiclass support vector machines employing Lyapunov exponents

Digital Signal Processing
Automatic identification of cardiac health using modeling techniques: A comparative study

Information Sciences: an International Journal
Support vector machines for detection of electrocardiographic changes in partial epileptic patients

Engineering Applications of Artificial Intelligence
Cross-correlation aided support vector machine classifier for classification of EEG signals

Expert Systems with Applications: An International Journal
Features for analysis of electrocardiographic changes in partial epileptic patients

Expert Systems with Applications: An International Journal
Eigenvector methods for automated detection of electrocardiographic changes in partial epileptic patients

IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Entropies based detection of epileptic seizures with artificial neural network classifiers

Expert Systems with Applications: An International Journal
A modified mixture of experts network structure for ECG beats classification with diverse features

Engineering Applications of Artificial Intelligence
Recurrent neural networks employing Lyapunov exponents for EEG signals classification

Expert Systems with Applications: An International Journal
Evolutionary model selection in a wavelet-based support vector machine for automated seizure detection

Expert Systems with Applications: An International Journal
A decision support system for EEG signals based on adaptive fuzzy inference neural networks

Journal of Computational Methods in Sciences and Engineering

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this study, a new approach based on the consideration that electrocardiogram (ECG) signals are chaotic signals was presented for detection of electrocardiographic changes in patients with partial epilepsy. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of electrocardiographic changes in patients with partial epilepsy. Two types of ECG beats (normal and partial epilepsy) were obtained from the MIT-BIH database. The computed Lyapunov exponents of the ECG signals were used as inputs of the MLPNNs trained with backpropagation, delta-bar-delta, extended delta-bar-delta, quick propagation, and Levenberg-Marquardt algorithms. The performances of the MLPNN classifiers were evaluated in terms of training performance and classification accuracies. Receiver operating characteristic (ROC) curves were used to assess the performance of the detection process. The results confirmed that the proposed MLPNN trained with the Levenberg-Marquardt algorithm has potential in detecting the electrocardiographic changes in patients with partial epilepsy.