Engineering Applications of Artificial Intelligence
IEEE Transactions on Neural Networks
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
Expert systems for time-varying biomedical signals using eigenvector methods
Expert Systems with Applications: An International Journal
Features extracted by eigenvector methods for detecting variability of EEG signals
Pattern Recognition Letters
Implementing automated diagnostic systems for breast cancer detection
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Obtaining transparent models of chaotic systems with multi-objective simulated annealing algorithms
Information Sciences: an International Journal
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
Usage of eigenvector methods to improve reliable classifier for Doppler ultrasound signals
Computers in Biology and Medicine
Automatic seizure detection based on time-frequency analysis and artificial neural networks
Computational Intelligence and Neuroscience - Regular issue
Multiclass support vector machines for diagnosis of erythemato-squamous diseases
Expert Systems with Applications: An International Journal
Decision support systems for time-varying biomedical signals: EEG signals classification
Expert Systems with Applications: An International Journal
Cross-correlation aided support vector machine classifier for classification of EEG signals
Expert Systems with Applications: An International Journal
Analysis of EEG signals by implementing eigenvector methods/recurrent neural networks
Digital Signal Processing
Automatic EEG signal classification for epilepsy diagnosis with Relevance Vector Machines
Expert Systems with Applications: An International Journal
Automatic Recurrent ANN development for signal classification: detection of seizures in EEGs
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Statistics over features: EEG signals analysis
Computers in Biology and Medicine
Discrimination between ictal and seizure-free EEG signals using empirical mode decomposition
Research Letters in Signal Processing
Expert Systems with Applications: An International Journal
Lyapunov exponents/probabilistic neural networks for analysis of EEG signals
Expert Systems with Applications: An International Journal
Recurrent neural networks employing Lyapunov exponents for analysis of ECG signals
Expert Systems with Applications: An International Journal
Epileptic seizure detection in EEGs using time-frequency analysis
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Entropies based detection of epileptic seizures with artificial neural network classifiers
Expert Systems with Applications: An International Journal
Evolving simple feed-forward and recurrent ANNs for signal classification: a comparison
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Extreme energy difference for feature extraction of EEG signals
Expert Systems with Applications: An International Journal
Manifold analysis in reconstructed state space for nonlinear signal classification
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
Epileptic EEG detection using the linear prediction error energy
Expert Systems with Applications: An International Journal
Computers in Biology and Medicine
Combination of EEG complexity and spectral analysis for epilepsy diagnosis and seizure detection
EURASIP Journal on Advances in Signal Processing
Expert Systems with Applications: An International Journal
A machine learning approach to classify vigilance states in rats
Expert Systems with Applications: An International Journal
Epileptic seizure detection on EEG signal using statistical signal processing and neural networks
SENSIG'08 Proceedings of the 1st WSEAS international conference on Sensors and signals
Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study
Artificial Intelligence in Medicine
Detection of epileptic electroencephalogram based on Permutation Entropy and Support Vector Machines
Expert Systems with Applications: An International Journal
Review article: Human scalp EEG processing: Various soft computing approaches
Applied Soft Computing
Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition
Computer Methods and Programs in Biomedicine
Clustering technique-based least square support vector machine for EEG signal classification
Computer Methods and Programs in Biomedicine
A tunable support vector machine assembly classifier for epileptic seizure detection
Expert Systems with Applications: An International Journal
Application of Higher Order Spectra to Identify Epileptic EEG
Journal of Medical Systems
Expert Systems with Applications: An International Journal
Time-frequency distributions in the classification of epilepsy from EEG signals
Expert Systems with Applications: An International Journal
Classification of Epilepsy Using High-Order Spectra Features and Principle Component Analysis
Journal of Medical Systems
International Journal of Artificial Intelligence and Soft Computing
Automated EEG analysis of epilepsy: A review
Knowledge-Based Systems
Computer Methods and Programs in Biomedicine
Hi-index | 12.08 |
There are a number of different quantitative models that can be used in a medical diagnostic decision support system including parametric methods, non-parametric methods and several neural network models. Unfortunately, there is no theory available to guide model selection. The aim of this study is to evaluate the diagnostic accuracy of the recurrent neural networks (RNNs) employing Lyapunov exponents trained with Levenberg-Marquardt algorithm on the electroencephalogram (EEG) signals. An approach based on the consideration that the EEG signals are chaotic signals was used in developing a reliable classification method for electroencephalographic changes. This consideration was tested successfully using the non-linear dynamics tools, like the computation of Lyapunov exponents. We explored the ability of designed and trained Elman RNNs, combined with the Lyapunov exponents, to discriminate the EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures). The RNNs achieved accuracy rates which were higher than that of the feedforward neural network models. The obtained results demonstrated that the proposed RNNs employing the Lyapunov exponents can be useful in analyzing long-term EEG signals for early detection of the electroencephalographic changes.