Detection of seizure activity in EEG by an artificial neural network: a preliminary study
Computers and Biomedical Research
Classification of EEG signals using the wavelet transform
Signal Processing
Intelligent optimal control with dynamic neural networks
Neural Networks
Wavelet analysis of generalized tonic-clonic epileptic seizures
Signal Processing
Estimation of the self-similarity parameter using the wavelet transform
Signal Processing
On three intelligent systems: dynamic neural, fuzzy, and wavelet networks for training trajectory
Neural Computing and Applications
Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients
Expert Systems with Applications: An International Journal
Wavelet neural networks for function learning
IEEE Transactions on Signal Processing
Trajectory priming with dynamic fuzzy networks in nonlinear optimal control
IEEE Transactions on Neural Networks
Gaussian networks for direct adaptive control
IEEE Transactions on Neural Networks
EEG signal classification using wavelet feature extraction and a mixture of expert model
Expert Systems with Applications: An International Journal
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
Function approximation using artificial neural networks
MATH'07 Proceedings of the 12th WSEAS International Conference on Applied Mathematics
Towards Personalized Neural Networks for Epileptic Seizure Prediction
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Function approximation using artificial neural networks
WSEAS Transactions on Mathematics
Epileptic Spike Recognition in Electroencephalogram Using Deterministic Finite Automata
Journal of Medical Systems
Classification of EEG signals using relative wavelet energy and artificial neural networks
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
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
Expert model for detection of epileptic activity in EEG signature
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
Computers in Biology and Medicine
Detection of seizures in EEG using subband nonlinear parameters and genetic algorithm
Computers in Biology and Medicine
Discrete wavelet transform-based time series analysis and mining
ACM Computing Surveys (CSUR)
Combination of EEG complexity and spectral analysis for epilepsy diagnosis and seizure detection
EURASIP Journal on Advances in Signal Processing
Parallel Algorithm to Analyze the Brain Signals: Application on Epileptic Spikes
Journal of Medical Systems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Detection of epileptic electroencephalogram based on Permutation Entropy and Support Vector Machines
Expert Systems with Applications: An International Journal
Discrete harmony search based expert model for epileptic seizure detection in electroencephalography
Expert Systems with Applications: An International Journal
Automatic seizure detection based on support vector machines with genetic algorithms
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Journal of Medical Systems
Classification of EMG signals using combined features and soft computing techniques
Applied Soft Computing
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Hi-index | 12.06 |
Epileptic seizures are manifestations of epilepsy. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. Wavelet transform is particularly effective for representing various aspects of non-stationary signals such as trends, discontinuities, and repeated patterns where other signal processing approaches fail or are not as effective. Through wavelet decomposition of the EEG records, transient features are accurately captured and localized in both time and frequency context. This paper deals with a novel method of analysis of EEG signals using discrete wavelet transform, and classification using ANN. EEG signals were decomposed into the frequency sub-bands using wavelet transform. Then these sub-band frequencies were used as an input to an ANN with two discrete outputs: normal and epileptic. In this study, FEBANN and DWN based classifiers were developed and compared in relation to their accuracy in classification of EEG signals. The comparisons between the developed classifiers were primarily based on analysis of the ROC curves as well as a number of scalar performance measures pertaining to the classification. The DWN-based classifier outperformed the FEBANN based counterpart. Within the same group, the DWN-based classifier was more accurate than the FEBANN-based classifier.