Determining mental state from EEG signals using parallel implementations of neural networks
Scientific Programming - On applications analysis
Detection of seizure activity in EEG by an artificial neural network: a preliminary study
Computers and Biomedical Research
Neural networks and logistic regression: Part I
Computational Statistics & Data Analysis
Neural networks and logistic regression: Part II
Computational Statistics & Data Analysis
On-line Successive Synthesis of Wavelet Networks
Neural Processing Letters
Logistic regression and artificial neural network classification models: a methodology review
Journal of Biomedical Informatics
Wavelet neural networks for function learning
IEEE Transactions on Signal Processing
Using wavelet network in nonparametric estimation
IEEE Transactions on Neural Networks
Analysis and synthesis of feedforward neural networks using discrete affine wavelet transformations
IEEE Transactions on Neural Networks
Comparison of AR and Welch Methods in Epileptic Seizure Detection
Journal of Medical Systems
Atrial fibrillation classification with artificial neural networks
Pattern Recognition
Journal of Medical Systems
Automatic seizure detection based on time-frequency analysis and artificial neural networks
Computational Intelligence and Neuroscience - Regular issue
Function approximation using artificial neural networks
MATH'07 Proceedings of the 12th WSEAS International Conference on Applied Mathematics
Cross-correlation aided support vector machine classifier for classification of EEG signals
Expert Systems with Applications: An International Journal
Function approximation using artificial neural networks
WSEAS Transactions on Mathematics
Expert Systems with Applications: An International Journal
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Application of self-adaptive wavelet neural networks in ultrasonic detecting
ASID'09 Proceedings of the 3rd international conference on Anti-Counterfeiting, security, and identification in communication
EEMD method and WNN for fault diagnosis of locomotive roller bearings
Expert Systems with Applications: An International Journal
Review article: Human scalp EEG processing: Various soft computing approaches
Applied Soft Computing
Engineering Applications of Artificial Intelligence
A novel mobile epilepsy warning system
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Single-trial EEG classification of artifacts in videos
ACM Transactions on Applied Perception (TAP)
Identification of motor imagery tasks through CC-LR algorithm in brain computer interface
International Journal of Bioinformatics Research and Applications
Wavelet neural networks: A practical guide
Neural Networks
Human lower extremity joint moment prediction: A wavelet neural network approach
Expert Systems with Applications: An International Journal
Hi-index | 0.01 |
Since EEG is one of the most important sources of information in therapy of epilepsy, several researchers tried to address the issue of decision support for such a data. In this paper, we introduce two fundamentally different approaches for designing classification models (classifiers); the traditional statistical method based on logistic regression and the emerging computationally powerful techniques based on artificial neural networks (ANNs). Logistic regression as well as feedforward error backpropagation artificial neural networks (FEBANN) and wavelet neural networks (WNN) based classifiers were developed and compared in relation to their accuracy in classification of EEG signals. In these methods we used FFT and autoregressive (AR) model by using maximum likelihood estimation (MLE) of EEG signals as an input to classification system with two discrete outputs: epileptic seizure or nonepileptic seizure. By identifying features in the signal we want to provide an automatic system that will support a physician in the diagnosing process. By applying AR with MLE in connection with WNN, we obtained novel and reliable classifier architecture. The network is constructed by the error backpropagation neural network using Morlet mother wavelet basic function as node activation function. The comparisons between the developed classifiers were primarily based on analysis of the receiver operating characteristic (ROC) curves as well as a number of scalar performance measures pertaining to the classification. The WNN-based classifier outperformed the FEBANN and logistic regression based counterpart. Within the same group, the WNN-based classifier was more accurate than the FEBANN-based classifier, and the logistic regression-based classifier.