Detection of spikes with artificial neural networks using raw EEG
Computers and Biomedical Research
Justification of a Neuron-Adaptive Activation Function
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
Data Mining " An Adaptive Neural Network Model for Financial Analysis
ICITA '05 Proceedings of the Third International Conference on Information Technology and Applications (ICITA'05) Volume 2 - Volume 02
EEG signal classification using wavelet feature extraction and a mixture of expert model
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A fuzzy clustering neural network architecture for classification of ECG arrhythmias
Computers in Biology and Medicine
Neuron-adaptive higher order neural-network models for automated financial data modeling
IEEE Transactions on Neural Networks
ACS'08 Proceedings of the 8th conference on Applied computer scince
Comparative clustering analysis of bispectral index series of brain activity
Expert Systems with Applications: An International Journal
Entropies based detection of epileptic seizures with artificial neural network classifiers
Expert Systems with Applications: An International Journal
Expert model for detection of epileptic activity in EEG signature
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
A decision support system for EEG signals based on adaptive fuzzy inference neural networks
Journal of Computational Methods in Sciences and Engineering
Hi-index | 12.06 |
This paper presents new neural network models with adaptive activation function (NNAAF) to detect epileptic seizure. Our NNAAF models included three types named as NNAAF-1, NNAAF-2 and NNAAF-3. The activation function of hidden neuron in the model of NNAAF-1 is sigmoid function with free parameters. In the second model, NNAAF-2, activation function of hidden neuron is sum of sigmoid function with free parameters and sinusoidal function with free parameters. In the third model, NNAAF-3, hidden neurons' activation function is Morlet Wavelet function with free parameters. In addition, we implemented traditional multilayer perceptron (MLP) neural network (NN) model with fixed sigmoid activation function in the hidden layer to compare NNAAF models. The proposed models were trained and tested using 5-fold cross-validation to prove robustness of these models and to find the best model. We achieved 100% average sensitivity, average specificity, and approximately 100% average classification rate in all the models. It was seen that their speeds and the number of maximum iteration were changed for each model. The training time and the number of maximum iteration were reduced on about 50% using NNAAF-3 model. Hence it can be remarkable that NNAAF-3 is more suitable than the other models for real-time application.