Classification of EEG signals using the wavelet transform
Signal Processing
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
EEG signal classification using wavelet feature extraction and a mixture of expert model
Expert Systems with Applications: An International Journal
Entropies for detection of epilepsy in EEG
Computer Methods and Programs in Biomedicine
Recurrent neural networks employing Lyapunov exponents for EEG signals classification
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Detection of Unusual Objects and Temporal Patterns in EEG Video Recordings
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
International Journal of Data Analysis Techniques and Strategies
Classification of electroencephalogram signals with combined time and frequency features
Expert Systems with Applications: An International Journal
Artificial Intelligence in Medicine
Drawing boundaries: using individual evolved class boundaries for binary classification problems
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Detection of epileptic electroencephalogram based on Permutation Entropy and Support Vector Machines
Expert Systems with Applications: An International Journal
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Automated EEG analysis of epilepsy: A review
Knowledge-Based Systems
Hi-index | 12.06 |
The aim of this study is to classification of EEG signals using a new hybrid automated identification system based on Artificial immune recognition system (AIRS) with fuzzy resource allocation mechanism, principal component analysis (PCA) and fast Fourier transform (FFT) method. EEG signals used belong to normal subject and patient that has epileptic seizure. The proposed system has three stages: (i) feature extraction using Welch (FFT) method, (ii) dimensionality reduction using PCA, and (iii) EEG classification using AIRS with fuzzy resource allocation. We have used the 10-fold cross-validation, classification accuracy, sensitivity and specificity analysis, and confusion matrix to show the robustness and efficient of proposed system. The obtained classification accuracy is about 100% and it is very promising compared to the previously reported classification techniques.