Industrial Applications of Fuzzy Control
Industrial Applications of Fuzzy Control
Discovering Knowledge in Data: An Introduction to Data Mining
Discovering Knowledge in Data: An Introduction to Data Mining
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
EEG signal classification using wavelet feature extraction and a mixture of expert model
Expert Systems with Applications: An International Journal
Improved spiking neural networks for EEG classification and epilepsy and seizure detection
Integrated Computer-Aided Engineering
Expert Systems with Applications: An International Journal
Entropies for detection of epilepsy in EEG
Computer Methods and Programs in Biomedicine
Characterization of EEG-A comparative study
Computer Methods and Programs in Biomedicine
Non-linear analysis of EEG signals at various sleep stages
Computer Methods and Programs in Biomedicine
Recurrent neural networks employing Lyapunov exponents for EEG signals classification
Expert Systems with Applications: An International Journal
EEG signal classification using PCA, ICA, LDA and support vector machines
Expert Systems with Applications: An International Journal
Application of Higher Order Spectra to Identify Epileptic EEG
Journal of Medical Systems
Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks
IEEE Transactions on Information Technology in Biomedicine
Automated EEG analysis of epilepsy: A review
Knowledge-Based Systems
| Hi-index | 12.05 |
Electroencephalogram (EEG) signals are used to detect and study the characteristics of epileptic activities. Owing to the non-linear and dynamic nature of EEG signals, visual inspection and interpretation of these signals are tedious, time-consuming, error-prone, and subjected to inter-observer variabilities. Therefore, several Computer Aided Diagnostic (CAD) based studies have adopted non-linear techniques to study the normal, interictal, and ictal activities in EEGs. In this paper, we present a novel automatic technique based on data mining for epileptic activity classification. In order to compare our study with the results of relative studies in the literature, we used the widely used benchmark dataset from Bonn University for evaluation of our proposed technique. Hundred samples each in normal, interictal, and ictal categories were used. We decomposed these segments into wavelet coefficients using Wavelet Packet Decomposition (WPD), and extracted eigenvalues from the resultant wavelet coefficients using Principal Component Analysis (PCA). Significant eigenvalues, selected using the ANOVA test, were used to train and test several supervised classifiers using the 10-fold stratified cross validation technique. We obtained 99% classification accuracy using the Gaussian Mixture Model (GMM) classifier. The proposed technique is capable of classifying EEG segments with clinically acceptable accuracy using less number of features that can be extracted with less computational cost. The technique can be written as a software application that can be easily deployed at a low cost and used with almost no expert training. We foresee that this software can, in the future, evolve into an efficient adjunct tool that cannot only classify epileptic activities in EEG signals but also automatically monitor the onset of seizures and thereby aid the doctors in providing better and timely care for the patients suffering from epilepsy.