Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Machine Learning
Classification of EEG signals using the wavelet transform
Signal Processing
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Kolmogorov Complexity of Finite Sequences and Recognition of Different Preictal EEG Patterns
CBMS '95 Proceedings of the Eighth Annual IEEE Symposium on Computer-Based Medical Systems
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Computers in Biology and Medicine
Features extracted by eigenvector methods for detecting variability of EEG signals
Pattern Recognition Letters
Time-frequency feature extraction of newborn EEG seizure using SVD-based techniques
EURASIP Journal on Applied Signal Processing
Analysis of EEG signals by combining eigenvector methods and multiclass support vector machines
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
Decision support systems for time-varying biomedical signals: EEG signals classification
Expert Systems with Applications: An International Journal
A New Approach to Automated Epileptic Diagnosis Using EEG and Probabilistic Neural Network
ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Lyapunov exponents/probabilistic neural networks for analysis of EEG signals
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
Epileptic seizure detection in EEGs using time-frequency analysis
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Recurrent neural networks employing Lyapunov exponents for EEG signals classification
Expert Systems with Applications: An International Journal
Alias-free generalized discrete-time time-frequency distributions
IEEE Transactions on Signal Processing
Multiclass Support Vector Machines for EEG-Signals Classification
IEEE Transactions on Information Technology in Biomedicine
Entropy-based algorithms for best basis selection
IEEE Transactions on Information Theory - Part 2
| Hi-index | 12.05 |
In this paper we propose a novel recognition algorithm for the discrimination of epilepsy based on electroencephalogram (EEG) signals. We validate the algorithm on a benchmark dataset in order to compare the algorithm with other algorithms in the literature. More specifically, features were extracted from the bilinear time-frequency distributions (TFD) of the EEG signal. A one-against-one decomposition is used to break the multi-class problem into binary subproblems solvable with a support vector machine (SVM). The decomposition permitted binary subproblem-dependent feature libraries to be constructed from biologically inspired features derived from conditional moments calculated from EEG TFD. This results in a flexible, class-dependent feature selection based on a forward selection wrapper representing a departure from prior work which tends to utilize the same set of features to delineate all classes. We investigated the sensitivity of the classification accuracy to changes in the proportion of data used to train the algorithm. It was found that the distribution of classification accuracies was statistically similar over a range of proportions of data used to train the algorithm. This served to validate our algorithm in a statistical sense and represents a significant departure from literature, which tends to report only the best result for a given classification algorithm. To the best of our knowledge, the newly introduced algorithm is able to outperform the best reported accuracy in literature for the problem considered in this paper.