Classification of EEG signals using the wavelet transform
Signal Processing
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
SSVM: A Smooth Support Vector Machine for Classification
Computational Optimization and Applications
Lagrangian support vector machines
The Journal of Machine Learning Research
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
EEG signal classification using wavelet feature extraction and a mixture of expert model
Expert Systems with Applications: An International Journal
A neural-wavelet architecture for voice conversion
Neurocomputing
Automatic seizure detection based on time-frequency analysis and artificial neural networks
Computational Intelligence and Neuroscience - Regular issue
Speech, Audio, Image and Biomedical Signal Processing using Neural Networks
Speech, Audio, Image and Biomedical Signal Processing using Neural Networks
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
Multiclass Support Vector Machines for EEG-Signals Classification
IEEE Transactions on Information Technology in Biomedicine
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Gesture unit segmentation using support vector machines: segmenting gestures from rest positions
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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Objective: We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification. Methods and materials: The kernel machines investigated include the standard support vector machine (SVM), the least squares SVM, the Lagrangian SVM, the smooth SVM, the proximal SVM, and the relevance vector machine. An extensive series of experiments was conducted on publicly available data, whose clinical EEG recordings were obtained from five normal subjects and five epileptic patients. The performance levels delivered by the different kernel machines are contrasted in terms of the criteria of predictive accuracy, sensitivity to the kernel function/parameter value, and sensitivity to the type of features extracted from the signal. For this purpose, 26 values for the kernel parameter (radius) of two well-known kernel functions (namely, Gaussian and exponential radial basis functions) were considered as well as 21 types of features extracted from the EEG signal, including statistical values derived from the discrete wavelet transform, Lyapunov exponents, and combinations thereof. Results: We first quantitatively assess the impact of the choice of the wavelet basis on the quality of the features extracted. Four wavelet basis functions were considered in this study. Then, we provide the average accuracy (i.e., cross-validation error) values delivered by 252 kernel machine configurations; in particular, 40%/35% of the best-calibrated models of the standard and least squares SVMs reached 100% accuracy rate for the two kernel functions considered. Moreover, we show the sensitivity profiles exhibited by a large sample of the configurations whereby one can visually inspect their levels of sensitiveness to the type of feature and to the kernel function/parameter value. Conclusions: Overall, the results evidence that all kernel machines are competitive in terms of accuracy, with the standard and least squares SVMs prevailing more consistently. Moreover, the choice of the kernel function and parameter value as well as the choice of the feature extractor are critical decisions to be taken, albeit the choice of the wavelet family seems not to be so relevant. Also, the statistical values calculated over the Lyapunov exponents were good sources of signal representation, but not as informative as their wavelet counterparts. Finally, a typical sensitivity profile has emerged among all types of machines, involving some regions of stability separated by zones of sharp variation, with some kernel parameter values clearly associated with better accuracy rates (zones of optimality).