The nature of statistical learning theory
The nature of statistical learning theory
Detection of seizure activity in EEG by an artificial neural network: a preliminary study
Computers and Biomedical Research
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks
JVA '06 Proceedings of the IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing
EEG signal classification using wavelet feature extraction and a mixture of expert model
Expert Systems with Applications: An International Journal
Atrial fibrillation classification with artificial neural networks
Pattern Recognition
Wavelet/mixture of experts network structure for EEG signals classification
Expert Systems with Applications: An International Journal
A modified mixture of experts network structure for ECG beats classification with diverse features
Engineering Applications of Artificial Intelligence
Multiclass Support Vector Machines for EEG-Signals Classification
IEEE Transactions on Information Technology in Biomedicine
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Support vector machine (SVM) is a machine learning technique widely applied in classification problems. SVM are based on the Vapnik's Statistical Learning Theory, and successively extended by a number of researchers. On the order hand, the electroencephalogram (EEG) signal captures the electrical activity of the brain and is an important source of information for studying neurological disorders. In order to extract relevant information of EEG signal, a variety of computerized-analysis methods have been developed. Recent studies indicate that methods based on the nonlinear dynamics theory can extract valuable information from neuronal dynamics. However, many these of methods need large amount of data and are computationally expensive. From chaos theory, a global value that is relatively simple to compute is the fractal dimension (FD), it can be used to measure the geometrical complexity of a time series. The FD of a waveform represents a powerful tool for transient detection. In analysis of EEG this feature can been used to identify and distinguish specific states of physiologic function. A variety of algorithms are available for the computation of FD. In this work, we employ SVM to classify the EEG signals from healthy subjects and epileptic subjects using as the features vector the FD. From the experimental results, we can see that classification based on SVM with FD perform well in EEG signals classification, which indicates this classification method is valid and has promising application.