The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Application of fractal theory in analysis of human electroencephalographic signals
Computers in Biology and Medicine
Engineering Applications of Artificial Intelligence
Wavelet support vector machine
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Computers and Electronics in Agriculture
Automatic classification of sleep stages based on the time-frequency image of EEG signals
Computer Methods and Programs in Biomedicine
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Support vector machines (SVM) have in recent years been gainfully used in various pattern recognition applications. Based on statistical learning theory, this paradigm promises strong robustness to noise and generalization to unseen data. As in any classification technique, appropriate choice of the kernels and input features play an important role in SVM performance. In this study, an evolutionary scheme searches for optimal kernel types and parameters for automated seizure detection. We consider the Lyapunov exponent, fractal dimension and wavelet entropy for possible feature extraction. The classification accuracy of this approach is examined by applying the MIT (Massachusetts Institute of Technology) dataset and comparing results with the SVM. The MIT-BIH dataset has the electrocardiographic (ECG) changes in patients with partial epilepsy which two types ECG beats (partial epilepsy and normal). A comparison of results shows that performance of the evolutionary scheme outweighs that of support vector machine. In the best condition, the accuracy rate of the proposed approaches reaches 100% for specificity and 96.29% for sensitivity.