Evolutionary model selection in a wavelet-based support vector machine for automated seizure detection

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...

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Bibliographic Details
Published in:Expert systems with applications 2011-09, Vol.38 (9), p.10751-10758
Main Authors: Zavar, M., Rahati, S., Akbarzadeh-T, M.-R., Ghasemifard, H.
Format: Article
Language:English
Subjects:
Accuracy
Classification
ECG
Entropy
Epilepsy
Evolutionary
Genetic algorithm
Kernels
Model selection
Partial seizure
Seizing
Support vector machines
Wavelet kernel function
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Summary: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.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2011.01.087