Methodology for Epileptic Episode Detection Using Complexity-Based Features
Epilepsy is a neurological disease with a high prevalence on human beings, for which an accurate diagnosis remains as an essential step for medical treatment. Making use of pattern recognition tools is possible to design accurate automatic detection systems, capable of helping medical diagnostic. The present work presents an automatic epileptic episode methodology, based on complexity analysis where 3 classical nonlinear dynamic based features are used in conjunction with 3 regularity measures. k-nn and Support Vector Machines are used for classification. Results, superior to 98% confirm the discriminative ability of the presented methodology on epileptic detection labours.
KeywordsSupport Vector Machine Extreme Learning Machine Hurst Exponent Sample Entropy Approximate Entropy
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