Automatic epilepsy detection using hybrid decomposition with multi class support vector method
The epilepsy has been detected from the electroencephalogram (EEG) by utilizing the complex wavelet transform with support vector machine. These methods successfully examine each and every frequency in the EEG signal for detecting the epilepsy with effective manner because epilepsy is one of the most important brain abnormalities which affect the entire brain function. The epilepsy is occurring due to the brain stroke, lack of blood flow, brain fever and so on, these lead to create the number of human deaths. So, the brain epilepsy needs to be analyzed and it has to be detected for improving the epilepsy recognition rate. But the major problem with the epilepsy recognition is the accuracy and efficiency of the classifier because of the traditional approximation entropy only extracts the minimum number of features which is difficult to detect the epilepsy with effective manner. These problems increase the false classification rate while analyzing the brain features. So, brain abnormalities has been automatically recognized by using the various machine learning steps like preprocessing, signal decomposition, feature extraction, feature selection and classification. In this research, the brain Epilepsy is recognized by applying the Hybrid Multi Class Support Vector Machine (HMCSVM). Then the performance of the system is analyzed using the experimental results and discussions.
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Sujatha, Krishnamoorthy, "Automatic epilepsy detection using hybrid decomposition with multi class support vector method" (2020). Kean Publications. 1232.