Epileptic Seizure Detection from Eeg Signals by Adaptive Wavelets and Support Vector Machine - Venkateshkanna, T.; K, Punithavathy; R, Vallikannu (International Journal of Engineering and Technology, 2018)
Recent advances in computer technologies help to diagnose various disorders of the brain using Electroencephalogram (EEG) signals. The importance of a diagnostic application is to reduce the False Positive (FP) cases. To achieve this, an efficient approach for epileptic seizure classification is proposed using adaptive wavelets called Empirical Wavelet Transform (EWT).  The different modes of EEG signals are extracted using EWT and then features from the different modes are extracted. Then, classification is done by a typical machine learning technique, Support Vector Machine (SVM). The performance of EWT-SVM system is evaluated using confusion matrix. From the confusion matrix, sensitivity and specificity are computed. In this paper, an efficient approach for the diagnosis of epileptic seizure using EEG signals is designed. At first, the given EEG signal is decomposed using EWT to extract the information of all components. The EWT energy features are extracted from the EEG signals which are used for training the SVM classifier. Results show that EWT-SVM system reduces the FP cases with 100% of accuracy, sensitivity and specificity which indicates that no misclassification occurs.