An efficient classification of epilepsy risk levels from EEG signals using hard thresholding computation applied to code converters

Epilepsy is the most prevalent neurological disorder affecting the autonomous nervous system to a higher extent. Characterized by the sudden onset of recurrent, regressive and transient disturbances epilepsy occurs due to the hyper synchronization of the neurons in the cortical regions of the brain. The epileptic patient always experiences continuous electrical discharges in the brain. The recurrent seizures are termed as epileptic seizures. To monitor those epileptic seizures, Electroencephalography (EEG) signals are highly useful. This paper implements the performance comparison of various thresholding techniques with Sparse Representation Classifiers (SRC) as Post Classifiers for the Classification of Epilepsy Risk Levels from EEG signals. The bench mark parameters used here are Performance Index (PI), Quality Values (QV), Time Delay, Accuracy, Specificity and Sensitivity.