Performance Analysis of Ica, Pca as Dimensionality Reduction Techniques and Approximate Entropy, Src as Post Classifiers for the Classification of Epilepsy Risk Levels from Eeg Signals

Characterized by recurrent and rapid seizures, epilepsy is a great threat to the livelihood of the human beings. A significant clinical tool for the study, analysis and diagnosis of the epilepsy is electroencephalogram (EEG) .In this paper the high dimensional EEG data is reduced to a low dimension by techniques such as Independent Component Analysis (ICA) and Principal Component Analysis (PCA). After employing them as dimensionality reduction techniques, Approximate Entropy (ApEn) and Sparse Representation Classifiers (SRC) are used 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.