A simplified epilepsy classification technique utilizing svd

Abstract

EEG signals represent both the brain function and also the status of the whole body, i.e. a simple action as blinking the eyes introduces oscillation in the EEG records. The EEG is a direct way to measure neural activities and it is important in the area of biomedical research to understand and develop new processing techniques. EEG signal pre-processing and postprocessing methods include EEG signal modeling, segmentation, filtering and de-noising, and EEG processing methods which consist of two tasks, namely, feature extraction/dimensionality reduction and classification. In this paper, the performance analysis of Independent Component Analysis (ICA) is considered as a dimensionality reduction technique followed by Singular Value Decomposition (SVD) as a Post Classifier for the Classification of Epilepsy Risk Levels from EEG Signals. The analysis is done in terms of bench mark parameters such as Performance Index (PI), Quality Values (QV), Sensitivity, Specificity and Time Delay.