ABSTRACT:
Epilepsy is the most common neurological disorder affecting fifty million people worldwide, eighty five percent of which belong to the developing countries. Around 2.4 million new cases occur every year globally. At least fifty of the epileptic cases begin at childhood or adolescence. Sudden onset may also be seen in geriatric population. Epileptic people have premature nature when compared to a normal person. Hence study of epilepsy has always been an utmost importance in the biomedical field of research. Processing of epileptic signals has become more mandatory for the perfect classification of the epileptic risk levels from EEG signals. In the field of computer vision and pattern recognition, a lot of dimensionality reduction techniques have gained much importance in both supervised and unsupervised learning tasks. This work emphasizes the use of Linear Graph Embedding as a dimensionality reduction technique for the processing of epileptic encephalographic signals. Keywords: Epilepsy, Dimensionality Reduction, Linear Graph Embedding.