Epilepsy is a chronic disorder the hallmark of which is recurrent unprovoked seizures. Many
people with epilepsy have more than one type of seizures and may have other symptoms of
neurological problems as well. Epilepsy is caused due to sudden recurrent firing of the neurons
in the brain. The symptoms are convulsions dizziness and confusion. One out of every hundred
persons experiences a seizure at some time in their lives. It may be confused with other events
like strokes or migraines. Unfortunately the occurrence of an epileptic seizure seems
unpredictable and its process still is hardly understood. In India the number of persons
suffering from epilepsy is increasing every year. The complexity involved in the diagnosis and
therapy has to be cost effective in. In this project the authors applied an algorithm which is
used for a classification of the risk level of epilepsy in epileptic patients from
Electroencephalogram (EEG) signals. Dimensionality reduction is done on the EEG dataset by
applying Power Spectral density. The KNN Classifier and K-Means clustering is implemented on
these spectral values to epilepsy risk level detection. The Performance Index (PI) and Quality
Value (QV) are calculated for the above methods. A group of twenty patients with known epilepsy
findings are used in this study.
