• Divya Dipak Gorde
• Aashish Bardekar

This paper presents a signal modeling based new methodology of automatic seizure detection in EEG signals. A seizure is an event that causes an abrupt surge of electrical activity in the brain, where epilepsy is the disease involving recurrent unprovoked seizures. Worldwide, approximately 50 million people are affected by epilepsy and this is one of the most common neurological diseases. Electroencephalography (EEG) enlighten about the state of the brain i.e. about the electrical bustle going on in the brain. The electrical activity measured as voltage at different points of brain act as basis of EEG. These signals are generally time-varying and non-stationary in nature. Diagnosis of epilepsy requires long term electroencephalography (EEG) monitoring. The interpretation of long-term EEG monitoring takes a lot of time and requires the assistance of experienced experts. To optimize the seizure detection we used feature extraction concept. In order to overcome these limitations we apply machine learning technique.

Detection, EEG , Epilepsy, Machine Learning Technique, Seizure ,Non- Seizure, Signals

"Signal Modelling Approach For Classification of Epileptic Seizure EEG Signal Using Support Vector Machine", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.324-329, April-2019, Available :http://www.jetir.org/papers/JETIR1904850.pdf

"Signal Modelling Approach For Classification of Epileptic Seizure EEG Signal Using Support Vector Machine", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp324-329, April-2019, Available at : http://www.jetir.org/papers/JETIR1904850.pdf