• Moushmi Kar
• Laxmikant Dewangan

Epilepsy is the most prevalent neurological disorder in humans. Epilepsy is characterized by recurrent seizures .This happens due to an abnormality in brain wiring, an imbalance of nerve signaling chemicals called neurotransmitters, or some combination of these factors. Normally, neurons generate electrochemical impulses, act on other neurons, glands, and muscles to generate human thoughts, feelings and actions. Epilepsy is determined by EEG signal recording, which contain valuable information for understanding epilepsy. Epilepsy can create clear disturbance and leaves its signature on standard EEG signals. The detection of seizures occurring in the EEGs is an important component for the diagnosis and treatment of epilepsy. In recent years soft computing based techniques are attracting much attention from the scientific communities as an alternative tool. This technique for detection and classification of epilepsy has been reported by number of researchers. It is an important part of EEG based computer aided diagnosis (CAD) systems. This review investigates the application of different machine learning approaches for classification of EEG signals to detect epileptic and non epileptic seizures. Different components of machine learning such as feature extraction, feature selection and classification are explored and evaluated using CAD system.

EEG, Epilepsy, Computer aided diagnosis (CAD)

"Epilepsy Detection using EEG signals in machine learning paradigm: Review and Challenges", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 2, page no.1122-1124, February-2018, Available :http://www.jetir.org/papers/JETIR1802202.pdf

"Epilepsy Detection using EEG signals in machine learning paradigm: Review and Challenges", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 2, page no. pp1122-1124, February-2018, Available at : http://www.jetir.org/papers/JETIR1802202.pdf