• Mythilisha S
• Jintu Ann Mathew
• Adhiruban K

Detection of epileptic seizure exercises from long haul multi-channel electroencephalogram (EEG) signals assumes a noteworthy job in the convenient treatment of the patients with epilepsy. Visual distinguishing proof of epileptic seizure in long haul EEG is lumbering and monotonous for nervous system specialists, which likewise prompt human blunder. In this way, a computerized apparatus for exact recognition of seizures in a long haul multi-channel EEG is basic for the clinical conclusion. This investigation proposes a calculation utilizing multi-highlights and K-Nearest Neighbor (KNN) classifier. At first, pre-processing was performed to evacuate the electrical cable commotion and movement ancient rarities. Four highlights, to be specific force ghostly thickness, Shannon Entropy, Renyi Entropy, and Teager vitality, were extricated. Single and multi-highlights were taken care of to the KNN classifier to assess the presentation of the investigation. The reenactment results demonstrated precision, affecting ability, particularity of 99 %, 97.1% and 97.8% individually, utilizing multi-highlights.

Seizure detection,EEG, ESD,Epileptic Seizure Detector

"Epileptic Seizure Detector (ESD)", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 9, page no.1340-1344, September-2020, Available :http://www.jetir.org/papers/JETIR2009184.pdf

"Epileptic Seizure Detector (ESD)", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 9, page no. pp1340-1344, September-2020, Available at : http://www.jetir.org/papers/JETIR2009184.pdf