• Aarti Sharma, J K Rai, and R P Tewari.
• a Department of ECE , Inderprastha Engineering College , Ghaziabad , India.
• J Med Eng Technol. 2018 Apr 1; 42 (3): 203-216.

AbstractElectric activity of brain gets disturbed prior to epileptic seizure onset. Early prediction of an upcoming seizure can help to increase effectiveness of antiepileptic drugs. The scalp electroencephalogram signals contain information about the dynamics of brain and have been used to predict an upcoming seizure and localise its zone. The objective of this paper is to localise the epileptogenic region and predict an upcoming seizure at the earliest. To localise epileptogenic region, Electroencephalogram signals are categorised into four regions of brain (Frontal, Temporal, Parietal and Central). For each signal seventy-two (72) parameters in frequency domain have been extracted by using ten minute non overlapping window. Four prominent ratio parameters, γ1/γ5, γ3/γ1, θ/γ2 and γ4/θ have been identified as best parameters based on relative fisher score. Zone 2 shows the highest change in all the parameters as compared to the other zones. So, temporal region is identified as the epileptogenic region in this work. For prediction of the epileptic seizure machine learning algorithm artificial neural network (ANN) is proposed. The proposed machine learning algorithm has an accuracy of 92.3%, sensitivity of 100% and specificity of 83.3%.

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