Neural network classification of EEG signal for the detection of seizure

Brain is the strongest part of the human body which consists of the number of neurons. Electrical activity of the brain can be measured using many techniques in which EEG is widely used. Any change in electrical signal will define a particular abnormality in human. This paper, suggest a algorithm for the EEG signal analysis for the detection of seizure using wavelet transform and statistical parameters. Data set consists of two sampling rates, one with 128Hz and another with sampling rate of 1024Hz. Feature extraction was done using discrete wavelet transform. Once a feature extraction is done the data will be given to a neural network for the classification. A multilayered neural network was used classify seizure and normal person. The proposed algorithm is tested on 23 data sets. Classification accuracy of 100% has been achieved for the sampling rate of 1024 and 85% for the data with sampling rate of 128. Total system accuracy achieved is 92.5%.