Wavelet transforms and neural network approach for epileptical EEG

A technique proposed for the automatic detection of spikes in electroencephalograms (EEG). The important features of the raw EEG data are extracted using two methods.i.e, Wavelet Transform and Energy Estimation. This data is normalized and given as input to the Neural Network, which is trained using Back propagation algorithm. Energy Estimation is used as an amplitude threshold parameter. The Wavelet transform (WT) is a powerful tool for multi-resolution analysis of non-stationary signal as well as for signal compression, recognition and restoration, which uses Daubechies 4 as the mother wavelet. The details of the Wavelet decomposition level, 1,2,3 and energy estimation parameters are given as input to the neural network in order to detect spikes. The codes are written in C and implemented on the Texas Instruments TMS320C5410-100 processor board, and a Del Mar PWA EEG Amplifier. The waveforms are observed on MATLAB. The effectiveness of the proposed technique was confirmed with and EEG layouts.