EEG Signals Processing by Using Wavelet Technique and Artificial Neural Networks

Abstract

Two artificial neural network systems were designed by using wavelet based features for the classification of normal and abnormal EEG signals were decomposed to 4 levels using Daubechies wavelet of order 2.These EEG signals were decomposed to four statistical features: minimum, maximum, mean and standard deviation to depict their distribution. These features computed over the wavelet coefficients for each level, and used as input to the artificial neural network systems. After training and testing the systems results were obtained for classification of signals. The Two type of neural networks(Feed Forward Back propagation and Cascade Forward Back propagation) were tested for sensitivity, specificity and accuracy it was found that the Cascade Forward Back propagation (CFBP) give more accurate results with an accuracy of 96.76%.