Abstract:
Brain signals are important in diagnosing various disorders and abnormalities in the human body. These signals are recorded by scalp electrodes and are called as EEG signals. EEG signals are a mixture of signals from different brain regions which contain artifacts along with original information. EEG has several limitations; most important is its poor spatial resolution. EEG is most sensitive to a particular set of post-synaptic potentials. For this reason selected 25 samples (epilpsey) A computer program (Matlab) used for makining processing to remove noise from EEG signal and then estimating the EEG parameters for calculating the statistical parameters(mean,median,root mean square, standard deviation ). The aim of this study is to classify the EEG signal as normal or abnormal. It is proposed to develop an automated system for the classification of brain abnormalities. The proposed system includes pre-processing, feature extraction, feature selection and classification. In pre-processing the noises are removed. The discrete wavelet transform is used to decompose the EEG signal into sub-band signals. The feature extraction methods are used to extract the time domain and frequency domain features of the EEG signal ,finally classification by using the table obtained from time and frequency domain features then calculated the Euclidean distance, calculated eucsum and the value was rounded
Finally it found that the accuracy is equall 84%,sensitivity 86.66%,specificity 80%,positive protective value 86.66% and negative protective value 80%.
