Detection of primary brain tumor present in eeg signal using wavelet transform and neural network

Abstract

The Brain tumor is life threatening disease of brain. The brain contains about 10 Billion or more working brain cells. Damazied brain cells are diagnosed themselves by dividing to make more cells. Normally, this turnover takes place in an orderly and controlled manner. If, for some reason, the process gets out of control, the cells will continue to divide, developing into a lump, which is called a tumor. Brain tumors are broadly categorized in to two types, primary and secondary brain tumor. The detection of primary brain tumor (Gliomas) is possible by analyzing EEG signals. This paper, proposes a technique to classification EEG signal for detection of primary brain tumor detection, which is combination of multi-wavelet transform and artificial neural network. Irregularity in the EEG signals is measured by using the Approximate Entropy. The proposed technique is implemented, tested and compared with existing method based on performance indices such as sensitivity, specificity, accuracy; results are promising with accuracy (96%).