Automated Detection of Brain Tumor in EEG Signals Using Artificial Neural Networks

Abstract

Electroencephalograms (EEGs) are progressively emerging as a significant measure of brain activity and they possess immense potential for the diagnosis and treatment of mental and brain diseases and abnormalities. EEGs are up-and-coming as a vital methodology to suit the increasing global demand for more affordable and effectual clinical and healthcare services, with fitting interpretation methods. This research paper presents an automated system for efficient detection of brain tumors in EEG signals using artificial neural networks (ANNs). The ANN employed in the proposed system is feedforward backpropagation neural network. Generally, the EEG signals are bound to contain an assortment of artifacts from both subject and equipment interferences along with essential information regarding abnormalities and brain activity (responses to certain stimuli). Initially, adaptive filtering is applied to remove the artifacts present in the EEG signal. Subsequently, generic features present in the EEG signal are extracted using spectral estimation. Specifically, spectral analysis is achieved by using Fast Fourier Transform that extracts the signal features buried in a wide band of noise. The clean EEG data thus obtained is used as training input to the feedforward backpropagation neural network. The trained feedforward backpropagation neural network when fed with a test EEG signal, effectively detects the presence of brain tumor in the EEG signal. The experimental results demonstrate the effectiveness of the proposed system in artifacts removal and brain tumor detection.