Epilepsy is one of the most common neurological disorders causing from repeating brain seizures that are the result of the temporal and sudden electrical disturbance of the brain. Electroencephalogram (EEG) signal plays an important role in the diagnosis of epilepsy. This project proposed to develop a system that can detect epilepsy based on EEG signal using artificial neural network. Discrete Wavelet Transform (DWT) and Fast Fourier Transform (FFT) were applied as feature extraction methods. These features then set as input to the feedforward neural network with backpropagation training algorithm to get the classification accuracy. The accuracy of DWT with 10000 epochs is 97% while accuracy of FFT method gives 53.889% accuracy. The combination of DWT and FFT extracted features give the highest accuracy, which is 98.889%. The classification accuracy depends on the number of epoch and the features from the feature extraction. Increased number of epoch gives long response time to train the network.
Thresholding is one of the powerful methods in segmentation phase. Numerous methods were proposed to segment the foreground from the background but there is limited number of studies that analyse the effect of noise since the present of noise will affect the performance of the thresholding method. In this paper, the main idea is to analyse the effect of noise in Inverse Surface Adaptive Thresholding (ISAT) method. ISAT method is known as an excellent method to segment the image with the present of noise. The result of this analysis can be a guideline to researcher when implementing ISAT method especially in medical image diagnosis. Initially, several images with different noise variations were prepared and underwent ISAT method. In ISAT method, several image processing methods were incorporated namely edge detection, Otsu thresholding and inverse surface construction. The resulting images were evaluated using Misclassification Error (ME) to evaluate the performance of the segmentation result. Based on the obtained results, ISAT performance is consistent although the noise percentage increases from 5% to 25%.
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