Electroencephalogram signal classification based on Fourier transform and Pattern Recognition Network for epilepsy diagnosis

Epilepsy is a central nervous system (CNS) disorder that affects nerve cells in the brain and produces seizures in which consciousness is lost. People with epilepsy have frequent seizures as a result of increased brain electrical activity, which disrupts the message system between brain cells, making epilepsy a serious condition that must be treated. Correct diagnosis of this disease can save the patient from death and complications caused by the disease. The recording of electroencephalogram (EEG) signals is an effective method for determining the electrical activity of the brain and learning a great deal about how the brain function. The Fast Fourier Transform (FFT), the Discrete Wavelet Transform (DWT), and a pattern recognition network are used in this study to offer a method for analyzing EEG signals. This study aimed to provide a multi-step algorithm for extracting signal features and diagnosing epilepsy. Wavelet transform is used to remove signal noise. For this purpose, the optimal wavelet mother (Dabichiz 8) was used. Then, signal features are extracted using the Fourier transform, and generate the EEG matrix. The features are regarded as the Pattern Recognition Network's input. There were 5% of test data and 80% of training data. 15% of the data were left over for validation. The architecture was thought to consist of one input layer (14 neurons = number of selected features), one hidden layer (6 neurons), and two output layers (2 neurons = number of sleep stages). The result of this output was compared with other classifiers, such as multilayer perceptron (MLP) neural network. The results show that using a Pattern Recognition Network can classify the features with 92.5% accuracy. Using this method can process the signal with high accuracy while being simple. In fact, a new method based on the architecture of a new network was presented for AC signal processing. Hence, the proposed method can diagnose epilepsy with high accuracy while removing signal noise.