Selecting Statistical Characteristics of Brain Signals to Detect Epileptic Seizures using Discrete Wavelet Transform and Perceptron Neural Network

Electroencephalogram signals (EEG) have always been used in medical diagnosis. Evaluation of the statistical characteristics of EEG signals is actually the foundation of all brain signal processing methods. Since the correct prediction of disease status is of utmost importance, the goal is to use those models that have minimum error and maximum reliability. In anautomatic epileptic seizure detection system, we should be able to distinguish between EEG signals before, during and after seizure. Extracting useful characteristics from EEG data can greatly increase the classification accuracy. In this new approach, we first parse EEG signals to sub-bands in different categories with the help of discrete wavelet transform(DWT) and then we derive statistical characteristics such as maximum, minimum, average and standard deviation for each sub-band. A multilayer perceptron (MLP)neural network was used to assess the different scenarios of healthy and seizure among the collected signal sets. In order to assess the success and effectiveness of the proposed method, the confusion matrix was used and its accuracy was achieved98.33 percent. Due to the limitations and obstacles in analyzing EEG signals, the proposed method can greatly help professionals experimentally and visually in the classification and diagnosis of epileptic seizures

Improved Shape Parameter Estimation in K Clutter with Neural Networks and Deep Learning

The discrimination of the clutter interfering signal is a current problem in modern radars’ design, especially in coastal or offshore environments where the histogram of the background signal often displays heavy tails. The statistical characterization of this signal is very important for the cancellation of sea clutter, whose behavior obeys a K distribution according to the commonly accepted criterion. By using neural networks, the authors propose a new method for estimating the K shape parameter, demonstrating its superiority over the classic alternative based on the Method of Moments. Whereas both solutions have a similar performance when the entire range of possible values of the shape parameter is evaluated, the neuronal alternative achieves a much more accurate estimation for the lower Fig.s of the parameter. This is exactly the desired behavior because the best estimate occurs for the most aggressive states of sea clutter. The final design, reached by processing three different sets of computer generated K samples, used a total of nine neural networks whose contribution is synthesized in the final estimate, thus the solution can be interpreted as a deep learning approximation. The results are to be applied in the improvement of radar detectors, particularly for maintaining the operational false alarm probability close to the one conceived in the design