Prediction of neurological disorders using optimized neural network

In the earth there is distressing number of people who suffer from neurological disorders. Electroencephalogram EEG signal are chaotic time series signals and tends to change rapidly with the patient condition. From normal to severe conditions the nature of signals has drastic difference and with change in amplitude as well as frequencies. Prediction of these signals in the early stage is mere a complex task. The work is focused on predicting individual state signal. The Generalized Regression neural networks (GRNN) variant of Radial basis function neural network (RBFNN) is best at the work but require a good choice of its spread factor. Choosing accurate spread factor is not a simple work, and requires experiments to be carried out, which is time consuming and tedious. The search of the particles in the swarm is opted for finding the spread factor for GRNN. The combination of particle swarm optimization (PSO) with GRNN greatly helped in improving prediction accuracy of GRNN to various neurological disorders.