SOFT COMPUTING TECHNIQUES FOR EPILEPSY DIAGNOSIS- A CASE STUDY

This paper presents a review on various soft computing techniques for epilepsy diagnosis and classification problems. A case study of different types of epilepsy classification problems are analyzed using soft computing methods. The performance of each case is extensively studied. The physician's standard classification level is compared with all the cases. The false alarm rate, which is usually high in fuzzy models can be reduced by selecting appropriate searching algorithms in a new search domain. The detection of epilepsy through Dimensionality Reduction, Nonlinear Models and Soft computing Techniques are also discussed here. Finally, the detection of epilepsy through Nonlinear Models and Extreme Learning Machine (ELM) is also discussed here.