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Optimal kernel learning for eeg based sleep scoring system

Authors:Girisha Garg, Vijander Sing, Mudita Grover, Nidhi, J.R.P Gupta
Int J Biol Med Res. 2011; 2(4): 1220 – 1225  |  PDF File


In recent years, Kernel methods have received major attention, particularly due to the increased popularity of the Support Vector Machines. Kernel functions can be used in many applications as they provide a simple bridge from linearity to non-linearity for algorithms which can be expressed in terms of dot products. Choosing the optimal kernel is very crucial for implementing Support Vector Machines (SVM) and highly depends on the nature of the problem. SVM has been widely used in EEG signal processing. In this paper two kernel functions, mahalanobis kernel and Multi Layer Perceptron Kernel (MLP) have been introduced for sleep scoring system using EEG (Electroencephalograph) signal processing and an empirical comparison has been made for various kernel functions. The results obtained depict that for automatic sleep scoring system, best performance is achieved by using Radial Basis Function and Polynomial kernel.