Automatic recognition of sleep spindles in EEG via radial basis support vector machine based on a modified feature selection algorithm

Abstract

This paper presents an application of a radial basis support vector machine (RB-SVM) to the recognition of the sleep spindles (SSs) in electroencephalographic (EEG) signal. The proposed system comprises of two stages. In the first stage, for feature extraction, a set of raw amplitude values, a set of discrete cosine transform (DCT) coefficients, a set of discrete wavelet transform (DWT) approximation coefficients and a set of adaptive autoregressive (AAR) parameters are calculated and extracted from signals separately as four different sets of feature vectors. Thus, four different feature vectors for the same data are comparatively examined. In the second stage, these features are then selected by a modified adaptive feature selection method based on sensitivity analysis, which mainly supports input dimension reduction via selecting the most significant feature elements. Then, the feature vectors are classified by a support vector machine (SVM) classifier, which is relatively new and powerful technique for solving supervised binary classification problems due to it’s generalization ability. Visual evaluation, by two electroencephalographers (EEGers), of 19 channel EEG records of six subjects showed that the best performance is obtained with an RB-SVM providing an average sensitivity of 97.7%, an average specificity of 97.4% and an average accuracy of 97.5%.