K-complexes like spindles are hallmark patterns of stage 2 sleep. Due to correlation between these patterns and some diseases, it is necessary to develop algorithms to detect them. In this study, a new method is used to detect K-complexes automatically. 10 time-series and chaotic features were used in order to extract the K-complex waves from stage 2 sleep. To use the most effective features, feature space dimension is reduced with Sequential Forward Selection method. The reduced feature space is classified using Generalized Radial Basis Function Extreme Learning Machine (MELM-GRBF) algorithm. GRBFs make the modification of the RBF possible by adjusting a new parameter . We're applied this methodology to K-complex classification for the first time. The classifier gives noticeably better results compared to ELM-RBF method for sensitivity and accuracy .± . and . ± . , respectively.

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