Sleep Spindle is the hallmark of the second stage of sleep in EEG signal. It had been analyzed using different methods, including Fourier transform, parametric and non-parametric models, higher order statistics and spectra, and also time-frequency methods such as wavelet transform, and matching pursuit. In this study, bump modeling has been used to analyze sleep spindle. Bump modeling is a method which represents the time-frequency map of signals with a number of elementary functions. Results of this work demonstrate that bump modeling is capable of analyzing different sleep spindle patterns in sleep EEG signals successfully.