Sleep spindles are one of the most important short-lasting rhythmic events occurring in the EEG during Non-Rapid Eye Movement sleep. Their accurate identification in a polysomnographic signal is essential for sleep professionals to help them mark Stage 2 sleep. Visual spindle scoring however is a tedious workload, as there are often a thousand spindles in an all-night recording. In this paper a novel approach for the automatic detection of sleep spindles based upon the Teager Energy Operator and wavelet packets has been presented. The Teager operator was found to accurately enhance periodic activity in epochs of the EEG containing spindles. The wavelet packet transform proved effective in accurately locating spindles in the time-frequency domain. The autocorrelation function of the resultant Teager signal and the wavelet packet energy ratio were used to identify epochs with spindles. These two features were integrated into a spindle detection algorithm which achieved an accuracy of 93.7%.