A portable and wearable yet low-power sleep monitoring system necessitates an automatic sleep scoring algorithm with the use of minimum number of recording channels. Computer-aided sleep staging is also important to eradicate the onus of sleep scorers of analyzing an enormous volume of data. The existing works on sleep scoring are either multichannel based or yield poor performance. Therefore, an automatic sleep scoring algorithm based on single channel EEG signals is yet to emerge. In this work, we utilize spectral features to extract discriminatory information from EEG signal segments. We then perform statistical analyses to find out the efficacy and the discriminatory capability of the selected features for various sleep states. Afterwards, we employ Adaptive Boosting to perform classification. The experimental outcomes perspicuously manifest that the proposed scheme is superior to state-of-the-art ones in accuracy.