Sleep is a key requirement for an individual's health, though currently the options to study sleep rely largely on manual visual classification methods. In this paper we propose a new scheme for automated offline classification based upon cross-frequency-coupling (CFC) and compare it to the traditional band power estimation and the more recent preferential frequency band information estimation. All three approaches allowed sleep stage classification and provided whole-night visualization of sleep stages. Surprisingly, the simple average power in band classification achieved better overall performance than either the preferential frequency band information estimation or the CFC approach. However, combined classification with both average power and CFC features showed improved classification over either approach used singly.