This paper presents a model for the estimation of a priori probabilities of sleep epoch classes based on the epoch location in a sleep cycle. These probabilities are used as additional features for sleep stage classification based on the analysis of respiratory effort. The model was validated with data of 685 subjects selected from the Sleep Heart Health Study dataset. The model improves a base algorithm by 8 percent points and demonstrates Cohen's kappa of 0.56 ± 0.12. Our results will contribute to the development of screening tools for unobtrusive sleep structure estimation.