Sleep-disordered breathing (SDB) is a highly prevalent condition associated with many adverse health problems. As the current means of diagnosis (polysomnography) is obtrusive and ill-suited for mass screening of the population, we explore a minimal-contact, automatic approach that uses acoustics-based methods in conjunction with pulse oximetry. We present a two-stage method for automatically classifying breathing sounds produced during sleep to track respiratory effort and predicting disordered breathing events using respiratory effort durations and oxygen desaturations. We compare our method for tracking respiratory effort and predicting disordered breathing with human expert event scoring. Our subject-independent method tracks respiratory effort with 87% accuracy and predicts disordered breathing events with 40–52% accuracy.