Seer’s Dr. Dean Freestone evaluated a supervised machine learning algorithm, Somnivore, for automated wake–sleep stage classification.

Read the full paper here.

Manual scoring of polysomnography data is labor-intensive and time-consuming, and most existing software does not account for subjective differences and user variability. Therefore, we evaluated a supervised machine learning algorithm, SomnivoreTM, for automated wake–sleep stage classification.