Quasi-supervised scoring of human sleep in polysomnograms using augmented input variables
Three common problems associated with clinical sleep scoring are identified. A quasi-supervised classification framework is proposed to address these problems. The proposed framework compensates for rater uncertainty with reasonable accuracy.
quasi-supervised automatic sleep scoring supervised unsupervised eeg psg hidden markov model gaussian mixture