Automatic two-channel sleep staging using a predictor-corrector method.

OBJECTIVE: We developed and implemented two predictor-corrector methods for the classification of two-channel EEG data into sleep stages.

APPROACH: The sequence of sleep stages over the night is modeled by a Markov chain of first and second order, resulting in an informative prior distribution for the new state, given the distribution of the current one. The correction step is realized by applying a Bayes classifier using the (preprocessed) data and this prior. The preprocessing step consists of a frequency analysis, a log transformation and a dimensionality reduction via principal component analysis.

MAIN RESULTS: The software automatically generates sleep profiles in which it detects wakeful phases as well as the different sleep stages with error rates of 16.5%-31.9% (n  =  8, healthy subjects, mean age  ±  SD: 39  ±  8.1 years, five females), where we compared our results to those of a certified polysomnographic technologist, who used a full polysomnograph and rated according to the American Academy of Sleep Medicine (AASM) criteria.

SIGNIFICANCE: The method presented relies on considerably less information than visual scoring and is done automatically. Furthermore, the error is comparable to visual scoring, where the inter-rater variability lies around 82%. Therefore, it has the potential to lessen the overheads associated with sleep diagnostics.