Summary
Objectives: Scoring sleep visually based on polysomnography is an important but time-consuming
element of sleep medicine. Whereas computer software assists human experts in the
assignment of sleep stages to polysomnogram epochs, their performance is usually insufficient.
This study evaluates the possibility to fully automatize sleep staging considering
the reliability of the sleep stages available from human expert sleep scorers.
Methods: We obtain features from EEG, ECG and respiratory signals of polysomnograms from ten
healthy subjects. Using the sleep stages provided by three human experts, we evaluate
the performance of linear discriminant analysis on the entire polysomnogram and only
on epochs where the three experts agree in their sleep stage scoring.
Results: We show that in polysomnogram intervals, to which all three scorers assign the same
sleep stage, our algorithm achieves 90% accuracy. This high rate of agreement with
the human experts is accomplished with only a small set of three frequency features
from the EEG. We increase the performance to 93% by including ECG and respiration
features. In contrast, on intervals of ambiguous sleep stage, the sleep stage classification
obtained from our algorithm, agrees with the human consensus scorer in approximately
61%.
Conclusions: These findings suggest that machine classification is highly consistent with human
sleep staging and that error in the algorithm’s assignments is rather a problem of
lack of well-defined criteria for human experts to judge certain polysomnogram epochs
than an insufficiency of computational procedures.
Keywords
Sleep staging - polysomnogram - linear discriminant analysis - automatic classification
