Interpretation and Further Development of the Hypnodensity
Representation of Sleep Structure
Abstract
Objective: The recently-introduced hypnodensity graph provides
a probability distribution over sleep stages per data window (i.e. an
epoch). This work explored whether this representation reveals
continuities that can only be attributed to intra-and inter-rater
disagreement of expert scorings, or also to co-occurrence of sleep
stage-dependent features within one epoch.
Approach: We proposed a simplified model for time series like
the ones measured during sleep, and a second model to describe the
annotation process by an expert. Generating data according to these
models, enabled controlled experiments to investigate the interpretation
of the hypnodensity graph. Moreover, the influence of both the
supervised training strategy, and the used softmax non-linearity were
investigated. Polysomnography recordings of 96 health sleepers (of which
11 were used as independent test set), were subsequently used to
transfer conclusions to real data.
Main results: A hypnodensity graph, predicted by a
supervised neural classifier, represents the probability with which the
sleep expert(s) assigned a label to an epoch. It thus reflects annotator
behavior, and is thereby only indirectly linked to the ratio of sleep
stage-dependent features in the epoch. Unsupervised training was shown
to result in hypnodensity graph that were slightly less dependent on
this annotation process, resulting in, on average, higher-entropy
distributions over sleep stages (Hunsupervised = 0.41 vs Hsupervised =
0.29). Moreover, pre-softmax predictions were, for both training
strategies, found to better reflect the ratio of sleep stage-dependent
characteristics in an epoch, as compared to the postsoftmax counterparts
(i.e. the hypnodensity graph). In real data, this was observed from the
linear relation between pre-softmax N3 predictions and the amount of
delta power.
Significance: This study provides insights in, and proposes
new, representations of sleep that may enhance our comprehension about
sleep and sleep disorders.