  The monitoring of sleep patterns without patient's inconvenience or
involvement of a medical specialist is a clinical question of significant
importance. To this end, we propose an automatic sleep stage monitoring system
based on an affordable, unobtrusive, discreet, and long-term wearable in-ear
sensor for recording the Electroencephalogram (ear-EEG). The selected features
for sleep pattern classification from a single ear-EEG channel include the
spectral edge frequency (SEF) and multi- scale fuzzy entropy (MSFE), a
structural complexity feature. In this preliminary study, the manually scored
hypnograms from simultaneous scalp-EEG and ear-EEG recordings of four subjects
are used as labels for two analysis scenarios: 1) classification of ear-EEG
hypnogram labels from ear-EEG recordings and 2) prediction of scalp-EEG
hypnogram labels from ear-EEG recordings. We consider both 2-class and 4-class
sleep scoring, with the achieved accuracies ranging from 78.5 % to 95.2 % for
ear-EEG labels predicted from ear-EEG, and 76.8 % to 91.8 % for scalp-EEG
labels predicted from ear-EEG. The corresponding kappa coefficients, which
range from 0.64 to 0.83 for Scenario 1 and from 0.65 to 0.80 for Scenario 2,
indicate a Substantial to Almost Perfect agreement, thus proving the
feasibility of in-ear sensing for sleep monitoring in the community.
