  Sleep spindles are neurophysiological phenomena that appear to be linked to
memory formation and other functions of the central nervous system, and that
can be observed in electroencephalographic recordings (EEG) during sleep.
Manually identified spindle annotations in EEG recordings suffer from
substantial intra- and inter-rater variability, even if raters have been highly
trained, which reduces the reliability of spindle measures as a research and
diagnostic tool. The Massive Online Data Annotation (MODA) project has recently
addressed this problem by forming a consensus from multiple such rating
experts, thus providing a corpus of spindle annotations of enhanced quality.
Based on this dataset, we present a U-Net-type deep neural network model to
automatically detect sleep spindles. Our model's performance exceeds that of
the state-of-the-art detector and of most experts in the MODA dataset. We
observed improved detection accuracy in subjects of all ages, including older
individuals whose spindles are particularly challenging to detect reliably. Our
results underline the potential of automated methods to do repetitive
cumbersome tasks with super-human performance.
