Easy-to-classify wake is just what it sounds like: wake that no classifier would ever mess up because it's so obvious. Think lots of visible movement, over a sustained period of time.
A sleep classifier can look artificially good on a dataset if that dataset has a lot of easy-to-classify wake. The score is high, but not because the model is better. The data is just easier.
Below is one synthetic night, scored by a movement-is-wake classifier. Drag the slider to prepend easy-to-classify (ETC) wake (a block of obvious, sustained movement) to the front of the record. The classifier never changes; only the amount of trivial wake does.
A shared benchmark lets you separate two things: how much of a score is your classifier being good, and how much is just a property of your test set.
Before crediting the architecture, see what a trivial baseline scores on the same data. Your complex algorithm might not be doing that much better.
Interactive built on the construction in etc_demo.py: an 8-hour synthetic night with a block of obvious wake prepended, scored by AUROC. Numbers are illustrative.