  Human sleep is cyclical with a period of approximately 90 minutes, implying
long temporal dependency in the sleep data. Yet, exploring this long-term
dependency when developing sleep staging models has remained untouched. In this
work, we show that while encoding the logic of a whole sleep cycle is crucial
to improve sleep staging performance, the sequential modelling approach in
existing state-of-the-art deep learning models are inefficient for that
purpose. We thus introduce a method for efficient long sequence modelling and
propose a new deep learning model, L-SeqSleepNet, which takes into account
whole-cycle sleep information for sleep staging. Evaluating L-SeqSleepNet on
four distinct databases of various sizes, we demonstrate state-of-the-art
performance obtained by the model over three different EEG setups, including
scalp EEG in conventional Polysomnography (PSG), in-ear EEG, and around-the-ear
EEG (cEEGrid), even with a single EEG channel input. Our analyses also show
that L-SeqSleepNet is able to alleviate the predominance of N2 sleep (the major
class in terms of classification) to bring down errors in other sleep stages.
Moreover the network becomes much more robust, meaning that for all subjects
where the baseline method had exceptionally poor performance, their performance
are improved significantly. Finally, the computation time only grows at a
sub-linear rate when the sequence length increases.
