  In human sleep staging models, augmenting the temporal context of the input
to the range of tens of minutes has recently demonstrated performance
improvement. In contrast, the temporal context of mouse sleep staging models is
typically in the order of tens of seconds. While long-term time patterns are
less clear in mouse sleep, increasing the temporal context further than that of
the current mouse sleep staging models might still result in a performance
increase, given that the current methods only model very short term patterns.
In this study, we examine the influence of increasing the temporal context in
mouse sleep staging up to 15 minutes in three mouse cohorts using two recent
and high-performing human sleep staging models that account for long-term
dependencies. These are compared to two prominent mouse sleep staging models
that use a local context of 12 s and 20 s, respectively. An increase in context
up to 28 s is observed to have a positive impact on sleep stage classification
performance, especially in REM sleep. However, the impact is limited for longer
context windows. One of the human sleep scoring models, L-SeqSleepNet,
outperforms both mouse models in all cohorts. This suggests that mouse sleep
staging can benefit from more temporal context than currently used.
