  Identifying bio-signals based-sleep stages requires time-consuming and
tedious labor of skilled clinicians. Deep learning approaches have been
introduced in order to challenge the automatic sleep stage classification
conundrum. However, the difficulties can be posed in replacing the clinicians
with the automatic system due to the differences in many aspects found in
individual bio-signals, causing the inconsistency in the performance of the
model on every incoming individual. Thus, we aim to explore the feasibility of
using a novel approach, capable of assisting the clinicians and lessening the
workload. We propose the transfer learning framework, entitled
MetaSleepLearner, based on Model Agnostic Meta-Learning (MAML), in order to
transfer the acquired sleep staging knowledge from a large dataset to new
individual subjects. The framework was demonstrated to require the labelling of
only a few sleep epochs by the clinicians and allow the remainder to be handled
by the system. Layer-wise Relevance Propagation (LRP) was also applied to
understand the learning course of our approach. In all acquired datasets, in
comparison to the conventional approach, MetaSleepLearner achieved a range of
5.4\% to 17.7\% improvement with statistical difference in the mean of both
approaches. The illustration of the model interpretation after the adaptation
to each subject also confirmed that the performance was directed towards
reasonable learning. MetaSleepLearner outperformed the conventional approaches
as a result from the fine-tuning using the recordings of both healthy subjects
and patients. This is the first work that investigated a non-conventional
pre-training method, MAML, resulting in a possibility for human-machine
collaboration in sleep stage classification and easing the burden of the
clinicians in labelling the sleep stages through only several epochs rather
than an entire recording.
