Before revision:  Results: In all acquired datasets, in comparison to the conventional approach, MetaSleepLearner achieved a range of 6.15 \% to 12.12 \% 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. Conclusion: MetaSleepLearner outperformed the conventional approach as a result from the fine-tuning using the recordings of both healthy subjects and patients. Significance: This is the first paper that investigated a non-conventional pre-training method, MAML, in this task, resulting in a framework for human-machine collaboration in sleep stage classification, easing the burden of the clinicians in labelling the sleep stages through only several epochs rather than an entire recording.
After revision:  Results: In all acquired datasets, in comparison to the conventional approach, MetaSleepLearner achieved a range of 5.4