Objective: 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, disadvantages can be posed in replacing the clinicians with the automatic system  . Thus, we aim to develop a framework , capable of assisting the clinicians and lessening the workload. Methods: We proposed the transfer learning framework  entitled MetaSleepLearner, using a Model Agnostic Meta-Learning (MAML), in order to transfer the acquired sleep staging knowledge from a large dataset to new individual subject. The capability of MAML was elicited for this task by allowing clinicians to label for a few samples and let the rest be handled by the system. Layer-wise Relevance Propagation (LRP) was also applied to understand the learning course of our approach. 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.\\%DIF > 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.
 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\\%DIF > 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.
D
Objective:
R
disadvantages
the difficulties
A
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
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develop a framework
explore the feasibility of using a novel approach
R
Methods: We proposed
We propose
A
,
R
using a
based on
R
subject. The capability of MAML was elicited for this task by allowing clinicians to label for a few samples and let the rest
subjects. The framework was demonstrated to require the labelling of only a few sleep epochs by the clinicians and allow the remainder to
D
Results:
R
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.
5.4