IEEE J Biomed Health Inform. 2021 Jun;25(6):1949-1963. doi: 10.1109/JBHI.2020.3037693. Epub 2021 Jun 3.
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 (source code is available at https://github.com/IoBT-VISTEC/MetaSleepLearner). 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.
基于生物信号的睡眠分期需要经过专业临床医生进行耗时且繁琐的工作。为了解决自动睡眠分期分类难题，引入了深度学习方法。然而，由于个体生物信号在许多方面存在差异，导致自动系统难以替代临床医生，从而影响模型在每个个体上的性能表现不一致。因此，我们旨在探索一种新的方法，以帮助临床医生减轻工作量。我们提出了一种基于模型不可知元学习（MAML）的迁移学习框架，名为 MetaSleepLearner，用于将从大型数据集中学到的睡眠分期知识转移到新的个体受试者中（源代码可在 https://github.com/IoBT-VISTEC/MetaSleepLearner 上获得）。该框架仅需临床医生对少数睡眠时段进行标注，其余部分由系统完成。还应用了层间相关性传播（LRP）来了解我们方法的学习过程。在所有获取的数据集上，与传统方法相比，MetaSleepLearner 在两种方法的平均值上均有 5.4%到 17.7%的改进，且具有统计学差异。对每个受试者进行适应性调整后的模型解释说明也证实了性能是朝着合理学习的方向发展的。由于对健康受试者和患者的记录进行了微调，MetaSleepLearner 优于传统方法。这是首次研究了一种非传统的预训练方法 MAML，为人机合作在睡眠分期分类中提供了可能性，并通过仅标注几个时段而不是整个记录来减轻临床医生的负担。
