BACKGROUND

Sleep scoring is an essential but time-consuming process, and therefore automatic sleep scoring is crucial and urgent to help address the growing unmet needs for sleep research. This paper aims to develop a versatile deep-learning architecture to automate sleep scoring using raw polysomnography recordings.

METHOD

The model adopts a linear function to address different numbers of inputs, thereby extending model applications. Two-dimensional convolution neural networks are used to learn features from multi-modality polysomnographic signals, a “squeeze and excitation” block to recalibrate channel-wise features, together with a long short-term memory module to exploit long-range contextual relation. The learnt features are finally fed to the decision layer to generate predictions for sleep stages.

RESULT

Model performance is evaluated on three public datasets. For all tasks with different available channels, our model achieves outstanding performance not only on healthy subjects but even on patients with sleep disorders (SHHS: Acc-0.87, K-0.81; ISRUC: Acc-0.86, K-0.82; Sleep-EDF: Acc-0.86, K-0.81). The highest classification accuracy is achieved by a fusion of multiple polysomnographic signals.

COMPARISON

Compared to state-of-the-art methods that use the same dataset, the proposed model achieves a comparable or better performance, and exhibits low computational cost.

CONCLUSIONS

The model demonstrates its transferability among different datasets, without changing model architecture or hyper-parameters across tasks. Good model transferability promotes the application of transfer learning on small group studies with mismatched channels. Due to demonstrated availability and versatility, the proposed method can be integrated with diverse polysomnography systems, thereby facilitating sleep monitoring in clinical or routine care.

中文翻译：

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自动睡眠评分：用于多模式时间序列的深度学习架构

背景

睡眠计分是必不可少的但很耗时的过程，因此自动睡眠计分对于解​​决日益增长的未满足的睡眠研究需求至关重要且迫切。本文旨在开发一种通用的深度学习体系结构，以使用原始的多导睡眠图记录自动进行睡眠评分。

方法

该模型采用线性函数来处理不同数量的输入，从而扩展了模型应用。二维卷积神经网络用于从多模态多导睡眠图信号中学习特征，使用“挤压和激发”模块来重新校准通道特征，并使用一个长短期记忆模块来开发远程上下文关系。最终将学习到的特征馈送到决策层，以生成睡眠阶段的预测。

结果

在三个公共数据集上评估模型性能。对于具有不同可用渠道的所有任务，我们的模型不仅在健康受试者上，甚至在患有睡眠障碍的患者上均表现出色（SHHS：Acc-0.87，K-0.81； ISRUC：Acc-0.86，K-0.82； Sleep-EDF： Acc-0.86，K-0.81）。通过多个多导睡眠图信号的融合可以实现最高的分类精度。

比较

与使用相同数据集的最新方法相比，所提出的模型达到了可比或更好的性能，并且具有较低的计算成本。

结论

该模型展示了其在不同数据集之间的可移植性，而无需更改模型架构或跨任务的超参数。良好的模型可转移性促进了转移学习在渠道不匹配的小组研究中的应用。由于证明了可用性和多功能性，因此所提出的方法可以与多种多导睡眠图系统集成在一起，从而有助于临床或常规护理中的睡眠监测。