In this work we describe a new deep learning approach for automatic sleep staging, and carry out its validation by addressing its generalization capabilities on a wide range of sleep staging databases. Prediction capabilities are evaluated in the context of independent local and external generalization scenarios. Effectively, by comparing both procedures it is possible to better extrapolate the expected performance of the method on the general reference task of sleep staging, regardless of data from a specific database. In addition, we examine the suitability of a novel approach based on the use of an ensemble of individual local models and evaluate its impact on the resulting inter-database generalization performance. Validation results show good general performance, as compared to the expected levels of human expert agreement, as well as state-of-the-art automatic sleep staging approaches

中文翻译：

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用于自动睡眠评分的深度学习方法的数据库间验证

在这项工作中，我们描述了一种用于自动睡眠分期的新深度学习方法，并通过解决其在各种睡眠分期数据库上的泛化能力来进行验证。在独立的本地和外部泛化场景的上下文中评估预测能力。有效地，通过比较这两个过程，可以更好地推断该方法在睡眠分期的一般参考任务上的预期性能，而不管来自特定数据库的数据如何。此外，我们检查了基于使用单个局部模型集合的新方法的适用性，并评估其对产生的数据库间泛化性能的影响。与人类专家一致的预期水平相比，验证结果显示出良好的总体性能，