论文标题
对自动睡眠评分的深度学习方法的数据库验证
Inter-database validation of a deep learning approach for automatic sleep scoring
论文作者
论文摘要
在这项工作中，我们描述了一种新的深度学习方法，用于自动睡眠阶段，并通过在广泛的睡眠登台数据库上解决其概括功能来进行验证。在独立的本地和外部概括方案的背景下评估预测能力。有效地，通过比较这两个过程，无论来自特定数据库的数据如何，都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。此外，我们根据使用单个局部模型的合奏来研究一种新方法的适用性，并评估其对所得数据库间概括性能的影响。与人类专家协议的预期水平以及最先进的自动睡眠分期临时方法相比，验证结果表现出良好的一般性能
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
