Inter-database validation of a deep learning approach for automatic sleep scoring
This work addresses the need for reliable and generalizable sleep staging tools in medical diagnostics, though it appears incremental as it builds on existing deep learning methods.
The authors tackled the problem of automatic sleep scoring by developing a deep learning approach and validating its generalization across multiple sleep staging databases, achieving performance comparable to human expert agreement and state-of-the-art methods.
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