Inter-database validation of a deep learning approach for automatic sleep scoring

TL;DR

This paper introduces a new deep learning method for automatic sleep staging, validated across multiple databases to assess its generalization ability and compare it with human experts and existing approaches.

Contribution

The study presents a novel ensemble-based deep learning approach and evaluates its inter-database generalization for sleep scoring, enhancing robustness across diverse datasets.

Findings

Good generalization performance comparable to human experts

Ensemble approach improves inter-database robustness

Outperforms some state-of-the-art methods

Abstract

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