Application of Transformers for Sleep Stage Classification

Abstract

[Abstract] Transformer architectures have revolutionized the field of artificial intelligence. This work investigates the application of Transformers to the sleep stage classification problem. Despite promising results in recent studies, the clinical application of these methods remains limited. In this paper, we propose a novel model that replaces the LSTM component of a state-of-the-art CNN+LSTM architecture with a Transformer encoder, comparing its performance against our baseline and several state-of-the-art models. The results showed faster convergence, reduced complexity, and enhanced performance. Leveraging the Transformer architecture, we propose and investigate an interpretability method based on attention mechanisms. Finally, we evaluate the inter-database generalization performance of our model.