SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification. (arXiv:2105.11043v3 [cs.LG] UPDATED)

Background: Black-box skepticism is one of the main hindrances impeding
deep-learning-based automatic sleep scoring from being used in clinical
environments. Methods: Towards interpretability, this work proposes a
sequence-to-sequence sleep-staging model, namely SleepTransformer. It is based
on the transformer backbone and offers interpretability of the model's
decisions at both the epoch and sequence level. We further propose a simple yet
efficient method to quantify uncertainty in the model's decisions. The method,
which is based on entropy, can serve as a metric …

arxiv interpretability uncertainty