Automated sleep stage scoring employing a reasoning mechanism and evaluation of its explainability.

ABSTRACT: Scoring sleep stages from biological signals is an essential but labor-intensive inspection for sleep diagnosis. The existing automated scoring methods have achieved high accuracy but are not widely applied in clinical practice. In our understanding, the existing methods have failed to establish the trust of sleep experts (e.g., physicians and clinical technologists) due to a lack of ability to explain the evidences/clues for scoring. In this study, we developed a deep-learning-based scoring model with a reasoning mechanism called class activation mapping (CAM) to solve this problem. This mechanism explicitly shows which portions of the signals support our model's sleep stage decision, and we verified that these portions overlap with the "characteristic waves," which are evidences/clues us

SUBMITTER: Horie K 

PROVIDER: S-EPMC9329306 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature