SOUP: Sleep Data Copilot for Accurate Hypnogram Labeling

Resumen

Sleep analysis is crucial for diagnosing disorders and understanding physiological patterns. However, accurately labeling hypnograms is challenging due to significant interrater variability and resource constraints that limit the use of multiple experts. This study introduces a novel verification tool that assesses biomedical signals, including heart rate and activity, alongside labeled hypnograms against state-of-the-art conditions. The tool was developed to evaluate the quality and reliability of hypnogram annotations, providing feedback on the credibility of labels generated by automated methods and single expert annotations. It cross-references labeled data against physiological signals and identifies discrepancies or anomalies that may indicate errors in the labeling process. For validation, the tool was applied to the MESA dataset, a well-known collection of sleep data. Application of the tool demonstrated its ability to provide objective feedback on hypnogram labels and to identify anomalies in patient data, potentially assisting clinicians in refining their assessments. By offering a user-friendly interface and flexible design, this verification tool enhances the accuracy of sleep stage annotations and serves as a valuable resource for both clinical and research applications.