From sleep staging to spindle detection: a case study on end-to-end automated sleep analysis

Clinical Snapshot

Key Findings

• P Value: Significant differences reported (specific p-values not provided)

• Effect Size: Not specified in abstract

• Primary Outcome: Significant differences in fast spindle densities between bipolar patients and healthy controls

• Nnt Or Sensitivity: Models performed at or above inter-rater agreement levels for both sleep staging and spindle detection

• Confidence Interval: Not reported in abstract

Clinical Application

High feasibility - reduces analysis time from months to minutes while maintaining accuracy Highly relevant to Australian sleep medicine services facing workforce shortages and increasing demand for sleep studies. Could support PBS-funded sleep studies and align with RACGP sleep disorder management guidelines Sleep medicine patients requiring polysomnography analysis, particularly those with psychiatric conditions

Abstract

Automation of sleep analysis, including both macrostructural (sleep stages) and microstructural (e.g., sleep spindles) elements, promises to enable large-scale sleep studies and to reduce variance due to inter-rater incongruencies. While individual steps, such as sleep staging and spindle detection, have been studied separately, the feasibility of automating multi-step sleep analysis remains unclear. In this case study, we evaluate whether a fully automated analysis using validated machine learning models for sleep staging (RobustSleepNet) and subsequent spindle detection (SUMOv2) can replicate findings from an expert-based study of bipolar disorder. The automated analysis qualitatively reproduced key findings from the expert-based study, including significant differences in fast spindle densities between bipolar patients and healthy controls, accomplishing in minutes what previously took months to complete manually. While the results of the automated analysis differed quantitatively from the expert-based study, possibly due to biases between expert raters or between raters and the models, the models individually performed at or above inter-rater agreement for both sleep staging and spindle detection. Our results demonstrate that fully automated approaches have the potential to facilitate large-scale sleep research. We are providing public access to the tools used in our automated analysis by sharing our code and introducing SomnoBot, a privacy-preserving sleep analysis platform.

References

1. 1.Grieger, N., Mehrkanoon, S., Ritter, P., & Bialonski, S. (2026). From sleep staging to spindle detection: a case study on end-to-end automated sleep analysis. Scientific Reports. https://doi.org/10.1111/jsr.12169