Automatic sleep staging based on 24/7 EEG SubQ (UNEEG medical) data displays strong agreement with polysomnography in healthy adults

Abstract

Goal and aims

Performance evaluation of automatic sleep staging on two-channel subcutaneous electroencephalography.

Focus technology

UNEEG medical’s 24/7 electroencephalography SubQ (the SubQ device) with deep learning model U-SleepSQ.

Reference method/technology

Manually scored hypnograms from polysomnographic recordings.

Sample

Twenty-two healthy adults with 1-6 recordings per participant. The clinical study was registered at ClinicalTrials.gov with the identifier NCT04513743.

Design

Fine-tuning of U-Sleep in 11-fold cross-participant validation on 22 healthy adults. The resultant model was called U-SleepSQ.

Core analytics

Bland-Altman analysis of sleep parameters. Advanced multiclass model performance metrics: stage-specific accuracy, specificity, sensitivity, kappa (κ), and F1 score. Additionally, Cohen’s κ coefficient and macro F1 score. Longitudinal and participant-level performance evaluation.

Additional analytics and exploratory analyses

Exploration of model confidence quantification. Performance vs. age, sex, body mass index, SubQ implantation hemisphere, normalized entropy, transition index, and scores from the following three questionnaires: Morningness-Eveningness Questionnaire, World Health Organization’s 5-item Well-being Index, and Major Depression Inventory.

Core outcomes

There was a strong agreement between the focus and reference method/technology.

Important supplemental outcomes

The confidence score was a promising metric for estimating the reliability of each hypnogram classified by the system.

Core conclusion

The U-SleepSQ model classified hypnograms for healthy participants soon after implantation and longitudinally with a strong agreement with the gold standard of manually scored polysomnographics, exhibiting negligible temporal variation.