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

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.
Keywords
Performance evaluationsMachine learningUltra long-term EEG monitoringSubcutaneous EEGHome monitoring