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This paper benchmarks frozen representations from self-supervised polysomnography foundation models against sleep-staging encoders pretrained at similar scale and modality coverage. Its main finding is that the apparent advantage of foundation models over a narrow EEG-only baseline disappears once supervised baselines are matched for scale and data, and that transfer from sleep scorers is more data-efficient. For ML readers, it is a useful reminder that fair pretraining comparisons in clinical time-series work require matched baselines.
On Sleep-Wake Disorder Identification from Polysomnographic Representations
arXiv:2610.17451v1 Announce Type: new Abstract: Self-supervised polysomnography foundation models (PSG FMs) aim to capture clinically useful information beyond sleep staging, yet are rarely compared with sleep-staging encoders pretrained at comparable scale and modality coverage. Using a common whole-night, missingness-aware state-space downstream modeling framework, we compared frozen representations from prominent large-scale, multi-cohort sleep-stage-supervised encoders and self-supervised PSG FMs for multilabel identification of six sleep-wake and circadian disorder groups in 7,906 PSGs from an unseen registry. Although PSG FMs outperformed an EEG-only, single-cohort supervised baseline, this ordering reversed when supervised pretraining approached comparable scale and modality coverage. Transfer learning from neural sleep scorers achieved higher overall performance while exhibiting greater downstream data efficiency. These findings underscore the need for scale- and modality-matched pretrained supervised baselines when assessing PSG FMs' downstream potential.
Abstract:Self-supervised polysomnography foundation models (PSG FMs) aim to capture clinically useful information beyond sleep staging, yet are rarely compared with sleep-staging encoders pretrained at comparable scale and modality coverage. Using a common whole-night, missingness-aware state-space downstream modeling framework, we compared frozen representations from prominent large-scale, multi-cohort sleep-stage-supervised encoders and self-supervised PSG FMs for multilabel identification of six sleep-wake and circadian disorder groups in 7,906 PSGs from an unseen registry. Although PSG FMs outperformed an EEG-only, single-cohort supervised baseline, this ordering reversed when supervised pretraining approached comparable scale and modality coverage. Transfer learning from neural sleep scorers achieved higher overall performance while exhibiting greater downstream data efficiency. These findings underscore the need for scale- and modality-matched pretrained supervised baselines when assessing PSG FMs' downstream potential.
Machine Learning (cs.LG)
arXiv:2610.17451 [cs.LG]
(or arXiv:2610.17451v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.17451 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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