Demographic-Aware Transfer Learning for Sleep Stage Classification in Clinical Polysomnography

• URL: http://arxiv.org/abs/2605.02245v1
• Date: Mon, 04 May 2026 05:38:20 GMT
• Title: Demographic-Aware Transfer Learning for Sleep Stage Classification in Clinical Polysomnography
• Abstract summary: Sleep patterns differ substantially across gender, age, and obstructive sleep apnea (OSA) severity.<n>We propose a two stage training strategy based on demographic stratification and transfer learning framework.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated sleep stage classification typically employs a single population-agnostic model, disregarding established demographic variations in sleep architecture. Sleep patterns, however, differ substantially across gender, age, and obstructive sleep apnea (OSA) severity, indicating that a onesize-fits all approach may be suboptimal for diverse clinical populations. In this paper, we propose a two stage training strategy based on demographic stratification and transfer learning framework. We first pretrains a convolutional recurrent model on the full population and then fine tunes it independently for demographic subgroups defined by gender, age, and Apnea-Hypopnea Index (AHI) severity according to the AASM clinical standard. Using the DREAMT dataset comprising 100 clinical subjects and 7 PSG channels, we evaluate 37 fine-tuned configurations across single-axis and two-way demographic combinations. Results demonstrate that 35 of the 37 fine-tuned models outperform the baseline, with Cohen's kappa improvements ranging from 0.9 to 12.9%. These findings indicate that stratified fine tuning tailored to specific patient demographics yields substantially more accurate sleep staging than a single generalized model, offering a practical and clinically grounded paradigm for personalized sleep assessment.

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