TLDR: The MEASURE framework addresses the challenge of domain generalization in automatic sleep staging. It proposes a novel approach that reduces “excess domain-relevant information”—subject-specific biases in physiological signals—while preserving essential multi-scale temporal and spectral features. By integrating minimal sufficient representation learning with multi-scale feature alignment, MEASURE learns robust, domain-invariant features. This method consistently outperforms state-of-the-art techniques on public sleep staging datasets, leading to more accurate and generalizable sleep stage predictions for diverse individuals without requiring additional adversarial training modules.
Automatic sleep staging, the process of identifying and tracking different sleep stages, is vital for understanding sleep quality and diagnosing sleep disorders. While deep learning models have significantly advanced in this area, they often struggle to perform consistently across different individuals or unseen subjects. This challenge, known as the domain generalization problem, arises because physiological signals like electroencephalography (EEG) can vary greatly from person to person due to factors like age, gender, or health conditions.
Existing deep learning methods, including those using contrastive learning, attempt to learn features that are consistent across different data sources. However, many of these approaches don’t fully address what researchers call “excess domain-relevant information.” This refers to specific characteristics within the data that are unique to a particular individual or data collection setting, rather than being universally relevant to sleep stages. This “superfluous information” can hinder a model’s ability to generalize effectively to new, unseen subjects.
To tackle this, a novel framework called MEASURE (Multi-scalE minimAl SUfficient Representation lEarning) has been introduced. The core idea behind MEASURE is to systematically reduce this excess domain-relevant information while carefully preserving the essential temporal and spectral features crucial for accurate sleep stage classification. It achieves this through a two-pronged approach: minimal sufficient representation learning and multi-scale learning.
Minimal sufficient representation learning focuses on extracting only the most relevant information for the task, discarding any superfluous details that might be specific to a particular domain. By minimizing the mutual information between the learned features and domain-specific characteristics, MEASURE ensures that the model learns features that are truly invariant to individual differences.
However, simply minimizing information can sometimes lead to models that overfit to high-level features, losing the rich, diverse information present at different scales of the physiological signals. Sleep stages are characterized by distinct frequency patterns; for example, deep sleep (N3) involves low frequencies, while wakefulness shows higher frequencies. To capture this diversity, MEASURE integrates multi-scale learning. This means the model processes and aligns features extracted from various layers of its internal architecture, ensuring that both fine-grained and broad temporal and spectral characteristics are preserved.
The MEASURE framework operates in two main stages. First, during a pre-training phase, the model learns to extract domain-invariant features using a specially designed objective function that combines contrastive learning with the minimization of domain-relevant information across multiple feature scales. In the second stage, the pre-trained feature extractor is frozen, and its multi-scale outputs are fed into a transformer-based classifier to make the final sleep stage predictions.
Extensive experiments on widely used sleep staging datasets, SleepEDF-20 and MASS, have demonstrated the superior performance of MEASURE. It consistently outperformed state-of-the-art methods, including other domain generalization techniques, across key metrics such as accuracy, F1 score, and Cohen’s Kappa. Visual analyses, like hypnogram comparisons and t-SNE visualizations, further confirmed that MEASURE produces predictions that align more closely with ground truth and generates more aligned feature distributions between different data domains.
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This research offers a significant step forward in making automatic sleep staging models more robust and reliable for diverse patient populations. By explicitly addressing and mitigating domain-specific biases while retaining crucial multi-scale information, MEASURE paves the way for more generalized and accurate sleep disorder diagnosis. For more technical details, the full research paper can be accessed here.
