---
title: 'Stanford Sleep Bench: SSRL for PSG Data'
url: https://www.emergentmind.com/topics/stanford-sleep-bench
type: topic
---

# Stanford Sleep Bench: SSRL for PSG Data

Stanford Sleep Bench is a large-scale standardized resource and systematic evaluation platform for developing and benchmarking self-supervised representation learning (SSRL) methods on polysomnography (PSG) data. Polysomnography, the gold standard in sleep analysis, is inherently multimodal, generating high-volume time-series clinical data well-suited for foundation model pre-training. Stanford Sleep Bench addresses two principal barriers in the field: the lack of an openly shared, task-diverse dataset and the absence of a comprehensive evaluation of SSRL approaches across canonical and clinically significant sleep-related downstream tasks. It comprises over 17,000 full-night PSG recordings and includes a suite of benchmark tasks—ranging from sleep staging and apnea diagnosis to age estimation and disease/mortality prediction—enabling systematic comparison of pretraining paradigms and accelerating reproducibility. The platform is accompanied by open-source pretrained weights, pipelines, benchmarks, and evaluation code [2512.09591].

## 1. Dataset Composition and Preprocessing

The Stanford Sleep Bench dataset consists of 17,467 full-night PSG recordings from 12,794 unique subjects, aggregating approximately 163,650 hours of multimodal physiological signals. The randomized subject-level split comprises 12,952 recordings for pre-training/training (≈121,365 hours), 1,500 for validation (≈13,979 hours), and 3,015 for testing (≈28,306 hours).

Patient demographics are broad, with an overall mean age of 43.3 ± 19.9 years and BMI of 27.9 ± 7.6 kg/m². Sex distribution is 60% male, 40% female. The sample is predominantly White (≈60%), also including Asian, Black, Hispanic/Latino, and other minorities.

The PSG data captures 16 channels across four modalities:
- **Brain activity (BAS)**: 8 channels (C3-M2, C4-M1, O1-M2, O2-M1, FP1-M2, FP2-M1, E1-M2, E2-M1)
- **Respiratory (RESP)**: 5 channels (thoracic effort, abdominal effort, nasal airflow, oral airflow, oxygen saturation—SpO₂)
- **Cardiac (EKG)**: 1 bipolar lead
- **Muscle (EMG)**: 2 channels (chin and leg EMG)

Signal preprocessing includes uniform resampling to 128 Hz using zero-phase Butterworth low-pass filtering at the Nyquist frequency.

## 2. Downstream Benchmark Tasks

Stanford Sleep Bench implements a diverse evaluation suite encompassing both canonical and complex clinical prediction tasks:

1. **Sleep-stage classification**: Five-class epoch-level annotation (Wake, N1, N2, N3, REM) over 5-second epochs.
2. **Sleep apnea diagnosis**: Binary (apnea/no apnea; AHI ≥ 15 events/hour) at the subject level.
3. **Age estimation**: Regression on chronological age.
4. **Mortality prediction**: Subject-level, all-cause, time-to-event modeling.
5. **Clinical disease prediction**: Time-to-event windowed prediction for twelve major diseases using Cox proportional hazards modeling: myocardial infarction, heart failure, atrial fibrillation/flutter, general atherosclerosis, angina, hypertension, hypotension, pulmonary heart disease, ischemic heart disease, chronic kidney disease, type 2 diabetes, and dementia.

## 3. SSRL Methods and Pre-training Paradigm

All SSRL approaches utilize a shared "patch + transformer" backbone: 5-second modality-specific patches are embedded by lightweight CNNs into 128-dimensional feature vectors, input to a temporal transformer; final modality-level embeddings are concatenated to 512-dimensional representations.

### Baseline Representations
- **Time-domain baseline**: A single channel per modality is downsampled (25.6 Hz), concatenated to 512 dimensions.
- **Frequency-domain baseline**: Discrete Fourier transform (DFT, excluding Nyquist), with within-modality channel averaging and extraction of 64 uniform amplitude+phase pairs, concatenated to form 512-dimensional vectors.

### Generative Pre-training
- **Masked Autoencoder (MAE)**: Implements both time and frequency-domain reconstruction losses.
  - *Time*: Reconstruct all or only masked time-domain patches.
  - *Freq*: Reconstruct all or only masked frequency-domain patches; loss combines amplitude and phase MSE after log transformation:
    - Amplitude: \( \mathcal{L}_{\mathrm{amp}} = \frac{1}{|\mathcal{K}|} \sum_{k\in\mathcal{K}} (A_X(k) - A_{\hat{X}}(k))^2 \)
    - Phase: \( \mathcal{L}_{\mathrm{phase}} = \frac{1}{|\mathcal{K}|} \sum_{k\in\mathcal{K}} \min_{\delta\in\{0, \pm 2\pi\}} (\angle X_k - (\angle \hat{X}_k + \delta))^2 \)
- **Denoising Autoencoder (DAE)**: Inputs are corrupted by Gaussian noise (per-channel, selected uniformly within [0.01, 0.3] × max amplitude; 50% channels per segment), forcing reconstruction in time or frequency domain using the MAE decoder/loss setup.

### Contrastive Learning
- **CL-Pairwise**: Pulls together 300-second mean-pooled embeddings of time-aligned modalities (i, j) within a sample, pushes apart other batch samples; loss:
  \[
  \mathcal{L}^{\mathrm{pair}}_{i, j, k} = -\log \frac{\exp(\mathrm{sim}(x_k^i, x_k^j)/\tau)}{\sum_{m=1}^N \exp(\mathrm{sim}(x_k^i, x_m^j)/\tau)}
  \]
- **CL-LOO (leave-one-out)**: Each modality is aligned to the mean embedding of all other modalities, with analogous loss calculation.

All models are pre-trained with Adam optimizer (learning rate 1e-3, batch size 256), using early stopping on a 100-recording validation set. On two A100 GPUs, pre-training typically completes in 15–35 hours, with downstream convergence observed within four epochs.

## 4. Fine-tuning and Evaluation Procedures

Downstream task architectures are tailored:
- **Patch-level (Sleep staging)**: The SSRL backbone is frozen. A two-layer transformer (4 heads) and a two-layer bidirectional LSTM are added, ending with a softmax classifier.
- **Full-night tasks (Apnea, age, disease/mortality)**: The backbone is frozen. A single transformer layer (8 heads) is applied over hourly embeddings, then attention pooling, followed by regression or classification heads.

Losses and key metrics:
- **Sleep staging, apnea diagnosis**: Cross-entropy loss; AUROC reporting.
- **Age estimation**: Softplus output, MSE (age scaled 0–1), reporting MAE (years).
- **Disease/mortality**: Cox proportional hazards loss, reporting Harrell’s C-index.

### Summary of Quantitative Results

| Task                        | Best Method(s) (Metric)     | Top Absolute Results           |
|-----------------------------|-----------------------------|-------------------------------|
| Sleep staging (AUROC)       | CL-LOO                      | 0.823                         |
| Apnea diagnosis (AUROC)     | MAE(Freq, all)              | 0.830                         |
| Age estimation (MAE)        | CL-LOO                      | 6.20 years                    |
| Disease + mortality (C-index)| CL-LOO/CL-Pairwise          | 0.74 (avg.)                   |

For sleep staging and apnea diagnosis, frequency-domain and contrastive methods are comparably strong (AUROC ≈ 0.80–0.83). For age and disease/mortality, CL approaches (CL-LOO/CL-Pairwise) outperform generative objectives by 4–7% (C-index gain) and exhibit faster convergence during pre-training. Frequency-domain baselines substantially outperform time-domain in apnea (AUROC 0.81 vs. 0.56).

In few-shot conditions, contrastive approaches require less data to attain near-maximal performance: CL-LOO and CL-Pairwise reach ≈95% maximum sleep staging AUROC with only 64 training subjects, whereas generative SSRL needs more data.

## 5. Comparative Insights and Interpretation

For tasks requiring short-term local physiological patterns (e.g., sleep staging, apnea), both frequency-domain generative (MAE/DAE) and contrastive learning approaches yield strong, similar performance. For complex integrative tasks (age, mortality, disease prediction), contrastive methods leveraging cross-modal relationships and temporal attention pooling outperform. This suggests that contrastive objectives, being explicitly discriminative and able to enforce multimodal/timewise coherence, better capture global, long-range physiological dependencies essential for clinically relevant downstream tasks, compared to generative losses that prioritize short-term reconstruction fidelity.

## 6. Limitations and Prospective Directions

Key limitations identified include the non-exhaustive exploration of architecture and hyperparameter search spaces, and the restriction to a subset of SSRL paradigms—hybrid or channel-specific objectives are plausible avenues for future study. The generalizability of results beyond the primary cohort (e.g., to diverse clinical populations outside SHHS) also requires further study. *A plausible implication is that cross-site validation and adaptation of foundation models will be necessary for translational robustness.*

## 7. Reproducibility and Open Science Resources

All data (PSG recordings), pretrained SSRL model weights, standardized splits, and code for pretraining, fine-tuning, and evaluation are provided for reproducibility:

- **Data portal**: https://doi.org/10.60508/0ta5-v163
- **Codebase and pretrained weights**: https://github.com/RuudeResearch/SleepBench

This open-source resource enables rigorous benchmarking, ablation studies, and catalyzes development of multimodal foundation models for broad sleep science applications, supporting standardized progress for tasks including sleep staging, apnea detection, age-related biomarker discovery, and disease risk prediction [2512.09591].

Source: https://www.emergentmind.com/topics/stanford-sleep-bench