sleep2vec: Unified Cross-Modal Alignment for Heterogeneous Nocturnal Biosignals
Authors: Weixuan Yuan
Zengrui Jin
Yichen Wang
Donglin Xie
Ziyi Ye
Chao Zhang
Xuesong Chen
Venue: ICLR 2026 Poster (2026)
Keywords: Contrastive Learning
Physiological Signal
Sleep Medicine
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
TL;DR: sleep2vec is a large-scale physiological foundation model trained on heterogeneous polysomnography (PSG) data, offering robust sleep assessment and clinical predictions despite missing or variable sensor inputs.

Abstract: Tasks ranging from sleep staging to clinical diagnosis traditionally rely on standard polysomnography (PSG) devices, bedside monitors and wearable devices, which capture diverse nocturnal biosignals (e.g., EEG, EOG, ECG, SpO$_2$). However, heterogeneity across devices and frequent sensor dropout pose significant challenges for unified modelling of these multimodal signals. We present sleep2vec, a foundation model for diverse and incomplete nocturnal biosignals that learns a shared representation via cross-modal alignment. sleep2vec is contrastively pre-trained on 42,249 overnight recordings spanning nine modalities using a Demography, Age, Site & History-aware InfoNCE objective that incorporates physiological and acquisition metadata (e.g., age, gender, recording site) to dynamically weight negatives and mitigate cohort-specific shortcuts. On downstream sleep staging and clinical outcome assessment, sleep2vec consistently outperforms strong baselines and remains robust to any subset of available modalities and sensor dropout. We further characterize, to our knowledge for the first time, scaling laws for nocturnal biosignals with respect to modality diversity and model capacity. Together, these results show that unified cross-modal alignment, coupled with principled scaling, enables label-efficient, general-purpose modelling of real-world nocturnal biosignals.

Published as a conference paper at ICLR 2026
SLEEP2VEC: UNIFIED FOR HETEROGENEOUS NOCTURNAL BIOSIGNALS
ALIGNMENT
Weixuan Yuan 16. Zengrui 2 Yichen Wang Donglin Xie Ziyi Ye Chao Zhang 2, Xuesong Chen 1.3.t
Five Seasons Medical, TTinghua University, *Beijing Key Laboratory for Sleep Breathing Disorder, "Peking University, Fudan University, Technical University of Munich
{zrjin, (z277]@tsinnge edu. en; chenxuesong@mmjji wwwpoo/wwwwwwwiiie..oeooooem com
ABSTRACT
Tasks ranging from sleep staging to clinical diagnosis traditionally rely on stan- dard polysomnography (PSG) devices, bedside monitors and wearable devices, which capture diverse nocturnal biosignals (c.g., EEG, EOG, ECG, SpO2). How- ever, heterogeneity across devices and frequent sensor dropout pose signifi- cant challenges for unified modelling of these multimodal signals. We present s sleep2vec, a foundation model for diverse and incomplete nocturnal biosig- nals that learns a shared representation via cross-modal alignment. alee eeee2vec is contrastively pre-trained on 42,249 overnight recordings spanning nine modal- ities using a Demography, Age, Site & History-aware InfoNCE objective that in- corporates physiological and acquisition metadata (e.g.. age, gender, recording site) to dynamically weight negatives and mitigate cohort-specific shortcuts. On downstream sleep staging and clinical outcome assessment, s s1eep2ved consis- tently outperforms strong baselines and remains robust to any subset of available modalities and sensor dropout. We further characterize, to our knowledge for the first time, scaling laws for nocturnal biosignals with respect modality diversity and model capacity. Together, these results show that unified cross-modal align- ment, coupled with principled scaling, enables label-efficient, general- -urppse modelling of real-world nocturnal biosignals.
INTRODUCTION
Sleep is central determinant of human health, it shapes cognition, metabolism, cardiovascular func- tion, and mental well-being, and its disruption both signals and drives disease (Irwin) 2015) Mukher- jee et al. phy (PSG) (Bloch] 1997 Boulos et al. (211)), which is gold standard multi-sensor recording that muscle tone, cardiorespiratory dynamics, and oxygen saturation. Outside the clinical facilities, growing range of bedside monitors and wearable devices captures subsets of these PSG modalities, creating fragmented landscape across devices and care settings (Paalasmaa et al. 2012, Sadek et al., 2020 Birrer et al., 2024, Yu et al. 2025 al. 2025). This reality motivates the question:
2015 Leng al. 2019 Lim al.] 2023). Sleep is clinically assessed with polysomnogra-
jointly measures neural and ocular electrophysioler
Can cross-modal alignment of nocturnal biosignals enable unified physiological representation that generalizes robustly across heterogeneous sensor sets in sleep medicine?"
Physiological signal pre-training offers a promising paradigm by learning generalized representa- tions from diverse biosignals with minimal supervision (Thapa et al., 2024 2025 Pillai et al. 2025 Fox et al. 2025). Yet real-world data bring hard constraints, sensor montages vary across centers and devices, sampling rates differ, entire channels are often missing, and large-scale expert annotation remains costly, making such foundation both necessary and challenging.
We posit that concurrent nocturnal signals represent multiple perspectives of the same latent phys- iological state (Rechtschaffen & Kales 1968 Berry et al. 2012 2017). A proper alignment of
"Equal contribution was made between the first two authors.
Corresponning author.

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High Sampling Rate Channels
Low Sampling Rate Channels
EEG
Airflow
FOG
Resp-
inatory
5p0
Polysomnography (PSG) captures diverse physiological signals, illustrated here 30- second segments for each modality. High sampling rate clectrophysiological channels include EEG, EMG, EOG, and ECG, while lower sampling rate cardiopulmonary and oximetry channels encom- pass Nasal Airflow, Abdominal/Thoracic Belt (ABD/Thor Belt), and SpO2- Inter-Beat Interval (IBI) and Respiratory effort (RESP) are interval-derived features computed from ECG and respiratory channels (ABD/Thor Belts when available, otherwise Nasal Airflow), respectively. Together, these concurrent nocturnal signals provide complementary perspectives shared latent physiological state, highlighting the multimodal complexity inherent to sleep monitoring.
Figure 1:
these heterogeneous views into unified representation space enables downstream tasks flexibly operate on arbitrary modalities without retraining specialized pipelines. Such space must yield modality-agnostie representations robust enough ensure reliable inference even when modality missing occurs. This leads to a scaling hypothesis, suggesting that increasing modality diversity and model capacity can enrich semantic coverage and regularize modality-speciiff nuances. Although scaling laws have been extensively studied in language and vision, their implications remain largely unexplored in physiological signal contexts. We therefore propose and evaluate framework demon- strating predictable benefits of scaling PSG foundation models along both modality and parameter axes, especially for cross-center generalization where variations sensor montage, demographics, and acquisition protocols are prevalent.
Prior work only partially addresses these needs. Existing models are typically trained for a specific downstream task (Wang et al. 2024 Shen et al.] 2024 Carter et al.) 2024 Pan al. 2024 Shen
ctt al. 2024 Lee et al. 20255] Ma et al. 2025 Fox et al. 2025), lacking the generality required of foundation model capable of supporting multiple tasks. Contrastive pre-training has shown promise
on limited sets of physiological, typically one to three channels (e.g., EEG and ECG) (Wang et al.,
2024 Mathew al.) 2024 Thapa et al. 2024, Zhou al. 2025 Thapa al. 20255). but has not scaled to the full palette of PSG sensors. When more modalities are involved, objectives often pri-
oritize reconstruction (Narayanswamy al. 2024 Luo al. 2024; Mathew et al. 2024 Nie al. 2025) rather than explicit cross-modal alignment. Reconstruction encourages fidelity to modality- specific details but does not enforce that heterogeneous inputs map shared semantic manifold. As result, inference typically assumes access to the same modality set used in training, degrading under realistic sensor missing scenarios. Moreover, systematic analyses of how performance scales with modalities and parameters scarce.
We address these gaps with sleep2vec, a PSG foundation model that aligns heterogeneous noc- tumal signals into unified embedding space. Our framework jointly leverages nine modalities, waveform channels including EEG, EOG, EMG, ECG, Nasal airflow, Abdominal/Thoracic Belt (ABD/Thor Belt) and SpOz; and interval-derived features including Inter-beat Interval (IBI) and Respiratory effort (RESP), from 42,249 nights physiological recordings. A context-aware In- foNCE objective, explicitly modelling physiological similarity (age, gender, recording center) to
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dynamically weight samples, effeetively distinguishes hard from easy negatives, mitigating overfit- ting to -ptaset-specific nuances.
Our work makes the following contributions:
(i) Unified multimodal PSG pre-training: We propose, to our knowledge, the largest scale multi- modal contrastive pre-training framework for PSG foundation models, jointly aligning waveform and interval- based modalities, uncovering comprehensive inter-modal physiological correlations.
(ii) Scaling law investigation: We systematically explore scaling PSG foundation models along modality diversity and parameter dimensions, demonstrating predictable improvements in cross- cohort generalization with minimal task-specific labels.
(iit) Cross-modal training objective: We propose Demography, Age, Site History-aware InfoNCE (DASH-InfoNCE), context-aware contrastive objective that conditions negative-sample weight- ing on demographic, age, acquisition-site, and recording-history metadata. This metadata-guided weighting suppresses cohort-specific shorteuts and improves robustness and cross-site generaliza- tion across heterogeneous PSG sensor montages.
(iv) Comprehensive downstream evaluation: We extensively evaluate sleep2vec on both SHHS and WSC datasets, spanning tasks such as sleep staging, demographic prediction, and diagnostic out- comes, representing the broadest evaluation of PSG foundation model to date.
RELATED WORK
Multimodal alignment for flexible inference. Contrastive alignment maps heterogeneous inputs into a shared embedding space, enabling zero-shot transfer, retrieval, and robustness to input per- mutations. In vision language, CLIP popularized large-scale image- texte--exttee text alignment (Radford et al., 2021]. and many-to-one binding across six modalities in ImageBind (Girdhar et al., (023). In par- ficular, PSG contains tens of synchronized channels (EEG, EOG, EMG, ECG, Nasal airflow, Respi- ratory effort, SpO2, etc.), yel prior muliimoall alignment works seldom exlend beyond EEG-only or few paired channels, and rarely handle montage shifts at this scale.
Self-supervised learning for sleep and PSG data. Self-supervised learning (SSL) for sleep data has evolved from early approaches targeting task-specific objectives such sleep staging using fixed PSG montages (Supratak et al. 2017 Perslev et al. (021). toward broader pre-training frame- works. Recent works emphasize constructing foundational models but typically remain limited by: (i) pre-training strategies narrowly tailored to single downstream tasks or restricted label sets (Fox al. 2025). (ii) alignment restricted to selected subsets of PSG channels, thus failing to address comprehensive multimodal integration (Fang et al. 2024, Narayanswamy et al.) 2024 Luo et al.) 2024 Thapa et al., 2024; 2025). or (iii) employing cross-modal generative methods that priori- tize modality-specifie signal fidelity rather than explicitly aligning heterogeneous modalities (Chen) ct al. 2024 Nie et al. 2025). Consequently, cross-modal alignment covering the full PSG spectrum remains largely unexplored, with most pre-training focusing on fixed, small montages.
Scaling and generalization. In language and vision, performance follows predictable trends as model and data scale. Despite rapid progress, systematic studies of scaling laws for physiological time series and PSG remain sparse. Existing PSG SSL. studies seldom probe modality diverrity scaling or parameter scaling. To our knowledge, systematic modality-divrrsie scaling has not been charted in sleep; existing studies also under-report parameter/data scaling for PSG SSL, leaving open how capability grows with both model size and channel count.
METHOD
3.1 DATASET AND PREPROCESSING
We leveraged publicly available PSG datasets for pre-training, including the Human Sleep Project (HSP) (Sun et al. 2023) and four cohorts obtained from the National Sleep Research Resource (NSRR) (Zhang et al. 2018); the Sleep Heart Health Study (SHHS) (Quan et al.) 1997). Osteo- porotic Fractures in Men Study (MrOS) (Blackwell al. 2011), Multi-Fthnic Study of Atheroscle-
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rosis (MESA) (Chnn et al. 2015) and Wisconsin Sleep Cohort (WSC) (Young al. 2009). collec- tively encompassing multi-center,, multi-device acquisitions from diverse demographic populations (age range: 1-109; recording span: 1995-present). Table A presents an overview of the five pre- training cohorts, Apnea Positive Pressure Long-term Efficacy Study (APPLES) Quan et al. (2011) is additionally used external validation cohort. The five datasets
harmonized into unified corpus comprising 42,249 overnight recordings from 30,852 subjects, processed through a standard- ized pipeline minimize cohort-specific biases and ensure symmetrical handling during batching and evaluation.
pool of nine PSG channels across cohorts was established as shown in Figure comprising two groups of signals differentiated by sampling rates: higher sampling rate electrophysiological
signals, including EEG, EOG, EMG, ECG uniformly resampled to 128 Hz, and lower sampling rate physiological signals, including Nasal airflow, ABD/Thor belt, SpO2. Inter-Beat Interval (IBI) and respiratory signals uniformly resampled Hz. Only minimal preprocessing is applied the higher sampling rate signals to preserve raw signal characteristics crucial for downstream physiolog- ical interpretation, involving temporal resampling to the target frequency and Z-score normalization with vohort-invarian statistics. The IBI channel is derived from ECG R-peak detection, with raw inter-beat intervals cleaned for outliers and artifacts and then linearly interpolated Hz sequence. The Respiratory effort reflects breathing cycles extracted from either Nasal Airflow or Abdominal Belt, standardized by band-limiting, and resampling to 4 Hz. Both IBI and Respira- tory effort can be captured only by PSG but also using simpler, low-burden hardware such as ballisocardiograahyy mats or other contactless sensors (Chen et al., 2025).
a continuous
Participant information retained when available, including age, gender, and recording site, fa- cilitate cohort-aware analysis and difficulty estimation during pre-training. Participant--lvel data partitions are established prevent data leakage across splits. dedicated pre-training split (Npe-train tine-train23.999 =23,934 participants) is exclusively reserved for foundation model learning, while down- stream splits follow 8:1:1 ratio (Nunin/N.a//Nes==8, 792/7, 16), ensuring no participant over- lap and identical modality coverage. For downstream evaluation, sleep staging labels on SHHS and WSC as well as clinical diagnosis labels on SHHS were aligned with the same participant-leve splits in pre-training, ensuring consistency and preventing any data leakage.
3.2 MODEL ARCHITECTURE
Tokemizers
FEG
Intra-subject Segrents
Pairmatsc
Yi
Y
Yx
EOG
RoF onFormer Backborn
Modal Apnostic
X1
X; -
ISI
Inter-subject Segrents
Nasal AinFloM
X2
tho/ Belt
Wig
A31
X,
Respiratory
Sp0
X
Flodal Spec1fit
Figure 2: An illustration of the multimodal pre-training framework. Each overnight PSG record- ing is partitioned into intra-subject segments (different temporal slices from the same individual) and inter-subject segments (slices from different individuals). which are independently tokenized via modality-speciee quence before processing through modality-agnostie RoFormer backbone. Hidden states from the backbone at each timestep are projected into shared alignment space, enabling timestep-wise pair- wise contrastive alignment across modalities.
MLP tokenizers. A learnable [CLS] token is prepended to each masked se-
A minimalistic tokenizer based on multi-layer perceptron (MLP) implemented, comprising two feed-forward layers and a residual connection. The tokenizer maps input 30-second tokens into embeddings of dimension D through an initial linear transformation that projects inputs into
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intermediate hidden representation of dimension 2D, activated by the SiLU nonlinearity (Elfwing et al. 2018) and regularized using dropout with a 0.1 probability. The choice of 30-second token aligns with the standard epoch duration recommended by the American Academy of Sleep Medicine (AASM) guidelines (Berry et al., 2012 2017) for polysomnographic analysis. Subsequently, this hidden representation is linearly transformed into the final embedding space (D). In parallel, resid- ual linear transformation directly maps the inputs into the output embedding dimension, enhancing gradient flow and training stability. A LayerNorm is further applied to the final embedding for nor- malization. Cross-modks sampling rate differences are resolved by encoding 30-second tokens into tokens of equal embedding dimension using modality-specciff tokenizers, each operating directly on the original sampling rates, resulting in temporally aligned embeddings across modalities to be fed into the aggasity backbone.
As illustrated in Figure 2 to maintain stable optimization as the number of modalities increases, each mini-batch is constructed around a single modality pair (ma; my). At the batch level, two modalities are sampled from those available in the batch, and each training sample contributes one aligned view from each selected modality, forming the contrastive pairs used for pre-training. In- dependent time-step masking is then applied to these paired instances to enhance robustness and mitigate shortcut learning. Each 30-second token has a 15% probability of being replaced by a learnable, modality-specific mask token, after which alignment is conducted exclusively between these masked segments. A dedicated learnable [CLS] token is prepended to the sequence, the re- sulting input then processed through modality-agnostic RoFormer backbone (Su a (024). is important to emphasize that the RoFormer backbone in sleep2vec should be viewed as one con- crete instantiation of a generic modality-agnostic sequence encoder rather than core contribution in isolation. The aim is not to advocate RoFormer as the uniquely optimal architecture for PSG, but to show that pairing flexible backbone capable of ingesting arbitrary channel subsets with a metadata-aware contrastive alignment objective provides simple and effective recipe for handling heterogeneous PSG montages. In principle, other Transformer style or state-space sequence en- coders could be substituted without changing the overall framework, and we expect the benefits of unified cross-cohort pre- training and metadata-awar aligmment to transfer across such choices. The backbone outputs hidden states for each timestep, well as a global nocturnal representation the [CLS] position. These hidden states are projected into a shared 128-dimensional alignment space via a shared three-layer MLP projection head, enabling the application of cross-modal contrastive loss at each timestep.
During fine-tuning, both masking and the contrastive learning projection head removed, and modal configurations remain fixed per downstream task. Task-specific heads directly operate on backbone features. Sequence-Ievel tasks (e.g.. sleep staging) use per-time-step hidden states, while aggregate tasks (e.g. gender, age, or clinical diagnosis) rely on the global nocturnal representation from the [CLS] position. When multiple modalities are available at inference, their representations are aggregated using simple fusion strategies such as averaging, concatenation, or small gating module. Specific fusion methods employed per task are detailed in the experimental results section.
3.3 CROSS-MODAL ALIGNMENT OBJECTIVE: DASH-INFONCE
During pre-training, each mini-batch contains B paired segments, each of length L timesteps. For segment index i E (I....,B3 B), time index t E (1,..., 4). and modality {ma, ma), denote vil E Rd as the corresponding d-dimensional embedding. Given vi as its lg normalized product (m))
given by ((m)) = =v."") //.///ll/0 /|lv([m) (m) ||2. the cosine similarity is
(mma) v((ra)) E [-1,1, $t..1 =vvir viee
i.j E (I....,B,, t {l...., .......,..
(1)
where (: denotes the dot product. The index mapping π. I...., + I...., B specifies the number of paired segments in modality my for an anchor in modality man where batches are typically aligned such that (1) = i. Demographic and acquisition metadata for segment are denoted by (a,, gt. C2 u.), where a; E R+, G.C, C, and u; represent age, gender, acquisition site, and the subject-nige identifier, respectively. These variables are used solely for weighting and modulation below and never labels in the learning objective.
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3.3.1 BASE FORMULATION: TEMPORAL INFONCE
With temperature 0, the baseline timestep InfoNCE loss aligning ma to my is
B
log exp(Sta(00./7) 1
Σ 1=1
(2)
buse
B
Σラ exppexxp(pp0 (Bi.s//T)
This objective encourages the similarity between the paired cross-modal embeddings (i, to exceed the similarities to all in- batch, same-tine nume-time candidates (i. J. t) with j * (T)). The temperature coefficient controls the concentration of the induced softmax distribution.
3.3.2 PROPOSED DASH-INFONCE Loss
A novel DASH-InfONCE loss that reshapes the negative set by (i) metadata-driven sample weighting and (ii) margin- based modulation of pseudo-negatives (i.e., negatives from the same subject-night) introduced in this section. For anchor (i, t), define
exp
(DASH(i,4) = - -log プラ=1 WiS
where Wi, 2. O are segment-specific weights satisfying Σド= 1, 0 is modulation strength, and ) 2 reduces the effective logit of designated pseudo-negatives before the soft- max. The binary indicator d4, E 0, 1 selects which pairs are margin-modullated, with the convention di,z(s) ensuring that positives
not penalized. The optional factor Pi.jr [0, encodes time- specific signals, and in our instantiation below, we set vú to fixed margin with Ps.j. absorbed into that choice. Relative to Eq. (2), the numerator unchanged while the denominator concentrates probability mass demographically similar, presumably harder negatives via wi,j and diminishes the competitive strength of sune-subject-night negatives through the subtractive margin 7 t
3.3.3 SAMPLE WEIGHTINGG MECHANISM
Let A: IR, R4 + R_ non-negative, symmetric kernel that decreases with the age difference
(g)
aayl We further define similarity factors for gender and acquisition site as si {Tsamee Yait]
and s. {8sames dain }. with Tsame Vailf 2 0 and deame dam 2 O, where the value is chosen according to whether gi = gj and c; = cj. respectively.
(c)
Given the pseadv-negative indicator his = [l[u új A Aj [()] E {0, 1}. the unnormalized
weights are defined as aij = k((a;,ag) 8.. 8i weights are computed by
(5) (c)
+ ehi,j where = 10-6, The normalized
Oiijj
Σ wi =
Wa.j E
Ciik
This weighting scheme assigns higher values to negatives closely matched by age, gender, and acquisition site. Constant & ensures negatives from the same subject-night retain non-zero weights, stabilizing the denominator in Eq. when closely matched demographic negatives are rare.
3.3.4 PSEUDO-NEGATIVE MODULATION
We modulate only negatives drawn from the same subject-night. Let di,j hesj and take margin- only instantiation of gh:
[m, di,j 1, o. di,j O,
(dd. Pr.j.))
(5)
By combining Eq. 5 and Eq. the fixed margin ym is subtracted from the logits of sumeesubject- night negatives prior to the softmax. This reduces the tendency to over-penalize semantically close negatives originating from the same subject-night while preserving their presence in the denominator through Eq. (4).

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3.3.5 FINAL OBJECTIVE
The DASH-IntONCE objective averages the per-anchor loss Eq. (3) over instances and time:
DASH
Σ (ASH((, t),
CDASH
DASH
Averaging across enforces aligmment each timestep. Note that every component of Eq. (3 -Eg. (6) depends only on demographic and acquisition metadata (44, Ii, c4) and identifiers via Eq. no downstream task labels are used during pre-training.
3.4 FEATURE FUSION
In multimodal physiological tasks, the way modality-specific features are fused/aggregated has direct impact on performance. na've Coneat strategy (i.e. concatenating embeddings before clas- sification) produces high-dimensional, sparse representations that inflate computation and sample complexity, exacerbating overfitting. Conversely, Mean aggregation (i.e. element-wise averaging) assumes equal informativeness and reliability across channels; in practice, physiological streams differ in SNR and complementary content, so uniform averaging washes out pondality-speciffe cues and is brittle under missing sensors.
To address these limitations, we adopt the Gating Mechanism, which introduces learnable scalar weights assigned each modality. This approach adaptively emphasizes modalities based on their informativeness, dynamically adjusting the contribution of each PSG channel. Consequently, yields more expressive, compact, and task-oriented aggregated representation, enabling efficient downstream learning.
EXPERIMENTS
4.1 PRE-TRAINING DIAGNOSTICS: ALIGNMENT & RETRIEVAL
Subject-Modalte Alignment
Time-Modality Alignment
(Rigees
(Oigtex Subject Tos)
Sa Bays a96
Random
Init
Ours
Random Init
Ours
Color:
EEG
EOG
ECG
Nasal AirlloM
ABD/Thor
Respiratory
5p0,
Figure 3: 1-SNE visualization of encoder embeddings comparing random initialization and post- pre-training results. Left Panel (Subject-Modality Alignment): Visualization of [CLS] token embeddings shows that pre-training effectively clusters embeddings from different modalities into distinct, subject-specific groups, indicating aligned subject-level physiological states. Right Panel (Time-Modality Alignment): Visualization of timestep-level embeddings, dot sizes indicate tem- poral ordering (larger - later). Pre-trained embeddings form structured trajectories, contrasting with the scattered distribution observed prior to training.
To assess the effectiveness of multimodal alignment, Figure 3 (left panel) visualizes the [CLS] to- ken embeddings using t-SNE both prior to and following pre-training. Initially, embeddings cluster by modality, reflecting intrinsic modality-specific biases and heterogeneous signal characteristics. After pre-training, embeddings from distinct modalities corresponding to the same subject are co- herently grouped, indicating improved alignment and preservation of subject-specific structures.
Further analysis of timestep-level embeddings from a random subject (Figure 3 right panel) re- veals structured trajectories emerging post-training, indicating effective modality alignment at

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finer temporal resolution. The coordinated variation in dot sizes across concentric rings emphasizes temporal consistency within the representations. Such consistency is beneficial for downstream se- quential tasks like sleep staging. underscoring the practical advantages of the temporally aligned a eep2vec embeddings.
4.2
DOWNSTREAM TUNING RESULTS
4.2.1 SLEEP STAGING
Table 1: Performance of five-class sleep staging (W/N1/22/3//REM) across PSG channel and
models on SHHS. Reported metrics regarding overall performance including Accuracy (Acc., % Cohen Kappa (x), Macro-F1 (MF1, %) Sensitivity (Sens., %) and Specificity (Spec., %). Class-wise F1 (%) is also listed. Baselines reproduced by us for fair comparison are marked with t. Note that these foundation model (FM) baselines were individually pre-trained for each PSG channel subset, whereas eep2vec: was pre-trained only once across all modalities. "FULL CHANC the fixed channel configuration that each model is designed for and individually pre-trained on. Underlined numbers indicate the best overall performance within each channel set; bold numbers denote the best performance among FMs; bold undeunndrl numbers indicate cases where the FM surpasses specialized models.
refers
PSG Channel Set
Overall Performance (1)
Class wise Fl (f)
Inference Subset
Model
Acc.
MFI
Scns.
Spec.
W
NI
N2
N3
REM
Specialized (non-FM) Model
DocpSloopNet Suppaaak et al.
2017
81.0
0.73
73.9
85.4
40,5
82.5
79.3
81.9
SleepEFGNet (Mousyyi nl.
2019
73.9
0.65
68.4
81.3
34.4
73.4
75.9
77.0
AttnSlocp (Eldgic & sur 2021
84.2
0.78
75.3
86.7
33.2
87.1
87.1
82.1
XSleepNet
87.6
0.83
80.7
79.7
96.5
91.6
51.4
88.5
85.0
88.4
XSleepNe12 (Phan el sl. 2021
87.5
0.83
81.0
80.4
96.5
92.0
49.9
88.3
85.0
88.2
L-SegSteepNat
2023
87.6
0.83
80.3
79.4
96.5
92.4
48.6
88.2
83.9
88.5
SleepTransfoma (Phan et al.
2022
87.7
0.83
80.1
78.7
96.5
92.2
46.1
88.3
85.2
88.6
Foundation Model
SlecpFM Thapa et al. 2024
2025
86.3
76.3
75.3
96.1
93.2
36.6
86.3
77.3
88.1
sleep2vec
87.4
0.82
77.3
76.2
96.1
92.4
40.1
86.5
77.7
88.7
Specialized (mon-FM) Model
Sun at. 2019 Goldammer et
71.3
0.59
57.3
56.9
91.7
85.2
4.8
70.1
49.9
76.4
77.2
0.68
63.6
62.7
93.4
58.2
15.5
76.4
55.5
82.4
IBI & RESP
Foundaioon Mtodel
SlcepPM Thapa et
2024
79.7
65 A
94.2
90.4
12.9
78.4
SleepFounder (Nle et mi 2025
80.9
0.73
68.3
67.0
94.5
91.3
22.3
80.0
61.1
85.9
sleep2vee
83.0
0.75
65.5
95.1
5.3
80.3
60.9
Foundation Model
ECG ABD
SlscpFM Thapa et 2024
2025
77.9
0.68
62.7
93.6
6.6
76.9
60.9
80.4
sleep2vec
82.7
0.75
65.6
65.2
95.0
92.6
6.2
80.6
62.4
86.
Specialized (mon-FM) Model
SogSleepNet (Phan ct
2019
87.2
0.82
80.2
78.7
96.3
91.8
49.1
88.2
83.5
88.2
XSleepNet1 Phan al
2021
89.1
0.85
82.3
81.2
96.9
EEG & FOG & EMG
XSleepNer2 Piin
89.1
0.85
82.2
81.4
96.9
et
85.8
Foundation Model
SleepFM Thapa et
87.0
77.8
96.4
40.7
86.3
sleep2vec
88.3
0.83
78.7
77.9
96.8
94.5
40.6
87.8
79.8
89.0
Foundation Model
SleepIM Thapa et al 2024 2025
86.7
0.81
77.3
76.9
96.3
93.4
39.2
86.7
77.1
90.3
CHANNELS
PFTSleep etal.
87.7
82.3
sleep2vec (MOONCE) sleep2vec
88.4
0.84
78.6
77.9
96.8
94.7
39.8
87.9
80.0
90.8
88.6
We first assess the quality of the learned representations on sleep staging. Experiments are per- formed on the SHHS and WSC datasets, and the results are presented in Tables and 15 respec- tively. Several trends be observed from Table [
(i) There remains a very limited number comprehensive works on PSG data, as the majority of existing methods focus narrowly single-channel EEG small subsets of physiological signals. Specialized methods typically achieve top performance across available channel sets, setting a chal- lenging baseline for foundation models.
(ii) Foundation models generally exhibit lower performance compared to specialized sleep staging approaches optimized specifically for sleep data. This is evident in EEG-only scenarios, where spe-
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cialized models consistently hold slight edges in overall metrics compared to baseline FM SleepFM (Acc. 86.3%) and sleep2vec (Acc. 97).. However, the gap marginal, with sleep2vec nearly matching specialized models in certain metrics (N of 0.82 vs. 0.83).
(iii) sleep2vec consistently outperforms baseline foundation models across all PSG channel subsets. Notable improvements appear in configurations such as "IBI & RESP", where sleep2vec exceeds baseline FMs (Acc.: 83.0% vs. SleepFM 79.7% and SleepFounder 80.9%). sleep2vec can also achieve performance comparable to, and in certain cases surpassing, specialized models.
(iv) Increasing modality diversity appears beneficial, with sleep2vec consistently demonstrating per- formance gains when additional physiological signals are included. This trend highlights the scal- ability and utility of incorporating diverse modalities into foundational model frameworks, further underscoring the capability of sleep2vec effectively leverage multimodal physiological signals.
To further assess cross-cohort generalization, we evaluate models fine-tuned on SHHS directly on the APPLES cohort, which is unseen during both pre-training and fine-tuning, As shown in Table sleep2vec preserves strong robustness under distribution shift and consistently outperforms baseline methods.
Table Cross-cohort evaluation of five-class sleep staging (W/N1/N2/33/REM) across PSG chan- nel sets and models on unseen APPLES. Models are fine-tuned SHHS without seeing any data from APPLES during both pre-training and fine-tuning. "FULL CHANNELS" refers to the fixed channel configuration that each model is designed for and individually pre-trained Underlined numbers indicate the best overall performance within each channel set; bold numbers denote the best performance among FMs; bold-underlined numbers indicate cases where the FM surpasses specialized models.
PSG Channel Set
Overall Performance (f)
Class wise Fl (7)
Inference Subset
Model
Acc.
MFI
Sens.
Spec.
N1
N2
N3
REM
Specialized (non-FM) Model (Sun et al. 2019
63.6
0.46
48.8
56.4
89.0
78.6
1.7
68.5
20.2
75.2
Goldammer
61.2
90.4
79.9
7.5
72.6
25.8
79.5
Foundation Model
IBI & RESP
SleepEM 'Thapa et 2024
2025
69.1
0.55
54.3
65.2
91.0
82.2
4.6
73.5
28.1
82.8
SleepFoundeer [Nie
2025
68.8
0.55
55.6
67.3
91.0
85.5
8.8
71.9
27.2
84.6
sleep2vec (InfoNCF)
71.5
0.59
56.5
66.7
91.7
86.8
7.9
74.1
29.5
84.
sleep2vec
73.2
0.61
57.8
66.2
92.1
76.5
31.5
84.2
Foundation Model
SleepEM (Thapa et 2024
2025
71.4
60.0
58.9
91.6
85.5
24.5
75.8
32.0
81.5
FULL CHANNELS
sleep2vee [INOONE)] sleep2vec
76.8
0.67
63.5
73.3
93.4
90.5
24.0
79.5
35.5
88.1
78.4
0.69
65.2
72.0
93.8
89.6
27.3
81.8
39.0
88.2
4.2.2
LEAVE-ONE-ORE ANALYSIS
(Acc.) )
88.5
87.5
87
86.5
None
5p0
RCG
AB0/
RESP
IBI
Nasal
EDG
EHG
ERG
Dropped Modality
Thoi
Figure 4: Leave-one-out analysis on the SHHS sleep staging task. Each bar represents model ac- curacy when one of the nine modalities is excluded during both pre-training and fine-tuning. The observed drop in accuracy relative to the full channels baseline (labeled "None") reflects the contri- bution and relative importance of each individual modality to the overall model performance.
To further examine the role of individual modalities, we performed a leave-one-out (LOO) study, where modality was excluded during both pre-training and fine-tuning. Using the same setup

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Published as a conference paper at ICLR 2026
in Section 4.2.1
evaluated the model on the SHHS dataset. As shown in Figure 4 excluding
modalities such as EEG or IBI leads to a substantial accuracy drop, while others (e.g., 2p02, EOG) have relatively minor effects.
4.2.3
CLINICAL DISEASE PREDICTION AND MODALITY SCALING
(80C (ROC-AUC)
(ROC-AUC)
(ROC-UUC
(ROC-AUE)
0.30
0.08
0.70
0.75
8.55
0.67
0.75
0.74
0.62
0.74
9.54
8.65
0.71
0.73
0.53
(N)
(V)
2 3
9 (N)
allergies/sinus problens
ast
hypertension
coronary heart disease
sleep2vec (DASH-InFOONCE)
sleep2vec (InFoNCE)
Figure 5: ROC-AUC scores for disease prediction tasks using varying numbers of modalities (N) on the SHHS dataset. Results are averaged across all possible modality combinations of size
For clinical evaluation, four prevalent and clinically significant conditions are selected from the SHHS dataset, including allergies/sinue problems, asthma, hypertension and coronary heart disease, as shown in F These conditions span two major physiological systems directly monitored by PSG, the respiratory system and the cardiovascular system. By including these diverse clinical out- comes, we explicitly test whether cross-modal embeddings generalize robustly across organ systems and sensor subsets.
Specifically, for a given number modalities N, we enumerate all possible modality combinations, build corresponding ensemble models, and report the average ROC-AUC. Results presented in Fig- ure demonstrate: (i) clear modality- scaling effects, as performance consistently improves as more modalities incorporated, suggesting robust scaling law across clinical prediction tasks; (ii) the proposed DASH-InfONCE loss consistently outperforms the standard InfoNCE baseline, indicat- ing its effectiveness in harnessing richer inter-modal physiological correlations. This performance advantage of DASH-InioNCE becomes increasingly pronounced with additional modalities, under- scoring its efficacy large-scale multimodal pre-training scenarios.
5 CONCLUSION
In this work, we introduced sleep2vec, foundation model aligning multimodal polysomnogra- phy (PSG) signals into unified embedding space for robust physiological representation learning. Leveraging over 42,000 overnight recordings and our novel DASH-InfONCE loss, which accounts for demographie, age, site, and history variations, provements on sleep staging and clinical prediction tasks. Experiments confirmed s s1eep2vec's robustness incomplete sensor data and revealed clear scaling laws with increased modality di- versity and larger model sizes. Our results establish s sleep2vec as a scalable and versatile tool, enabling generalized physiological monitoring and clinical decision support in sleep medicine.
demonstrated significant performance im-
6 ETHICS STATEMENT
The datasets employed consist of anonymized PSG recordings from publicly available sources. Eth- ical approval and informed consent for the original data collection secured by the institutions responsible for the individual studies. All subject identifiers were removed prior to dataset acquisi- tion, ensuring complete anonymization and protecting participants' privacy.
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7 REPRODUCIBILITY STATEMENT
To ensure the reproducibility of our research, we provide the following details. A comprehensive description of our data processing pipeline is provided in Section3.11 Details of the datasets involved and the training configurations for the proposed model presented in Appendices A.2 and A.3 respectively. Furthermore, the specific configuration used for fine-tuning is provided in Appendix A.7
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A APPENDIX
A.1 PHYSIOLOGICAL SIGNALS POLYSOMNOGRAPHYY (PSG)
Table 3: Polysomnography (PSG) channels and derived interval-based features used in this study. High sampling rate electrophysiological channels include EEG, EMG, EOG, and ECG; lower sam- pling rate cardiopulmonary and oximetry channels encompass Nasal airflow, Abdominal/Thoracie belt (ABD/Thor belt), and SpO2. Respiratory effort (RESP) and Inter-Beat Beat Interval (IBI) are interval-derived features, obtained from respiratory channels (ABD/Thor belt if available, otherwise Nasal airflow) and ECG channels, respectively, and can also be measured via wearable devices. Sampling rate ranges summarize AASM Berry et al. (2022 2017 minimum recommended digital sampling rates, actual device settings may vary.
Physiological Signals
Typical Placement
Sampling (Hz)
Common Usage
PSG Channel
EBG
Scalp
200-500
Detecting sleep stages, brain activity patterns, and brief arousals
EOG
Around the eyes
200-500
Identifying eye movements, especially for REM sleep detection and stage transitions
Chin EMG
Under the chin
200-500
Measuring muscle tone, useful for distinguishing REM and detecting disorders such as bruxism
ECG
Chest leads
200-500
Hcart activity and variability (HR/HRV), used to study arousals and cardiorespiratory patterns
Nasal airflow
Under the nose
25-100
Detecting apnoeas/hyponnees and breathing iregularities
ABD/Thor belt
Around abdomen & chest
250
Tracking breathing effort, helping to classify types sleep- seep-disordered breathing Monitoring blood oxygen drops during breathing events, used to measure severity
SpO₂
Finger prohe
10-25
Interval-derived Feature
Respiratory effort (RESP)
4-10
Breath-to- -or-ttttbbrrhhh timing, used for variability analysis and detecting abnormal breathing cycles
Inter-Beat Interval (IBI)
4-10
Beat-to-best timing, used to compute HRRV and study autonomic regulation during sleep
Polysomnography (PSG) is a comprehensive overnight test performed using a polysomnograph that records multiple physiological signals during sleep, including brain activity, eye movements, muscle tone, heart rhythm, breathing patterns, and oxygen levels. An illustration of a subject wearing PSG device for nocturnal sleep recording presented Figure [ It is used in clinical and research settings to diagnose and study sleep disorders, including but not limited to sleep apnea, narcolepsy, and insomnia, by providing an objective assessment of sleep stages and abnormalities.
A.2 OVERVIEW OF DATASETS
Table 4: Overview of the PSG datasets used in this study.
Dataset
Age Spai
Durstion
Recoeding Span Recordings
Sobjocts Total Hours
Sleep Heart Health Study (SHHS)) (Oum et
[007]
50-90
8.9 1.1
1905-2003
8,440
5,795
75.431
Wisconsin Sleep Colert (WSC) Noing a
A077
57-85
8.0 0.8
2000-2015
2,570
1.123
30,520
Osteoparocic Frrccuuee in Mon Stooy
pracewell well et 2011
67-90
11.5 2.3
2000-2005
3,930
2,905
45.110
Mult-E:hos Echnic Study Alteroselaross (MESA)
S4-94
2010-2012
2055
2056
21,745
Human Sloep Project (HSP))
7.6
presess
18993
190,32
Apoen Poeltive Pressure Longstem kay Stdy (APPLES) Quan et 2011
8.2
2003-2004
1,896
1,066
8955
In SHHS and MrOS, ages greater than 90 top-coded and recorded as 90.
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Table 4 compiles six large publicly available PSG cohorts spanning children older-adult popula- tions (ages 1-109) and nearly three decades of acquisition -1995-preeete As indicated by the Du- ration column in Table 4 all recordings correspond to full-night PSG studies. The corpus of training data comprises 42,249 overnight recordings from 30,852 subjects across these five cohorts (SHHS, WSC, MrOS, MESA, and HSP). In addition, the APPLES cohort, consisting of 1,096 recordings, is used as an external validation cohort. HSP contributes the broadest age range and largest share of data, while SHHS, WSC, MrOS, MESA and APPLES provide well-characterized adult cohorts. The diversity in demographics and collection periods enables robust pre-training and evaluation under heterogeneous sensors and montages.
A.3 PRE-TRAINING CONFIGURATIONS
During pre-training we ensured consistency by using fixed batch size of 320 across all models.
Including the prepended [CLS] token, the maximum sequence length was capped at L = 121. For contrastive learning, the temperature parameter was set to = 0.2. Optimization was performed using AdamW (learning rate 5 10 $, B (0.9, 0.95), 10 and weight decay of 0.01 for non-normalization weights), with a linear warmup over 3% of steps followed by cosine decay.
Table 5: Configurations of the s1eep2vec model across different sizes.
Configuration
Small
Medium
Large
Number of parameters
63.5M
133. 13
238.2 338.2M
Hidden dimension
512
768
1024
Number of layers
8
12
16
Attention heads Segment duration pprentrannnng)
16
16
16
hour
hour
hour
Segment duration fine-tuning))
Whole night
Whole night
Whole night
The architectural hyper- pyper-parameters are adjusted to yield s eep2 variants with varying numbers of parameters, as detailed in Table Unless otherwise stated, all experiments were conducted using the s s1eep2ve variant.
For the sample-weighting mechanism introduced in Section 3.3.3
we employed a Laplace kernll
<<a;;, ag) exp
[0o ma;
dass
with bandwidth Oage 20.0. This choice reflects clinical observations that sleep physiology and sleep-disordered breathing vary gradually with age rather than abruptly. A 20-year scale captures meaningful across-lifespan differences without over-penalizing small age gaps (Ohayon al. 2004
Li al. 2022).
Gender coefficients are Tsame 1.0 and Jdiff 0.8, acknowledging sex differences in sleep architecture and in the prevalence/severity of sleep- diep-disoddered breathing that are present but dominant at the individual-record level (Peppard et al., (013).. The site coefficients were set to 8mame = 1.3 and Sanr 0.8 to account for systematic inter-site variation (device, montage, scoring protocol) that frequently larger than gender effects in multi-center cohorts (Rosenberg & Van Hout 2013 Kuna et al. 2013). We used 5 10-6 for numerical stability.
Finally, fixed margin term ymn = 0.1 (Eq. and Eq. 5 Section was applied when modulating pseudo-negatives from the same subject-night, reflecting the high correlation of repeated segments within a recording and discouraging them from being treated as fully independent negatives.
Each pre-training run used two high-memory GPUs, the largest configuration trained for up to 48 hours.
We intentionally avoid any cross-modal reconstruction objective during pre-training, s s1eep2vec trained solely with the InfoNCE and DASH-InfoNCE losses described above, which encour- age alignment between heterogeneous PSG montages and associated metadata without requiring an explicit generative decoder. In preliminary experiments, a variant that replaced the contrastive ob- jective with generic cross-modal reconstruction module was implemented. Under matched data
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and compute budgets, this reconstruction based variant was substantially harder to optimize and frequently failed to converge to competitive solutions. These observations, combined with the ad- ditional computational overhead of large reconstruction decoders, motivated design choice to focus on contrastive alignment as a more stable and scalable route to robust missing-modality gen- eralization.
A.4 ABLATION STUDY
A.4.1 ABLATION OF FEATURE FUSION STRATEGIES
Table 6: Ablation study of different feature-fusion strategies and their impact on five-class sleep- staging performance (W/N/N2//3/REM), The evaluated model is the medium-sized sleep2vec variant, fine-tuned on SHHS and evaluated on unseen APPLES. Bold numbers denote the best performance among FMs.
Overall Performance (1)
Class-wise Fl (t)
Feature Fusion
Acc.
MF1
Sens.
Spec.
Wake
N1
N2
N3
REM
Concatenation
76.9
0.66
63.1
71.8
93.3
89.9
21.0
79.9
37.3
87.4
Mean
78.1
0.68
64.7
71.9
93.7
89.3
26.2
81.5
38.4
88.1
Gating
78.4
0.69
65.2
72.0
93.8
89.6
27.3
81.8
39.0
88.2
To further assess the influence different feature-fusion strategies, we perform an ablation study comparing the three representative designs incorporated in our framework: Concatenation, Mean and the adopted Gating mechanism.
In practice, Concatenation rapidly becomes computationally prohibitive the number of modali- ties increases, since it expands the hidden representation dimensionality and consequently inflates both the parameter count and VRAM usage of subsequent layers. Additionally, it does not provide measurable performance benefits over the lightweight alternatives and is therefore not used as our default fusion approach.
Our analysis thus focuses on Mean and Gating, two scalable and computationally efficient paradigms. Across representative downstream sleep staging tasks, both strategies achieve com- petitive performance. Nonetheless, the Gating mechanism consistently yields small but robust im- provements over Mean, and further offers enhanced interpretability through modality-speciff gating coefficients that quantify the contribution of each input signal.
The results of this ablation study are reported in Table Collectively, these findings justify our choice of Gating as the default fusion strategy, as it provides balanced combination of scalability, empirical performance and interpretability.
A.4.2 ABLATION OF METADATA COMPONENTS DASH-
To isolate the contribution of each metadata component within DASH-InfONCE, an additional ab- lation study was conducted to examine the model's generalization behavior under distribution shift. Specifically, we evaluate the impact of incorporating individual metadata, including age, gender, and recording site, performance in the unseen APPLES cohort.
For this analysis, models are pre-trained with only one metadata enabled at a time, followed by fine- tuning on SHHS and direct evaluation on APPLES without any further adaptation. Downstream sleep staging performance as well as cross-modal retrieval accuracy (Recall@) 1) are both reported, quantifying the quality of modality alignment in the shared embedding space.
Results presented in Table 7 and Table suggest that activating any single metadata consistently improves performance over the vanilla InfoNCE baseline the unseen cohort, either through higher Macro-F1 (MF1) or improved retrieval alignment. Combining all three metadata factors in the full DASH-InfoNCE formulation provides the strongest overall performance.
These findings demonstrate that age, gender and site information contribute complementary signals that enhance robustness under distribution shift. Together, they strengthen cross-modal alignment
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Table 7: Ablation study of metadata-awar contrastive objectives. Four contrastive formulations are compared during pre-training: (i) vanilla InfoNCE, (ii) single-metadata- -waar aware variants that in- corporate one metadata factor at a time (Age-aware, Gender-aware, Site-aware InfoNCE), and (iii) the proposed DASH-InfONCE. All medium-sized sleep2vec models are pre-trained on the full mul- timodal corpus and subsequently fine-tuned and evaluated on SHHS for five-class sleep staging. "Retrieval Acc." corresponds to recall @ in cross-modal retrieval task, given query embedding from one modality, the model must retrieve the correctly paired PSG segment from a pool of candi- dates drawn from other modalities, and Retrieval Acc. is the fraction of queries for which the true pair is ranked first. "Retrieval Acc." computed on the fixed validation set from the pre-training corpus and is independent of the downstream fine-tuning and evaluation cohort. Bold numbers de- note the best performance among FMs.
Overall Performance (1)
Class-wise Fl (1)
Retrieval Acc. (f)
Method
Acc.
MFI
Sens.
Spec.
Wake
N1
REM
Vanilla InfoNCE
88.4
0.84
78.6
77.9
96.8
94.7
39.8
87.9
80.0
90.8
0.351
Age-aware
88.3
0.83
78.7
77.2
96.7
94.6
41.6
88.0
78.1
91.0
0,355
Gender-awan
88.5
0.84
79.2
78.1
96.8
94.7
43.0
88.1
79.3
91.1
0.356
Site-aware
88.1
0.83
78.0
75.9
96.6
94.6
39.9
87.8
76.8
90.9
0,363
DASH-InfONCE
88.6
0.84
79.5
78.4
96.8
94.8
44.1
88.2
79.2
91.2
0.368
Table 8: Ablation study of intaddata-aaard contrastive objectives on the unseen APPLES dataset. experimental setups, training protocols, and evaluation metries follow those reported in Table with retrieval accuracy ("Retrieval Acc.") computed on the same validation set. All medium-sized sleep2vec models are pre-trained on the full multimodal corpus and evaluated on unseen APPLES
for five-class sleep staging. Bold numbers denote the best performance among FMs.
Overall Performance (+)
Class-wisc (t)
Retrieval Acc. (f)
Method
Acc.
MF1
Sens.
Spec.
Wake
N1
N2
N3
REM
Vanilla InfoNCE
76.8
0.67
63.5
73.3
93.4
90.5
24.0
79.5
35.5
88.1
0.351
Age-aware
78.3
0.68
64.9
72.0
93.7
89.5
26.6
81.7
39.0
88.0
0.355
Gender-awan aware
78.3
0.69
65.5
72.4
93.7
89.7
29.1
81.7
39,2
87.8
0.356
Site-aware
78.1
0.68
64.8
72.1
93.6
90.3
26.2
81.2
38.4
87.8
0.363
DASH-IniONCE
78.4
0.69
65.2
72.0
93.8
89.6
27.3
81.8
39.0
88.2
0.368
and cross-cohort generalization, highlighting DASH-InfoNCE as an effective strategy for improving model stability and transferability in unseen clinical cohorts.
A.4.3 ABLATION OF MASKING STRATEGIES
Table 9: Ablation study on the effect of masking ratios during pre-training. Other experimental setups, training protocols, and evaluation metrics follow those reported in Table 7 with retrieval accuracy ("Retrieval Acc.") computed on the same validation set. All medium-sized sleep2vec models are fine-tuner tine-tuned and evaluated on SHHS for five-class sleep staging. Bold numbers denote the best performance among FMs.
Overall Performance (1)
Class-wise Fl (t)
Retrieval Acc. (1)
Mask Ratio
Acc.
MF1
Sens.
Spec.
Wake
N1
N3
REM
0%
88.5
0.84
78.8
77.2
96.7
94.7
41.9
88.2
78.2
91.1
0.403
15%
88.6
0.84
79.5
78.4
96.8
94.8
44.1
88.2
79.2
91.2
0.368
30%
88.4
0.83
78.8
77.2
96.7
94.7
41.6
88.1
78.6
91.1
0.281
The role of masking strength during contrastive pre-training is also investigated to assess how differ- ent corruption levels affect the robustness of the learned representations, Specifically, downstream sleep staging performance of three masking ratios (0%. 15% and 30%) is reported in Table 9 (in- domain SHHS) and Table (cross-cohorr APPLES).
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Table 10: Ablation study on the effect of masking ratios during pre-training. All experimental setups, training protocols, and evaluation metrics follow those reported in Table with retrieval accuracy ("Retrieval Acc.") computed on the same validation set. All medium-sized sleep2vec models are fine-tuned on SHHS and evaluated unseen APPLES for five-class sleep staging. Bold numbers denote the best performance among FMs.
Overall Performance ())
Class-wise Fl (1)
Retrieval Acc. (*)
Mask Ratio
Acc.
MF1
Sens.
Spec.
Wake
N1
N3
REM
0%
77.9
0.68
64.0
71.4
93.6
89.4
23.2
81.5
38.6
87.4
0.403
15%
78.4
0.69
65.2
72.0
93.8
89.6
27.3
81.8
39.0
88.2
0.368
30%
77.5
0.67
72.2
93.5
89.8
20.0
80.8
38.7
87.9
0.281
A moderate masking ratio of 15% yields the most favorable balance between representational bustness and downstream accuracy. Compared to the no-masking condition, moderate masking leads consistent improvements in Macro-F1 (MF1) and class-wise metrics across both evaluation set- tings. In contrast, increasing the masking ratio to 30% provides no additional generalization benefits, suggesting that overly aggressive masking may overly corrupt physiologically meaningful temporal structure. These results indicate that moderate masking functions as an effective regularizer during contrastive physiological pre-training.
We additionally note that retrieval accuracy is highest under the 0% masking configuration. This trend is likely driven by the closer match between the training objective and the retrieval evaluation when no corruption is applied, rather than reflecting superior generalization. Retrieval accuracy should therefore be interpreted jointly with downstream task performance when comparing masking strategies.
A.5 FURTHER INVESTIGATION OF ABLATION STUDY
A.5.1
SCALING LAW OF FOUNDATION MODEL PARAMETERS
100
95
90
80
Paron.
75
63.5M
133.7M
65
60
Acc.
(+)
(b)
Figure 6: Performance comparison across varying model sizes (63.5M, 133.70 7M and 238.2M 2... param- eters) for sleep staging on (a) SHHS and (b) WSC datasets. Results demonstrate a clear scaling law, where increasing the number of parameters consistently improves Accuracy (Acc.), Cohen's Kappa (k), Macro-F1 (MF1), Sensitivity (Sens.) and Specificity (Spec.), underscoring the effectiveness of scaling physiological foundation models in capturing complex sleep dynamics.
The scaling behavior of sleep2vec concerning model parameters is presented in Figure 6 The performance across two benchmark datasets, SHHS (a) and WSC (b), consistently improves as the number of parameters increases from 63.5M to 238.2N This improvement is evident in key metrics such as Accuracy (Acc.), Cohen's Kappa (k), Macro-FI (MFI), Sensitivity (Sens.) and Specificity (Spec.). Notably, the scaling effect exhibits diminishing returns, suggesting that while larger model sizes capture increasingly complex physiological patterns inherent in sleep data, the incremental gains become smaller with each parameter increase. Overall, these results affirm the robustness and scalability the s1eep2vec architecture, indicating its suitability for capturing detailed, multimodal physiological dynamics in sleep studies.
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InfoNCE Out
Vild Low
3.5
3.0
2.5
520p
The validation loss curves compare vanilla InfoNCE baseline with DASH-InfONEE models at three scales, small, medium, and large, denoted as Ours-S, Ours-M, and Ours-L, respec- tively. As model scale increases, training consistently converges lower validation loss. Moreover, at matched model scales, DASH-InfoNCE achieves lower validation loss than the comparable In- foNCE baseline.
Figure 7:
0.7
Ours-
Gurs-M Oure4
Vake ceteval scorety
0.3
0.2
2500 5000
7500
12500
17500
20000
Figure 8: The validation retrieval accuracy curves compare vanilla InfoNCE baseline with DASH- InfoNCE models three scales, small, medium, and large, denoted Ours-S, Ours-M, and Ours- L. respectively. Retrieval accuracy consistently improves as model scale increases. Moreover, at matched model scales, InSH-InfoNCE achieves higher retrieval accuracy than the corresponding InfoNCE baseline, demonstrating more effective cross-modal alignment.
The validation loss curves in Figure and Figure g further support the scaling analysis above. Be- yond the expected trend that larger models attain lower validation loss and higher retricval accuracy, key additional finding is that DASH-InfoNCE consistently yields improved optimization behav- ior and superior retrieval performance relative to the vanilla InfoNCE objective when evaluated at comparable model scales.
A.5.2 SCALING LAW PRE-TRAINING DATA SIZE
The impact of pre-train pre-training data scale on cross-cohort generalization is examined by varying the fraction of the pre-training corpus while keeping the downstream fine-tuning protocol fixed. Models are pre-trained using 25%, 50%, 75% and 100% of the available data and subsequently evaluated on unseen cohort.
As shown Table and Table 12 increasing the amount of pre-training data leads to consistent improvements in downstream performance, particularly in Macro-F1 (MF1) and Cohen's The
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Table 11: Effect of pre-training data size on sleep staging performance (W/N1/22/3/REMM) All experimental setups, training protocols, and evaluation metrics follow those reported in Table with retrieval accuracy ("Retrieval Acc.") computed on the same validation set. All medium-sizee sleep2vec models are fine-tuned and evaluated SHHS, Bold numbers denote the best perfor- mance among FMs.
Overall Performance (t)
Class-wise FI (f)
Retrieval Acc. (t)
Data Fraction
Acc.
Sens.
Spee.
Wake
N1
N2
N3
REM
25%
87.1
0.82
77.2
76.3
96.4
93.7
37.6
86.8
77.7
89.9
0.281
50%
88.0
0.83
77.9
76.8
96.6
94.3
38.5
87.5
78.8
90.6
0.295
75%
88.4
0.83
78.7
77.1
96.7
94.6
41.2
88.0
78.3
91.1
0.340
100%
88.6
0.84
79.5
78.4
96.8
94.8
44.1
88.2
79.2
91.2
0.368
Table 12: Effect of pre-training data size on cross-cohort sleep staging performance (W/N1/22/33/REM)) All experimental setups, training protocols, and evaluation metrics follow those reported in Table [ with retrieval accuracy ("Retrieval Acc.") computed on the same valida- tion set. All medium-sizee sleep2vec models are fine-tuned on SHHS and evaluated on the unseen APPLES. Bold numbers denote the best performance among FMs.
Overall Performance (t)
Class wise (f)
Retrieval Acc. (t)
Data Fraction
Acc.
MFI
Sens.
Spec.
Wake
N1
N2
N3
REM
25%
76.1
0.65
62.7
70.9
93.1
88.5
23.1
79.5
36.2
86.4
0.281
50%
76.8
0.66
63.0
71.7
93.3
89.5
21.6
80.0
36.2
87.7
0.295
75%
78.0
0.68
64.6
71.7
93.6
89.1
25.4
81.5
38.8
88.0
0.340
100%
78.4
0.69
65.2
72.0
93.8
89.6
27.3
81.8
39.0
88.2
0.368
performance improvements are most substantial when moving from low to intermediate-scale data regimes, with diminishing returns the full dataset is utilized. This trend suggests that larger- scale physiological pre-training promotes more robust and transferable representations, which is especially valuable under distribution shift.
Collectively, these findings indicate that the proposed framework benefits notably from increased data scale and exhibits stable generalization properties across cohorts.
A.5.3 SCALING LAW OF PRE-TRAINING MODALITY NUMBER
Table 13: Effect of mrodality-scalingg strategies SHHS. "Single-stage" corresponds to the proposed medium sized sleep2vec model pre-trained from scratch using available modalities. "Stage 1/2/3" implement a curriculum in which training begins with the most frequent and informative channels (EEG, RESP, and IBI in Stage 1), followed by the addition EOG, ECG, and nasal airflow in Stage 2, and finally EMG, abdominal//horacie belts, and SpO, in Stage 3, with each stage continuing from the previous checkpoint. All models are fine-tuned and evaluated on SHHS under identical EEG only and RESP+IBI downstream settings. Bold numbers indicate the best performance among FMs.
PSG Channel Set
Overall Performance (t)
Class-wise Fl (t)
Inference Subset
Curriculum
Acc.
MF1
Sens.
Spec.
Wake
N1
N2
N3
REM
Stage 1
86.9
0.81
77.4
76.3
96.3
93.5
41.0
86.7
88.6
Stage 2
87.0
0.82
77.7
76.5
96.3
93.6
42.5
86.7
76.8
88.8
EEG
Stage 3
87.3
0.82
77.2
75.7
96.4
93.7
38.9
87.0
77.6
Single-stage
87.4
0.82
77.3
76.6
96.5
94.2
40.1
86.5
77.7
88.3
Stage 1
82.1
0.74
67.5
66.6
94.8
91.9
15.2
80.5
63.7
86.3
Stage 2
82.2
0.75
66.3
94.9
91.8
6.6
80.2
66.2
86.6
RESP+IBI
Stage 3
82.5
0.75
95.0
92.3
17.1
80.9
63.7
Single-stage
83.0
0.75
65.9
65.8
95.1
86.6
5.3
80.9
64.3
86.6
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Table 14: Effect of modality-scaling strategies evaluated on the unseen APPLES. All models are fine-tuned on SHHS and evaluated on unseen APPLES dataset under identical EEG only and RESP+IBI downstream settings. Bold numbers indicate the best performance among FMs.
PSG Channel Set
Overall Performance (t)
Class-wis wise Fl (t)
Inference Subset
Curriculum
Acc.
k
MF1
Sens.
Spec.
Wake
N1
N2
REM
Stage I
76.6
0.66
63.6
72.8
93.3
89,4
25,4
79.8
36.7
86.5
Stage 2
77.4
0.67
64.3
72.5
93.5
89.6
26.8
80.8
37.6
86.9
EEG
Stage 3
77.2
0.67
63.2
72.1
93.4
89.1
20.8
80.8
37.8
87.3
Single-stage
76.7
0.66
62.5
71.9
93.3
89.6
19.1
80.0
37.3
86.7
Stage 1
72.3
0.60
56.8
66.4
91.9
86.7
7.1
75.3
31.0
83.8
Stage 2
71.2
0.59
55,4
66.7
91.7
86.8
3.1
73.9
29.0
83.9
RESP+IBI
Stage 3
72.3
0.60
57.1
67.0
91.9
87.6
7.8
74.8
30.1
85.0
Single-stage
73.2
0.61
57.8
66.2
92.1
86.5
10.2
76.5
31.5
84.2
The effect of pre-training modality count on downstream performance is examined by comparing four variants of the DASH-InfoNCE sleep2vec framework. The three curriculum stages differ only in the modality sets introduced during pre-training: Stage includes the most frequent and informa- tive channels (EEG, RESP, and IBI); Stage 2 resumes from the Stage 1 checkpoint and adds EOG, ECG, and nasal airflow; Stage incorporates the remaining, less frequent channels (EMG, abdom- inal/thoracic belts, and SpO2). By contrast, the Single-stage model trained from scratch using all modalities simultaneously.
All variants are fine-tuned using identical protocols on SHHS with either EEG or RESP+IBI as downstream inputs, and are evaluated both in-domain SHHS and cross-cohort on APPLES. As reported Table 13 and Table [4] expanding the modality set during pre-training yields small but consistent gains the Stage baseline, with the Single-stage model generally attaining the strongest or atar-strongest performance. Importantly, the addition of rarer modalities in Stage does not degrade performance, and all variants fall within narrow accuracy and Macro-F1 (MF1) range. This stability indicates that sleep2vec scales predictably and robustly with respect to both modality number and modality diversity.
Moreover, as the number of pre-training modalities increases, the model retains, and often enhances, its performance when fine-tuned using only the originally available modalities, providing greater flexibility in downstream modality selection without sacrificing accuracy.
A.6 RETRIEVAL ACCURACY MATRIX
The alignment quality is qualitatively validated using average recall@1 I metrics computed across modalities on
comprising 10,109 segments. sleep2vec achieves recall I of 36.8%, significantly surpassing the baseline with the original InfoNCE loss, which achieves 35. %. This highlights the representational quality and discriminative capacity the obtained embeddings.
The Recall@1 cross-modal retrieval accuracy matrix visualized in Figure 9 demonstrates distinct modality-speeifie alignment patterns. Notably, Respiratory and ABD/Thor exhibit exceptionally high mutual retrieval accuracy (0.82), aligning well with their known physiological coupling. EEG also demonstrates strong alignment with EOG (0.69). Conversely, modalities such as SpO₂ and EMG generally yield lower retrieval accuracies (== 0.2 2244)),, reflecting comparatively weaker phys- iological correlations.
A.7 COMPLEMENTARY DOWNSTREAM FINE-TUNING RESULTS
A.7.1 SLEEP STAGING CONFIGURATIONS
For fine-tuning the transformer backbone in the sleep staging task, we utilized Low-Rank Adaptation (LoRA) to achieve parameter-efficien adaptation. Specifically, LoRA adapters were integrated into the query, key and value projections of every transformer layer, while keeping the original backbone parameters frozen. Unless specified otherwise, we set the rank to = scaling factor 16, dropout probability p 0.05, without incorporating additional biases. For multimodal
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1.0
1.00
0.39
0.49
0.38
0.23
0.42
0.27
0.9
Respiratory-
0.51
EEG
0.33
0.6
0.5
EMG
0.25
0.39
0.30
0.38
14
0.34
0.22
5p02
0.21
0.24
0.20
0.1.8
0.19
0.12
1.00
0.25
0.18
0.3
ABD/Thor
0.44
0.82
0.55
0.45
0.54
0.34
0 27
1.00
0.42
0.2
Nasal
0.26
0.40
0.32
0.26
0.32
0.21
0.41
1.00
Respiratory
EEG
ECG
EOG
EMG
Spo2
ABDmT
Nasal
Figure 9: Recall@) I retrieval accuracy matrix of the learned representations, Rows correspond to the query modality, while columns indicate the retrieved modality.
fine-tuning scenarios, the same set of LoRA adapters was shared across all modalities. Instead of relying on a special classification token, transformer output embeddings from the final layer each time step were individually projected through a two-layer MLP classifier, whose hidden dimension matched that of the backbone output. Optimization was performed using the AdamW optimizer with a learning rate of 1 x 10-4 and weight decay of 1 x 10-5
Baseline models are reproduced using their original hyperparameters as reported in the correspond- ing publications.
A.7.2 PERFORMANCE ON WSC DATASET
Several trends emerge from Table 15
(i) Comprehensive research on PSG data remains limited, as existing methods commonly focus on single-channel EEG or small subsets of physiological signals. Specialized models typically pro- vide established benchmarks, particularly in cardiorespiratory modality configurations, presenting significant evaluation standards for generalized foundation models.
(ii) Foundation models consistently exhibit strong performance, often surpassing specialized sleep staging methods. This is evident the EEG configuration, where sleep2vec notably achieves the best overall performance among foundation models (Accuracy: 86.3%, n: 0.80), outperforming baseline FM SleepFM (Accuracy: 84.3%, k: 0.76).
(iii) sleep2vec consistently demonstrates superior performance across various PSG channel subsets. Specifically, in the "IBI& RESP" channel configuration. sleep2vec substantially surpasses both spe- cialized and baseline foundation models (Accuracy: 81 1.6% compared to SleepFoundert 79.8% and SleepFM's 77.7%). Similarly, the "EEG & EOG EMG" subset, sleep2vee outperforms baseline foundation models (Accuracy: 86.8% 84.5%) and considerably surpasses specialized methods (Aceuracy: 86.8% vs. 77.6%). In the Full Channels configuration, sleep2vec achieves the highest performance across multiple metrics (Accuracy: 87.3%, :0.81), underscoring the effectiveness of leveraging comprehensive modality combinations.
To further assess cross-cohort generalization, evaluate models fine-tuned WSC directly the APPLES cohort, which is unseen during both pre-training and fine-tuning. similar trend is observed in Table mirroring the cross-cohort generalization results reported in Table
23

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Published as a conference paper at ICLR 2026
Table 15: Performance of five-class sleep staging (W/NI/N2/N3/REM) across PSG channel sets and models on WSC. Reported metrics regarding overall performance including Accuracy (Acc., %), Cohen Kappa (x)) Macro-F1 (MF1, %), Sensitivity (Sens., %) and Specificity (Spec., %). Class- wise F1 (%) is also listed. Baselines reproduced by us for fair comparison are marked with i. Note that these foundation model baselines were individually pre-trained for each PSG channel subset, whereas s sleep2vec was pre-trained only once across all modalities. "FULL CHANNELS" refers to the fixed channel configuration that each model is designed for and individually pre- pre-trained Other naming conventions follow the one adopted in Table Underlined numbers indicate the best overall performance within each channel set; bold numbers denote the best performance among FMs; ondd-underlined numbers indicate cases where the FM surpasses specialized models.
PSG Channel Set
Overall Performance (t)
Class-wise F1 (1)
Inference Subvet
Sens.
Spec.
Foundation Model
SleepPM (Thapa et sleep2vec
90.1
86.3
0.80
74.8
73.5
96.1
93.8
45.2
89.1
60.1
85.7
Specialized (mon-FM) Model
Sun et 2019
74.7
0.59
56.6
55.9
91.5
79.6
15.4
81
26,6
80.4
73.0
13.9
IBI & RESP
Founadiio Wondy
SleepPM Thapa
SleepHounder (N1c et 2025
79.8
0.69
65.5
64.8
93.7
86.8
28.0
85.2
43.5
84.2
sleep2voc
81.6
65.1
29.1
84.0
Specialized (mon-FM) Model
2021
77.6
BEG & EOG & EMG
Foundation Model
SlecpFM Thapa et 2024
slocp2vec
86.8
0.80
77.4
75.5
96.1
92.8
51.2
90.1
63.9
88.8
Foundation Model
SteepEM Thapa et 2024 2025
84.6
0.77
75.4
75.0
95.4
90.0
45.2
89.4
64.1
88.1
FULL CHANNELS
PFTSleep
sleep2vec (MIONCE) sleep2vec
87.1
0.81
78.2
76.7
96.2
93.1
50.7
90.5
67.3
89.5
87.3
77.5
96.3
53.8
Table 16: Cross-cohort evaluation five-class sleep staging (W/N1/N2//3//EEN/ across PSG channel sets and models unseen APPLES. Models are fine-tuned WSC without seeing any data from APPLES during both pre-training and fine-tuning. "FULL CHANNELS" refers to the fixed channel configuration that each model is designed for and individually pre-trained Underlined numbers indicate the best overall performance within each channel set; bold numbers denote the best performance among FMs; bold-underlindd numbers indicate cases where the FM surpasses specialized models.
PSG Channel Set
Overall Performance (t)
Class wise Fl (?)
Inference Subset
Model
Acc.
MF1
Sens.
Spec.
N1
N2
N3
REM
Specialized (non-FM) Model
(Sun et al. 2019
69.9
0.54
51.6
53.3
90.4
78.6
8.9
76,2
17.1
77.2
Goldaammer
76.7
IBI & RESP
Foundation Model
SlsepEM TThaaa et 2024
2025
74.4
56.3
56.5
91.9
17.0
81.1
20.2
81.3
SleepFounster [Nre
74.0
0.61
59.4
64.0
92.1
84.7
17.9
79.2
31.7
83.5
sleep2vec (InfoNCF]
76.1
0.64
55.9
55.5
92.4
85.4
16.8
81.5
14.4
81.5
sleep2vec
76.4
0.64
57.7
57.7
92.7
85.7
19.2
81.8
19.5
82.5
Foundation Model
SleepEM (Thapa et 2025
85.4
32.3
FULL CHANNELS
2leep2vee TITINCEE
77.5
0.68
64.8
70.5
93.7
87.5
30.8
82.4
37.5
86.0
sleop2vec
80.1
66.3
06.5
94.1
85.3
38.9
85.2
24

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Published as a conference paper at ICLR 2026
A.7.3
INTERPRETABILITY OF CHANNEL- CONTRIBUTION
OOO
O
IBI Respiratory
ABD/Thor & EEG EOG
EMG
Full Channels
Airrlow
AB0/Thor
Respiratory
5p0;
Figure 10: Visualization of modality sppccifcc weights learned by the Gating Mechanism based fea- ture fusion for the four SHHS sleep staging configurations in Table [
Beyond performance of sleep staging, the Gated scalar fusion used during fine-tuning provides transparent, modality- attribution of the downstream decision. Concretely, the learned scalars quantify each modality's contribution to the task task-specific representation.
To illustrate, we analyze SHHS sleep staging under the four modality configurations in Table [ Figure TO presents the normalized fusion weights (visualized with a sharpening factor 7 = 0.4 that clarifies display while preserving relative ratios). Across tasks, EEG receives the largest weight, consistent with sleep staging being primarily annotated from EEG. EOG and EMG provide sub- stantial complementary signal, while cardiorespiratory channels (e.g., airflow, ABD/Thor belt, IBI) carry smaller weights and SpO, contributes the least.
These weights are global, task-level attributions rather than per 30-second explanations, and they do not capture higher-order interactions between modalities. Nevertheless, these weights offer an interpretable summary of channel contribution that aligns with domain expectations and can inform sensor selection in channel-limited deployments.
A.7.4 CLINICAL DIAGNOSIS CONFIGURATION
For clinical diagnosis tasks, the backbone parameters were kept frozen without applying LoRA adapters. Predictions were derived from the [CLS] token output of the final transformer layer, sub- sequently passed through two-layer MLP classifier whose hidden dimension matched the back- bone's output dimension. Training utilized the AdamW optimizer with learning rate of 1 x 10-4 and a weight decay of x 10 maintaining consistency with the sleep staging experimental setup. Note that site information is not used covariate during the fine-tuning process.
A.8 LLM USAGE
We use LLMs correct grammatical errors.
Some elements in Figure (an illustration of a participant wearing PSG device for sleep recording, along with icons representing each physiological signal modality) and Figure (participant icons in bed) were generated using LLMs.
A.9 ACKNOWLEDGMENT ON RESOURCE USAGE
The following acknowledgment information is listed as required by the National Sleep Research Resource (NSRR) (Zhang et al. 2018) and the individual datasets.
The Sleep Heart Health Study (SHHS) was supported by National Heart, Lung, and Blood Institute cooperative agreements UO1HL53316 (University of California, Davis), U01HL53931 (New York University), U01HL53934 (University of Minnesota), U01HL53937 and U01HL64366 (Johns Hop- kins University). UO1HL53938 (University of Arizona), UO1HL533900 (University of Washington), U01HL53941 (Boston University), and UO1HL63463 (Case Western Reserve University). The Na- tional Sleep Research Resource was supported by the National Heart, Lung, and Blood Institute (R24 HI.1 75N92019R009 (0002))..
25

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Published as a conference paper at ICLR 2026
The National Heart, Lung, and Blood Institute provided funding for the ancillary MrOS Sleep Study, "Outcomes of Sleep Disorders in Older Men," " under the following grant numbers: R01 HL071194 RO1 HL070848, RO1 HL070847, RO1 HL070842, RO1 HL070841, RO1 HL070837, RO1 HL070838, and RO1 HL070839. The National Sleep Research Resource was supported by the National Heart, Lung, and Blood Institute (R24 HI. 75N92019R002).
The Multi-Ethnic Study of Atherosclerosis (MESA) Sleep Ancillary study was funded by NIH- NHLBI Association of Sleep Disorders with Cardiovascular Health Across Ethnic Groups (RO1 HL098433). MESA is supported by NHLBI funded contracts JHHSN2682015000031 NOI-HC- 95159. H0I-HC-95100 N01-HC-95161, N01-HC-95162. N01-HC-95163 N01-HC-95164, NO1- HC-95165, N01-HC-95166 NO1-HC-95161 167, N01-HC-95168 and N01-HC-95169 from the Na-
tional Heart, Lung, and Blood Institute, and by cooperative agreements UL1-TR-000000, ULI1 TR-001079, and MIL1-TR-001420 funded by NCATS. The National Sleep Research Resource was supported by the National Heart, Lung, and Blood Institute (R24 HI 14477, 75N92019R002).
This Wisconsin Sleep Cohort Study was supported by the U.S. National Institutes of Health, Na- tional Heart, Lung, and Blood Institute (R01HL62252), National Institute on Aging (R01 AG036833, R01AG058680), and the National Center for Research Resources (IULI RR01500050111 1). The National Sleep Research Resource was supported by the U.S. National Institutes of Health, National Heart Lung and Blood Institute (R24 HL114473, 75N92019R002).
The Apnea Positive Pressure Long-term Efficacy Study (APPLES) was supported by the National Heart, Lung, and Blood Institute (U01HL68060). The National Sleep Research Resource sup- ported by the U.S. National Institutes of Health, National Heart Lung and Blood Institute (R24 HL1 14473, 75N92019R002).
26


Reviews:
[{"review_id": "hb0LGlb5Y0", "reviewer": "Reviewer_Yn6v", "summary": "The paper introduces Sleep2Vec, a foundation model for nocturnal physiological recordings that learns a shared representation space across nine polysomnography related channels, including high rate neural and ocular signals and lower rate cardiorespiratory signals. The core idea is to treat concurrent nocturnal signals as complementary views of the same latent physiological state and to enforce alignment at the level of short epochs through a contrastive objective. The authors train on more than forty two thousand overnight recordings collected from several large public cohorts and harmonized through a common preprocessing pipeline. They propose a metadata aware contrastive loss named DASH InfoNCE that adjusts the weight of negatives by demographic attributes, recording site and subject night identity in order to reduce cohort shortcuts and to keep very similar samples from dominating the denominator. They report results on sleep staging for SHHS and WSC, on clinical outcome prediction for several conditions, and they include an ablation on leaving out individual modalities. They also attempt to characterize scaling behaviour along both the number of modalities and model capacity. The work aims to show that unified cross modal alignment for sleep becomes practical once enough heterogeneous data are brought together.", "questions": "1.\tCan you report results in a leave one cohort out setting, for example training on HSP, SHHS, MrOS and MESA and evaluating only on WSC, with and without DASH InfoNCE, in order to show that the metadata aware weighting is particularly helpful when the target cohort is unseen.\n2.\tHow sensitive is performance to the choice of the age kernel bandwidth and to the relative weights for same gender and same site samples. An ablation where these hyperparameters are perturbed would clarify to what extent the proposed loss needs tuning for new populations.\n3.\tIn the two modality sampling scheme for pre training, have you tried curriculum strategies where the model first sees frequent and informative modality pairs such as EEG with respiratory effort and later progresses to rarer combinations. If so, did this accelerate convergence.\n4.\tFor clinical outcome prediction, are the labels balanced across cohorts. If some conditions appear mostly in certain centers, the site aware part of the loss could risk encoding site related biases. Please clarify how this was mitigated.\n5.\tCould the authors provide a short comparison in compute cost between this approach and a variant that simply reconstructs masked modalities, since one of the selling points is that the alignment objective is more suitable for flexible inference.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "Strengths\nThe paper addresses an important practical problem in sleep medicine and mobile or low burden monitoring, which is the presence of many possible channel layouts and frequent missing sensors. Showing that one pre trained model can handle nine different signal types and remain robust when some are absent is a meaningful step toward realistic deployment across devices and centers. The methodological core is coherent. The use of two modality batches, masking, a single backbone and a shared projection head creates a simple recipe that other researchers can reproduce. The proposed contrastive objective is well argued. It uses age, gender and site to prioritize harder negatives, which is a clever way to turn epidemiological and acquisition metadata into useful training signals. The experimental section is quite extensive. The authors compare to specialized sleep staging networks on SHHS and show that the gap becomes small, sometimes negligible, despite the fact that their model was trained to be general and not tailored to sleep staging alone. They also evaluate on lower information channel groups such as IBI and respiratory signals, where the proposed model still outperforms prior foundation models and even reaches or exceeds specialized approaches in some metrics. The leave one out analysis is particularly useful since it quantifies the relative importance of each modality and confirms known clinical intuition that EEG and IBI carry more discriminative information for stage recognition than some of the other channels. The analysis of clinical prediction tasks with increasing numbers of modalities is also valuable as it suggests a predictable scaling trend.", "weaknesses": "Weaknesses\nAlthough the paper claims better cross site generalization through metadata aware weighting, the current experiments do not fully isolate this effect. It would be more convincing to show a split in which one cohort is entirely held out during pre training and used only for evaluation, and to show that the gap between the standard InfoNCE and DASH InfoNCE enlarges in that setting. At present, the evidence comes from aggregate metrics and from the claim that site and demographic similarity are properly captured in the loss.\nThe paper positions itself as establishing scaling laws for nocturnal biosignals but the current analysis is still rather preliminary. The number of model sizes and modality subsets is limited and the curves are shown mostly for downstream performance without a deeper look at loss scaling during pre training, data efficiency or breakpoints where adding modalities stops helping. A more systematic study would make this part of the contribution stronger.\nThe reliance on a RoFormer backbone is sensible, yet the paper does not compare to other recent time series architectures that are specialized for long physiological sequences, for instance models with channel wise attention or structured state space models, which might be competitive or more efficient.\nWhile the dataset section describes harmonization in detail, the paper does not quantify the amount of missing channels per cohort, nor does it show per cohort downstream results. Since the main motivation of the work is robustness to sensor dropout and to heterogeneous montages, a more explicit evaluation on realistic missing patterns would be helpful.\nFinally, the work composes several ideas that have each appeared in some form in prior sleep foundation models, for example large scale contrastive pre training on ECG and respiratory signals and modality aware fusion. The novelty therefore resides mostly in the combination at scale and in the specific loss design rather than in a single transformative idea.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "J50jIc7faR", "reviewer": "Reviewer_7LYS", "summary": "The paper proposes a new foundation model for handling multi-modal PSD data. Instead of classic FM for sleep staging, the proposed sleep2vec can take as input all channels from the PSG study. To do this the authors use cross-modal alignment to have a shared representation. \nSleep2vec is compared to various SOTA models for either channel-specific tasks or full channels over more than 30.000 subjects.", "questions": "- What is the computation time of a full channel setup compared to an EEG-only setup? Considering all the channels, the results are improving slightly. I'm wondering if the additional computation time is justified? The same question arises when comparing FM to non-FM computation time versus score. It can bring more justification to why we use FM instead of a specialized model.\n- If the feature fusion is interesting, as it gives intuition of which channels are more useful, did you compare the three fusion strategies that you introduced (Concat, AVG, and gating)?\n- In the appendix, you showed that increasing the number of parameters always increases the performance. When do you reach a plateau ? Maybe a threshold between time computation and performances can be found?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 4, "contribution": 3, "strengths": "- The paper is very easy to follow. \n- The paper proposes a new representation learning for all the channels of a PSG.\n- The method is tested over a big corpus of subjects comprising more than 30.000 subjects. \n- With the gating mechanism, we can see which channels bring more importance for the classification, giving good interpretability of the model.\n- Good t-SNR visualization that gives insight into understanding the use of the proposed method.", "weaknesses": "- Figure 2 introduces the Intra-subject and Inter-subject segments. This is never used in the entire paper. This additional information, in my opinion, is likely to lead to a misunderstanding of the method.\n- The motivation is that no model deals with the full channels of PSG. In Table 1, two competitors are proposed for a full channel setting. Does that mean the model can handle all the channels? What is the addition of sleep2vec? \n- The competitors presented in Table 1 are never introduced. I understand that it can be challenging to describe everything in detail, but a description can be provided in the appendix, and at least FM can be properly introduced, such as SleepFM and PFTSleep. That can give better positioning to the literature.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "xnZ3RMGfWt", "reviewer": "Reviewer_BLg5", "summary": "This paper proposes a unified cross-modal pretraining framework, Sleep2Vec, designed for multimodal sleep representation learning. Specifically, the framework jointly enhances model generalization and robustness through cross-modal contrastive learning and data reconstruction tasks. The authors evaluate Sleep2Vec across multiple downstream tasks such as sleep staging, respiratory disorder detection, and periodic limb movement detection.", "questions": "Please see Weaknesses", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1.\tMulti-task pretraining objective: Combines reconstruction and contrastive objectives to strengthen inter-modal coordination and representation learning.\n\n2.\tCross-modal modeling innovation: The modality reconstruction task effectively addresses missing-modality scenarios.\n\n3.\tComprehensive experiments: Covers a wide range of datasets and tasks, demonstrating strong adaptability.", "weaknesses": "1.\tLimited originality: The approach lacks novelty, as many prior works have already explored missing-modality and contrastive learning in sleep research, such as CIMSleepNet (NeurIPS 2024), MultiConsSleepNet (IEEE JBHI 2025), and SleepSMC (ICLR 2025).\n\n2.\tOutdated baselines: The comparison methods are mostly old, missing fair comparisons with the latest relevant works mentioned above.\n\n3.\tLack of ablation studies: The paper does not explicitly analyze the independent contributions of each module.\n\n4.\tUnaddressed modality inconsistency: Although cross-modal reconstruction is used, the generalization ability under missing-modality conditions during training or testing is not sufficiently demonstrated.\n\n5.\tNo cross-subject experiments: The paper does not evaluate subject-independent generalization, which is crucial for assessing clinical applicability in real-world sleep studies.\n\n6.\tUnclear experimental interpretation: For instance, Table 1 does not clarify whether the first column corresponds to training or inference settings.\n\n7.\tNo statistical significance analysis: The experiments lack multiple runs or reported standard deviations, making the result stability uncertain.\n\nRelated Work\n\n[1] Shen Q, Xin J, Dai B, et al. Robust sleep staging over incomplete multimodal physiological signals via contrastive imagination[J]. Advances in Neural Information Processing Systems, 2024, 37: 112025-112049.\n\n[2] Pan J, Yu Y, Li M, et al. A Multimodal Consistency-Based Self-Supervised Contrastive Learning Framework for Automated Sleep Staging in Patients With Disorders of Consciousness[J]. IEEE Journal of Biomedical and Health Informatics, 2024.\n\n[3] Ma S, Zhang Y, Chen Y, et al. SleepSMC: Ubiquitous Sleep Staging via Supervised Multimodal Coordination[C]//The Thirteenth International Conference on Learning Representations, 2025.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "jpWzbbSikd", "reviewer": "Reviewer_7ymV", "summary": "The author introduces a foundation model for physiological signal data recorded during sleep time, that is modality agnostic. The model is pretrained using a contrastive loss to encourage subject-specific consistency in the representation space, along with a proposed Dash-InfoNCE objective to help the model distinguish between different input modalities. The proposed framework is evaluated on varied downstreaming tasks including sleep stage classification and disease classification. During the modeling process, varied prior works including specialized models serve as baselines, and ablation studies are conducted including scaling law verification, leave-one-modality-out validation.", "questions": "- The presentation of the main result is a little bit unclear. Results on SHHS and WSC are provided. Is it the case that other datasets don't have the valid corresponding tasks? If that is the case, it should be clearly stated somewhere, as this would also help readers understand how the data were split (e.g., the pretraining data were divided based on certain factors, etc.).\n- The evaluation criteria is a bit confusing, where the fine-tuning setting is not presented very clearly. Is all the baseline fine-tuned using the same setting? Does a different fine-tune strategy might potentially alter the overall observation? \n- There are varied backbone model frameworks that could handle heterogeneous input signal series. Is the observation of the representation separation consistent across different backbone model? It would be clearer if more empirical evidence were provided to clarify whether the backbone’s modality-agnostic nature itself enables it to handle heterogeneous inputs, or whether this capability primarily arises from the proposed loss function design.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- It is the first of this kind of model specifically for sensing signals during sleep that can address heterogeneous input signals (in terms of different sensor signals and varied number of input channels). \n- The evaluation objectives including stage classification and disease classification are representative in terms of revealing the underlying value behind the proposed framework. \n- The design of loss function seems rigorous and ingenious that it adequately addresses the issue of inter-subject variability and how the model handles heterogeneity in the input modalities, as shown by the visualization presented by the author. \n- The problem setup and the aim of the work is clearly depicted.", "weaknesses": "- On the methodology side, though the visualization of the embedding seems promising, there is lack of ablation study on different pre-training approaches, such as using vanilla reconstruction loss, or varied masking rate, etc. \n- Though scaling law is presented, only scaling with model size is provided, but there is a lack of result on scaling with data size. Also the difference in the scaling law results seems not significant just from the bar plot. Probably statistical test results are needed to strengthen the claim of contribution on this aspect. \n- The data statistics is a bit insufficient Specifically, some important information are missing, such as total hours of data or distribution of length of a night, etc. \n- It remains unclear whether the model’s ability to handle heterogeneous inputs comes from the backbone design itself or the proposed loss function.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)