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REVIEW 3 major objections 5 minor 48 references

Automated sleep staging can be made to obey physiological rules without sacrificing accuracy, by wrapping any neural backbone in a constraint layer that discourages rare transitions and enforces minimum bout durations.

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A backbone-agnostic constraint wrapper (soft transition penalty + semi-Markov decoder) makes automated sleep-staging outputs respect physiological transition and duration priors, improving downstream sleep-architecture statistics without sacrificing accuracy.

T0 review reviewed 2026-07-31 challenge →

load-bearing objection Good empirical wrapper paper, but the '91% rare-transition recall at 0.2% TVR' claim is internally impossible and needs to be fixed before this is publishable as-is. the 3 major comments →

arxiv 2607.23284 v1 pith:LEFQG664 submitted 2026-07-25 cs.LG q-bio.QM

StageGuard: Physiologically Constrained Sleep Staging

Juntang Wang , Yihan Wang , Hao Wu , Jiayu Gao , Shixin Xu , Dongmian Zou This is my paper
classification cs.LG q-bio.QM
keywords sleep stagingphysiological constraintssemi-Markov modelconstrained decodingtransition violation ratefragmentation indexsleep architecturedeep learning
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Automated sleep staging has reached expert-level epoch accuracy, yet unconstrained neural networks routinely emit hypnograms that violate physiology: direct Wake-to-REM jumps, REM-to-NREM reversals, and single-epoch bouts. The paper argues these violations are not harmless noise; they bias every downstream sleep-architecture endpoint derived from the hypnogram, including total sleep time, REM latency, sleep efficiency, and awakening counts. StageGuard is a proposed plug-and-play wrapper that adds two mechanisms to any staging backbone: a differentiable soft penalty that discourages rare transitions during training, and a duration-augmented semi-Markov decoder that enforces transition and minimum-duration priors at inference. The central empirical claim is that, across six backbones and four datasets spanning EEG, actigraphy, cardiorespiratory, and radar modalities, this wrapper reduces transition-violation rate from 3.6-12.3% to 0.1-0.8%, lowers fragmentation by 56-62%, maintains or slightly improves accuracy, reduces error on derived sleep statistics by 59-79%, and recovers expert-defined subgroup differences more faithfully. If true, a reliability layer rather than a new biological model is enough to make automated staging scientifically trustworthy.

Core claim

On the paper's own terms, the discovery is that accuracy and physiological validity are separable axes of a staging model's fitness, and the validity gap can be closed by explicit constraints rather than by better representations. The load-bearing mechanism is a semi-Markov Viterbi decoder over a duration-augmented state space, pairing each sleep state with an elapsed-duration counter, encoding minimum dwell times and soft log-probability penalties for rare transitions; unlike a hard prohibition, a rare transition is admitted when emission evidence is overwhelming. Trained jointly with a soft transition-penalty regularizer, the wrapper changes decoded sequences so that constraint satisfactio

What carries the argument

The central object is the duration-augmented semi-Markov decoder: an augmented state space where each sleep state s is paired with a duration counter d from 1 to 10, with a log-transition matrix that forbids leaving state s before a state-specific minimum dwell time d_min(s), scores typical transitions by empirical transition probabilities, and assigns rare transitions (Wake-to-REM, REM-to-NREM) a small penalty of log(0.001) rather than negative infinity. Running Viterbi on this augmented space yields a globally optimal path under the static objective. Around it, the paper wraps a differentiable soft transition penalty that regularizes the backbone's per-epoch logits during training, and a s

Load-bearing premise

The load-bearing premise is that the hand-set rare-transition set (Wake-to-REM, REM-to-NREM) and the minimum-duration thresholds match the true physiology of the cohort being scored; if those priors are wrong for a population, the constraints produce smoothly wrong hypnograms rather than more valid ones, and the paper itself flags narcolepsy, RBD, and pediatric sleep as cohorts where the default profile should not be applied.

What would settle it

Run StageGuard with its default priors on a narcolepsy or RBD cohort with expert-scored hypnograms: if expert-annotated Wake-to-REM events or short REM bouts are systematically deleted or shifted, so that recall of rare transitions falls well below the reported ~91% or REM-latency estimates become more biased than the unconstrained backbone, the claim that constraints improve physiological validity is falsified.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Automated hypnograms from any existing backbone can be made physiologically plausible without retraining, so large-scale sleep studies could re-run their pipelines through the constraint layer to reduce bias in REM latency, WASO, and awakening counts.
  • Sleep-architecture endpoints that are not directly optimized by the method inherit the constraint benefit, with 59-79% lower error on the reported backbone-dataset pairs.
  • Expert-defined subgroup contrasts such as OSA-severity and age differences, which unconstrained AI staging attenuates, are recovered closer to their expert-defined direction and effect size.
  • Rare transitions remain recoverable when evidence is strong, with about 91% recall of expert-annotated rare transitions at the default penalty, so pathologically informative events are not categorically erased.
  • The wrapper adds less than 1% training overhead and roughly 6.5% inference overhead on the largest tested backbone, making it practical for routine deployment.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: the same wrapper logic should transfer to other sequential biomedical classification tasks with known state-transition structure, such as seizure staging or cardiac rhythm annotation, though the paper does not test this.
  • Editorial inference: a natural testable extension is recalibrating the rare-transition set and minimum-duration thresholds on narcolepsy or pediatric cohorts; if diagnostic SOREMPs survive while noise transitions are suppressed, the soft-constraint design is doing what the paper claims.
  • Editorial inference: because the decoder forces the output to match hand-set priors, unusually fragmented but clinically real sleep (for example severe OSA) could be over-constrained; the paper's own subpopulation analysis shows slightly elevated residual transition violations in moderate/severe OSA, hinting at this boundary.
  • Editorial inference: the reported downstream gains likely scale with how fragmented the unconstrained baseline is, which would predict larger improvements on noisier modalities such as radar; the largest accuracy gains in the paper do appear on the radar dataset.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper proposes StageGuard, a backbone-agnostic wrapper for neural sleep staging that combines a soft training-time penalty on physiologically rare transitions (Wake→REM, REM→NREM) with a duration-augmented semi-Markov Viterbi decoder that enforces minimum bout durations and transition penalties at inference. Experiments across six backbones and four datasets report that TVR drops from 3.6–12.3% to 0.1–0.8%, FI drops by 56–62%, accuracy is maintained or slightly improved, errors on downstream sleep-architecture statistics drop by 59–79%, and expert-defined subgroup differences are recovered. The paper carefully separates exact, approximate, and heuristic components and provides ablations, sensitivity analyses, and a code link.

Significance. If the claims hold, StageGuard would be a practically useful reliability layer for automated sleep staging: it is plug-and-play, requires no backbone modification, and is evaluated with leave-one-subject-out CV across multiple architectures and modalities. The explicit decomposition into exact, approximate, and heuristic components is commendable, as are the ablation and sensitivity tables. However, the validity of the central 'recoverable rather than blocked' claim and of the downstream-fidelity interpretation depends on resolving an internal inconsistency in the reported rare-transition recall, and on whether the hand-set physiological priors are appropriate for each target cohort. The latter risk is responsibly acknowledged in §6.1, but the recall inconsistency is a concrete, load-bearing technical issue that must be fixed.

major comments (3)
  1. [Appendix A, Table 13; §4.5; §5] The reported 91% rare-transition recall cannot coexist with the reported TVR values. Expert-scored hypnograms are stated to have TVR ≈ 2.1%; StageGuard brings SHHS TVR to 0.1% for U-Sleep and the paper-wide range is 0.1–0.8%. If 'rare-transition recall' counts expert-annotated rare transitions recovered at the same transition, the number of true positives cannot exceed the number of predicted rare transitions, so recall ≤ TVR_pred/TVR_expert, i.e., ≤ 5–38% (about 9.5% at TVR 0.2%). A 91% per-transition recall would require predicted TVR ≈ 1.9%, not 0.1–0.8%. The paper must define the recall criterion precisely (per-transition? windowed? per-recording? restricted subset?) and recompute the numbers. This is load-bearing because §5 and the abstract use the 91% figure to support the claim that rare transitions are recoverable rather than blocked; as written, the two claims are mutually exclu
  2. [§3.1, §3.4, §4.5] The TVR reduction is, to a substantial extent, by construction. R is exactly the set penalized in Eq. (4) and constrained in Eq. (6), so a large TVR decrease is guaranteed by the method. The paper's strongest non-circular evidence is the downstream sleep-architecture and subgroup analysis (§4.6), and the abstract should say this explicitly rather than presenting low TVR as independent physiological validation. Relatedly, expert-scored hypnograms are reported to have TVR ≈ 2.1%; StageGuard outputs at 0.1–0.8% are below the gold-standard rate. Since expert labels are used as ground truth for accuracy, the choice of 0.1–0.8% as the 'physiologically plausible' target needs a physiological or measurement-noise justification, not just comparison with the unconstrained baseline.
  3. [Table 4, §4.6] The subgroup-difference recovery claim is load-bearing for the abstract but lacks uncertainty quantification. Table 4 reports only point estimates of Δ and Cohen's d for subgroups with n = 5–12 (e.g., No-OSA vs. Mod/Sev OSA), with no confidence intervals, bootstrap, or significance tests. Without such information, the claim that StageGuard 'recovers the direction and effect size of expert-defined subgroup differences more faithfully' is not assessable. Please add intervals or paired tests on the subgroup Δ values, or temper the claim to 'point estimates move toward expert contrasts'.
minor comments (5)
  1. [§4.1 / Abstract] The abstract says TVR is reduced across 'four datasets', but Sleep-Accel is a two-state dataset with no rare transitions and TVR is undefined ('—' in Table 3). The TVR-reduction claim should be explicitly limited to the three-state datasets.
  2. [§4.5] The expert TVR reference of 2.1% is given as a single number, but the datasets differ in species (mouse vs. human), epoch length, and scoring protocols. Please report the expert TVR per dataset to make the comparison unambiguous.
  3. [§3.4, Eq. (6)] For non-rare transitions the decoder uses log ε_ss' with ε_ss' = empirical transition probability from training labels. If any 'typical' transition has zero count in training data, log 0 = -∞ would silently forbid it. Please state how zero-count non-rare transitions are handled.
  4. [Abstract / §4.6] The 59–79% improvement figure comes from Table 5, which reports a single backbone–dataset pair in the main text; the appendix supports it. The abstract should either cite the consolidated appendix table or phrase the claim as representative across pairs.
  5. [Figure 2] Figure 2(a) appears to plot a continuous line over discrete bout-duration counts; bar charts or step plots would be more appropriate and would avoid implying fractional epoch durations.

Circularity Check

2 steps flagged

TVR reduction and the largest downstream-architecture gains are encoded in the loss/decoder, making the core validity claims partly by construction; the 91% rare-transition recall statement is numerically incompatible with the reported TVR.

specific steps
  1. self definitional [§3.3–3.4, Eq. (4) and Eq. (6); §4.5, Table 3]
    "For rare transitions (e.g., Wake→REM), we set ε_ss′ = 0.001—chosen to be small relative to typical transition probabilities (π̂_ss′≈0.1–0.3) while remaining numerically stable—strongly discouraging but not prohibiting them."

    TVR (Eq. 1) counts adjacent-epoch pairs in R. The training loss Eq. (4) sums p(y_{t-1}=s)p(y_t=s') over exactly the same R, and the decoder Eq. (6) adds log ε=log 0.001 for exactly the same R. Therefore the reported drop in TVR from 3.6–12.3% to 0.1–0.8% is the direct result of the objective being optimized. Interpreting low TVR as evidence of 'physiological plausibility' is a definitional consequence of building the method to suppress R; it does not independently confirm that R transitions are abnormal or that ε=0.001 is physiologically correct.

  2. fitted input called prediction [§3.5, Eq. (6); §4.6, Table 5]
    "Each state s has a minimum bout duration d_min(s) enforced via Equation (6): ... EEG/EMG (4-sec epochs): d_min(NREM)=3, d_min(REM)=2, d_min(Wake)=2 ... Crucially, these endpoints are not directly optimized by the soft penalty or the constrained decoder, so the consistent improvement is downstream of constraint satisfaction rather than the result of optimizing toward them."

    The largest Table 5 gains are on Mean NREM/REM Bout (74–75%) and Awakenings (79%), which are deterministic functions of run lengths and transition counts. Eq. (6) hard-enforces d_min(NREM)=3, d_min(REM)=2, d_min(Wake)=2 for 4-s epochs, eliminating the single-epoch bouts and repeated Wake entries that create baseline error; Eq. (8) further penalizes re-entering a state within k epochs. Thus the endpoints are not literally in the loss but are mechanically constrained by the same prior, so their improvement is not independent evidence of physiological validity.

full rationale

StageGuard's central validity metric is self-referential: TVR counts R transitions, and both the soft penalty (Eq. 4) and decoder (Eq. 6) are explicitly built to suppress exactly R, so the TVR reduction in Table 3 is a check that the optimizer minimized its own objective rather than an independent physiological finding. Likewise, the headline 59–79% downstream improvements concentrate on bout-duration and awakening statistics that are direct readouts of the duration/transition constraints hard-coded in Eq. (6) and Eq. (8); calling them 'not directly optimized' is true only in a narrow technical sense. Independent empirical content does exist: accuracy is maintained or improved across six backbones, and the SHHS subgroup directions (Table 4) are recovered rather than merely smoothed; there are no load-bearing self-citations. However, a separate numerical inconsistency removes the paper's main defense against over-constraining: §5 claims ≥91% recall of expert-annotated rare transitions, but with expert TVR ≈ 2.1% and StageGuard TVR 0.1–0.8%, per-transition recall cannot exceed about 4.8–38% (recall ≤ TVR_pred/TVR_expert). Either the recall is not per-transition (contradicting the text) or StageGuard deletes most rare transitions, in which case the low TVR and downstream gains are artifacts of smoothing. Section 6.1 honestly limits the priors to healthy adults, but that limitation is a scope statement, not a fix for the construction-level circularity. Overall: partial circularity, score 6.

Axiom & Free-Parameter Ledger

7 free parameters · 5 axioms · 0 invented entities

StageGuard introduces no new physical entity, force, or conserved quantity. Its central claim rests on hand-set physiological priors (R, d_min, epsilon_rare), a few hand-tuned hyperparameters (lambda, gamma, k, D_max), and empirical transition matrices estimated from training labels. The most consequential inputs are the physiological priors, whose validity for a given cohort is the main load-bearing assumption and is explicitly qualified by the authors in Section 6.1.

free parameters (7)
  • lambda (soft transition penalty weight) = 0.5 (grid-searched over {0.1, 0.25, 0.5, 1.0, 2.0} on a held-out validation fold)
    Controls the strength of the differentiable rare-transition penalty in Eq. 5; selected to reduce TVR while maintaining baseline accuracy.
  • epsilon_rare (rare-transition score in decoder) = 0.001
    Hand-set in Eq. 6 for rare transitions (e.g., Wake->REM, REM->NREM); chosen small relative to empirical transition probabilities (~0.1-0.3) to strongly discourage but not prohibit rare transitions. Directly determines the achieved TVR level.
  • gamma (flip-flop penalty weight) = 2.0
    Hand-set in Eq. 8 and selected alongside lambda on a validation fold; affects FI in ~3.8% of recordings.
  • k (flip-flop history window) = 5 epochs
    Hand-set in Eq. 8 based on the refractory period following state transitions; controls which recently visited states are penalized.
  • d_min(s): minimum bout durations per state and dataset = NREM=3, REM=2, Wake=2 (4-sec epochs); NREM=2, REM=2, Wake=1 (30-sec epochs)
    Hand-set from physiology in Section 3.5; exact minimum dwell times enforced in the augmented state space. Directly shapes bout-duration and fragmentation improvements.
  • D_max (maximum augmented duration state) = 10
    Caps the duration count in the augmented state space; chosen to cover typical bout lengths while keeping decoding complexity O(T * (K*D_max)^2) manageable.
  • pi_hat: empirical transition probabilities from training labels = Per-dataset 3x3 matrices (Table 15)
    Used as epsilon for typical transitions in Eq. 6; estimated from training-set labels, so the decoder inherits training-label noise and cohort-specific transition structure.
axioms (5)
  • domain assumption The rare-transition set R = {Wake->REM, REM->NREM} (plus modality-specific variants) encodes physiologically rare transitions in healthy subjects.
    Used to define TVR (Eq. 1), the training penalty (Eq. 4), and decoder penalties (Eq. 6). The paper's own Section 6.1 says this is not applicable to narcolepsy/RBD/pediatric cohorts.
  • domain assumption Minimum bout durations d_min reflect the temporal organization of sleep states and the underlying state-switching neurobiology.
    Section 3.5; enforcing these is claimed to increase physiological plausibility and reduce fragmentation. If d_min are wrong for a cohort, the decoder over-smooths and suppresses genuine short bouts.
  • standard math Per-epoch emissions are conditionally independent given the state sequence (HMM-style decoding objective in Eq. 7).
    The semi-Markov Viterbi decoder maximizes the sum of per-epoch log emissions plus transition scores; this is the standard structured-prediction/HMM assumption, acknowledged as approximate for sequential backbones in Section 3.3.
  • domain assumption Expert-scored training labels are treated as ground truth for estimating pi_hat and for evaluating downstream sleep-architecture metrics.
    Section 4.4 and 6.2 acknowledge inter-rater reliability of 80-85%; noisy labels propagate into transition priors and into the MAE comparisons against 'expert-derived' values.
  • standard math Viterbi on the augmented state space recovers the global optimum under the static transition/duration scores.
    Section 3.4; true for the static objective. The flip-flop term (Eq. 8) is a greedy deviation that the authors classify as heuristic in Table 2.

reviewed 2026-07-31 · how reviews work

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Cite this review

Pith. "Pith review of StageGuard: Physiologically Constrained Sleep Staging." pith.science (2026). https://pith.science/paper/LEFQG664

@misc{pith2026260723284,
  author       = {Pith},
  title        = {Pith review of: StageGuard: Physiologically Constrained Sleep Staging},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LEFQG664}},
  note         = {Machine review of arXiv:2607.23284}
}
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read the original abstract

Automated sleep staging is increasingly used in large-scale studies to derive sleep-architecture endpoints: total sleep time, REM latency, sleep efficiency, and bout-duration statistics. Deep learning models achieve epoch-level accuracy approaching inter-rater agreement, yet often produce hypnograms that violate physiological invariants, such as rare transitions (e.g., direct Wake -> REM) or excessively fragmented sequences. Such violations can bias downstream sleep metrics, regardless of overall accuracy. We propose StageGuard, a plug-and-play, backbone-agnostic structured-inference framework that wraps any neural sleep-staging backbone with physiology-informed priors. StageGuard combines (1) a differentiable soft transition penalty that discourages physiologically rare transitions during training, and (2) a semi-Markov constrained decoder with a duration-augmented state space that jointly enforces transition penalties and minimum bout durations at inference. Unlike hard-prohibition methods, it admits rare transitions when emission evidence is overwhelming, leaving informative pathological events recoverable rather than blocked. StageGuard constrains staging outputs to satisfy known physiological priors rather than modeling sleep generatively. We quantify the validity gap using transition-violation rate (TVR) and fragmentation index (FI) and demonstrate that, across six backbones and four datasets, StageGuard reduces TVR to physiologically plausible levels and lowers FI by 56-62%, while maintaining or slightly improving classification accuracy. Crucially, improved constraint satisfaction translates into 59-79% lower error on derived sleep-architecture statistics not directly optimized by the method, and recovers the direction and effect size of expert-defined subgroup differences (OSA severity, age) more faithfully than the unconstrained baseline.

Figures

Figures reproduced from arXiv: 2607.23284 by Dongmian Zou, Hao Wu, Jiayu Gao, Juntang Wang, Shixin Xu, Yihan Wang.

Figure 1
Figure 1. Figure 1: Overview of the StageGuard pipeline. Input features from one modality (EEG/EMG, actigraphy, cardiorespiratory, or [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Constraint-effect analysis on AccuSleep Mouse EEG/EMG. (a) Bout durations: StageGuard closely matches the expert [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

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Reference graph

Works this paper leans on

48 extracted references · 13 canonical work pages

  1. [1]

    Frye, Danqian Liu, Yang Dan, and Kristofer E

    Zeke Barger, Charles G. Frye, Danqian Liu, Yang Dan, and Kristofer E. Bouchard

  2. [2]

    Berry, Rita Brooks, Charlene Gamaldo, Susan M

    Richard B. Berry, Rita Brooks, Charlene Gamaldo, Susan M. Harding, Robin M. Lloyd, Stuart F. Quan, Matthew T. Troester, and Bradley V. Vaughn. 2017.The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications, Version 2.4. Darien, IL

    2017

  3. [3]

    Jacob Cohen. 1960. A Coefficient of Agreement for Nominal Scales.Educa- tional and Psychological Measurement20, 1 (April 1960), 37–46. doi:10.1177/ 001316446002000104

    1960

  4. [4]

    Heidi Danker-Hopfe, Peter Anderer, Josef Zeitlhofer, Marion Boeck, Hans Dorn, Georg Gruber, Esther Heller, Erna Loretz, Doris Moser, Silvia Parapatics, Bernd Saletu, Andrea Schmidt, and Georg Dorffner. 2009. Interrater Reliability for Sleep Scoring According to the Rechtschaffen & Kales and the New AASM Standard.Journal of Sleep Research18, 1 (March 2009)...

    arXiv 2009

  5. [5]

    Matthews, and Yike Guo

    Hao Dong, Akara Supratak, Wei Pan, Chao Wu, Paul M. Matthews, and Yike Guo

  6. [6]

    Emadeldeen Eldele, Zhenghua Chen, Chengyu Liu, Min Wu, Chee-Keong Kwoh, Xiaoli Li, and Cuntai Guan. 2021. An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG.IEEE Transactions on Neural Systems and Rehabilitation Engineering29 (2021), 809–818. doi:10.1109/ TNSRE.2021.3076234

    arXiv 2021

  7. [7]

    Bassetti, and Francesca D

    Luigi Fiorillo, Alessandro Puiatti, Michela Papandrea, Pietro-Luca Ratti, Paolo Favaro, Corinne Roth, Panagiotis Bargiotas, Claudio L. Bassetti, and Francesca D. Faraci. 2019. Automated Sleep Scoring: A Review of the Latest Approaches.Sleep Medicine Reviews48 (Dec. 2019), 101204. doi:10.1016/j.smrv.2019.07.007

    doi:10.1016/j.smrv.2019.07.007 2019

  8. [8]

    Pedro Fonseca, Xi Long, Mustafa Radha, Reinder Haakma, Ronald M Aarts, and Jérôme Rolink. 2015. Sleep Stage Classification with ECG and Respiratory Effort.Physiological Measurement36, 10 (Oct. 2015), 2027–2040. doi:10.1088/0967- 3334/36/10/2027

    doi:10.1088/0967- 2015

  9. [9]

    G.D. Forney. 1973. The Viterbi Algorithm.Proc. IEEE61, 3 (1973), 268–278. doi:10.1109/PROC.1973.9030

    arXiv 1973

  10. [10]

    Franken, D

    P. Franken, D. J. Dijk, I. Tobler, and A. A. Borbely. 1991. Sleep Deprivation in Rats: Effects on EEG Power Spectra, Vigilance States, and Cortical Temperature.Amer- ican Journal of Physiology-Regulatory, Integrative and Comparative Physiology 261, 1 (July 1991), R198–R208. doi:10.1152/ajpregu.1991.261.1.R198

    work page doi:10.1152/ajpregu.1991.261.1.r198 1991

  11. [11]

    Kuzman Ganchev, João Graça, Jennifer Gillenwater, and Ben Taskar. 2010. Poste- rior Regularization for Structured Latent Variable Models.J. Mach. Learn. Res.11 (Aug. 2010), 2001–2049

    2010

  12. [12]

    During, and Valentin Thorey

    Antoine Guillot, Fabien Sauvet, Emmanuel H. During, and Valentin Thorey. 2020. Dreem Open Datasets: Multi-Scored Sleep Datasets to Compare Human and Automated Sleep Staging.IEEE Transactions on Neural Systems and Rehabilitation Engineering28, 9 (Sept. 2020), 1955–1965. doi:10.1109/TNSRE.2020.3011181

    arXiv 2020

  13. [13]

    Chris Hokamp and Qun Liu. 2017. Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search. InProceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Vancouver, Canada, 1535–1546. doi:10.18653/v1/ P17-1141

    doi:10.18653/v1/ 2017

  14. [14]

    Wei-Bang Jiang, Li-Ming Zhao, and Bao-Liang Lu. 2024. Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI. In Proceedings of the 12th International Conference on Learning Representations

    2024

  15. [15]

    Faghmous, Michael Steinbach, Arindam Banerjee, Auroop Ganguly, Shashi Shekhar, Nagiza Samatova, and Vipin Kumar

    Anuj Karpatne, Gowtham Atluri, James H. Faghmous, Michael Steinbach, Arindam Banerjee, Auroop Ganguly, Shashi Shekhar, Nagiza Samatova, and Vipin Kumar. 2017. Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data.IEEE Transactions on Knowledge and Data Engineering29, 10 (Oct. 2017), 2318–2331. doi:10.1109/TKDE.2017.2720168

    arXiv 2017

  16. [16]

    Koley and D

    B. Koley and D. Dey. 2012. An Ensemble System for Automatic Sleep Stage Classification Using Single Channel EEG Signal.Computers in Biology and Medicine42, 12 (Dec. 2012), 1186–1195. doi:10.1016/j.compbiomed.2012.09.012

    work page doi:10.1016/j.compbiomed.2012.09.012 2012

  17. [17]

    Lafferty, Andrew McCallum, and Fernando C

    John D. Lafferty, Andrew McCallum, and Fernando C. N. Pereira. 2001. Con- ditional Random Fields: Probabilistic Models for Segmenting and Labeling Se- quence Data. InProceedings of the Eighteenth International Conference on Machine Learning (ICML ’01). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 282–289

    2001

  18. [18]

    Andrew S. P. Lim, Matthew Kowgier, Lei Yu, Aron S. Buchman, and David A. Bennett. 2013. Sleep Fragmentation and the Risk of Incident Alzheimer’s Disease and Cognitive Decline in Older Persons.Sleep36, 7 (July 2013), 1027–1032. doi:10.5665/sleep.2802

    work page doi:10.5665/sleep.2802 2013

  19. [19]

    Pierre-Hervé Luppi and Patrice Fort. 2018. Neuroanatomical and Neurochemical Bases of Vigilance States.Handbook of Experimental Pharmacology253 (2018), 35–58. doi:10.1007/164_2017_84

    work page doi:10.1007/164_2017_84 2018

  20. [20]

    Shing-Tai Pan, Chih-En Kuo, Jian-Hong Zeng, and Sheng-Fu Liang. 2012. A Transition-Constrained Discrete Hidden Markov Model for Automatic Sleep Staging.BioMedical Engineering OnLine11, 1 (Dec. 2012), 52. doi:10.1186/1475- 925X-11-52

    work page doi:10.1186/1475- 2012

  21. [21]

    Penzel, J

    T. Penzel, J. McNames, P. De Chazal, B. Raymond, A. Murray, and G. Moody

  22. [22]

    Mathias Perslev, Sune Darkner, Lykke Kempfner, Miki Nikolic, Poul Jørgen Jen- num, and Christian Igel. 2021. U-Sleep: Resilient High-Frequency Sleep Staging. npj Digital Medicine4, 1 (April 2021), 72. doi:10.1038/s41746-021-00440-5

    doi:10.1038/s41746-021-00440-5 2021

  23. [23]

    Chen, and Maarten De Vos

    Huy Phan, Fernando Andreotti, Navin Cooray, Oliver Y. Chen, and Maarten De Vos. 2019. SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging.IEEE Transactions on Neural Systems and Rehabilitation Engineering27, 3 (March 2019), 400–410. doi:10.1109/ TNSRE.2019.2896659

    arXiv 2019

  24. [24]

    Chen, Philipp Koch, Alfred Mertins, and Maarten De Vos

    Huy Phan, Kaare Mikkelsen, Oliver Y. Chen, Philipp Koch, Alfred Mertins, and Maarten De Vos. 2022. SleepTransformer: Automatic Sleep Staging With In- terpretability and Uncertainty Quantification.IEEE Transactions on Biomedical Engineering69, 8 (Aug. 2022), 2456–2467. doi:10.1109/TBME.2022.3147187

    arXiv 2022

  25. [25]

    Matt Post and David Vilar. 2018. Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation. InProceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). Association for Computational Linguistics, New Orleans, L...

    2018

  26. [26]

    S. F. Quan, B. V. Howard, C. Iber, J. P. Kiley, F. J. Nieto, G. T. O’Connor, D. M. Rapoport, S. Redline, J. Robbins, J. M. Samet, and P. W. Wahl. 1997. The Sleep Heart Health Study: Design, Rationale, and Methods.Sleep20, 12 (Dec. 1997), 1077–1085

    1997

  27. [27]

    Allan Rechtschaffen and Anthony Kales. 1968. A Manual of Standardized Ter- minology, Techniques and Scoring System for Sleep Stages of Human Subjects. (1968)

    1968

  28. [28]

    Cynthia Rudin. 2019. Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead.Nature Machine Intelligence1, 5 (May 2019), 206–215. doi:10.1038/s42256-019-0048-x

    doi:10.1038/s42256-019-0048-x 2019

  29. [29]

    Avi Sadeh. 2011. The Role and Validity of Actigraphy in Sleep Medicine: An Update.Sleep Medicine Reviews15, 4 (2011), 259–267. doi:10.1016/j.smrv.2010.10. 001

    work page doi:10.1016/j.smrv.2010.10 2011

  30. [30]

    Sharkey, and Mary A

    Avi Sadeh, M. Sharkey, and Mary A. Carskadon. 1994. Activity-Based Sleep-Wake Identification: An Empirical Test of Methodological Issues.Sleep17, 3 (May 1994), 201–207. doi:10.1093/sleep/17.3.201

    work page doi:10.1093/sleep/17.3.201 1994

  31. [31]

    Saper, Thomas E

    Clifford B. Saper, Thomas E. Scammell, and Jun Lu. 2005. Hypothalamic Regula- tion of Sleep and Circadian Rhythms.Nature437, 7063 (Oct. 2005), 1257–1263. doi:10.1038/nature04284

    work page doi:10.1038/nature04284 2005

  32. [32]

    Fred Shaffer and J. P. Ginsberg. 2017. An Overview of Heart Rate Variability Metrics and Norms.Frontiers in Public Health5 (2017), 258. doi:10.3389/fpubh. 2017.00258

    arXiv 2017

  33. [33]

    Arnaud Sors, Stéphane Bonnet, Sébastien Mirek, Laurent Vercueil, and Jean- François Payen. 2018. A Convolutional Neural Network for Sleep Stage Scoring from Raw Single-Channel EEG.Biomedical Signal Processing and Control42 (April 2018), 107–114. doi:10.1016/j.bspc.2017.12.001

    work page doi:10.1016/j.bspc.2017.12.001 2018

  34. [34]

    Stephansen, Alexander N

    Jens B. Stephansen, Alexander N. Olesen, Mads Olsen, Aditya Ambati, Eileen B. Leary, Hyatt E. Moore, Oscar Carrillo, Ling Lin, Fang Han, Han Yan, Yun L. Sun, Yves Dauvilliers, Sabine Scholz, Lucie Barateau, Birgit Hogl, Ambra Stefani, Seung Chul Hong, Tae Won Kim, Fabio Pizza, Giuseppe Plazzi, Stefano Vandi, Elena Antelmi, Dimitri Perrin, Samuel T. Kuna, ...

  35. [35]

    Akara Supratak, Hao Dong, Chao Wu, and Yike Guo. 2017. DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering25, 11 (Nov. 2017), 1998–2008. arXiv:1703.04046 [stat] doi:10.1109/TNSRE.2017.2721116

    Pith/arXiv arXiv 2017

  36. [36]

    Alexander Tataraidze, Lesya Anishchenko, Lyudmila Korostovtseva, Bert Jan Kooij, Mikhail Bochkarev, and Yurii Sviryaev. 2015. Sleep Stage Classification Based on Bioradiolocation Signals.2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)2015 (Aug. 2015), 362–365. doi:10.1109/EMBC.2015.7318374

    arXiv 2015

  37. [37]

    Matthews, and Yike Guo

    Orestis Tsinalis, Paul M. Matthews, and Yike Guo. 2016. Automatic Sleep Stage Scoring Using Time-Frequency Analysis and Stacked Sparse Autoencoders.An- nals of Biomedical Engineering44, 5 (May 2016), 1587–1597. doi:10.1007/s10439- 015-1444-y

    doi:10.1007/s10439- 2016

  38. [38]

    2018), 5229

    Neural Network Analysis of Sleep Stages Enables Efficient Diagnosis of Narcolepsy.Nature Communications9, 1 (Dec. 2018), 5229. doi:10.1038/s41467- 018-07229-3

    doi:10.1038/s41467- 2018

  39. [39]

    Olivia Walch, Yitong Huang, Daniel Forger, and Cathy Goldstein. 2019. Sleep Stage Prediction with Raw Acceleration and Photoplethysmography Heart Rate Data Derived from a Consumer Wearable Device.Sleep42, 12 (Dec. 2019), zsz180. doi:10.1093/sleep/zsz180

    work page doi:10.1093/sleep/zsz180 2019

  40. [40]

    Jared Willard, Xiaowei Jia, Shaoming Xu, Michael Steinbach, and Vipin Kumar

  41. [41]

    R Wolk, A Gami, A Garcia-Touchard, and V Somers. 2005. Sleep and Cardio- vascular Disease.Current Problems in Cardiology30, 12 (Dec. 2005), 625–662. doi:10.1016/j.cpcardiol.2005.07.002

    work page doi:10.1016/j.cpcardiol.2005.07.002 2005

  42. [42]

    Van Hees, Séverine Sabia, Kirstie N

    Vincent T. Van Hees, Séverine Sabia, Kirstie N. Anderson, Sarah J. Denton, James Oliver, Michael Catt, Jessica G. Abell, Mika Kivimäki, Michael I. Trenell, and Archana Singh-Manoux. 2015. A Novel, Open Access Method to Assess Sleep Duration Using a Wrist-Worn Accelerometer.PLOS ONE10, 11 (Nov. 2015), StageGuard: Physiologically Constrained Sleep Staging K...

    work page doi:10.1371/journal.pone.0142533 2015

  43. [43]

    Shun-Zheng Yu. 2010. Hidden Semi-Markov Models.Artificial Intelligence174, 2 (Feb. 2010), 215–243. doi:10.1016/j.artint.2009.11.011 A Robustness Analysis Constraint relaxation.Table 8 shows the effect of relaxing the rare-transition penalties (increasing𝜖) on AccuSleep Mouse EEG/EMG using U-Sleep. With full constraints (𝜖= 0.001), TVR is 0.2% and REM late...

    doi:10.1016/j.artint.2009.11.011 2010

  44. [47]

    Edward A. Wolpert. 1969. A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects.Archives of General Psychiatry20, 2 (Feb. 1969), 246. doi:10.1001/archpsyc.1969.01740140118016

    arXiv 1969

  45. [2002]

    doi:10.1007/BF02345072

    Systematic Comparison of Different Algorithms for Apnoea Detection Based on Electrocardiogram Recordings.Medical and Biological Engineering and Computing40, 4 (July 2002), 402–407. doi:10.1007/BF02345072

    work page doi:10.1007/bf02345072 2002

  46. [2018]

    IEEE Transactions on Neural Systems and Rehabilitation Engineering26, 2 (Feb

    Mixed Neural Network Approach for Temporal Sleep Stage Classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering26, 2 (Feb. 2018), 324–333. doi:10.1109/TNSRE.2017.2733220

    arXiv 2018

  47. [2019]

    2019), e0224642

    Robust, Automated Sleep Scoring by a Compact Neural Network with Distributional Shift Correction.PLOS ONE14, 12 (Dec. 2019), e0224642. doi:10. 1371/journal.pone.0224642

    2019

  48. [2023]

    Surveys55, 4 (April 2023), 1–37

    Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems.Comput. Surveys55, 4 (April 2023), 1–37. doi:10. 1145/3514228

    2023

This paper was first reviewed by deepseek-v4-flash on July 31, 2026.