Paper Citation Record · LEDGER

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging

As of 21 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2607.19441.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.19441 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T14:10:00.694480Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

36 of 36 outbound references displayed

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External citation measurements

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Outbound references

Observation a370f40a-608a-4cd3-aa98-bf5b297d6153 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 1

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Observation bff274ed-813e-4e4d-bf9d-1f84309f2165 · outbound

This paper cites Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Reference 2

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Observation 2a7d7aef-e8e1-4f1d-886e-facec6a5c61b · outbound

This paper cites Harnessing electroencephalography con- nectomes for cognitive and clinical neuroscience,.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging Harnessing electroencephalography con- nectomes for cognitive and clinical neuroscience,

Reference 3

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Observation 9ca22c34-2dab-4684-b37b-6e4453587cd4 · outbound

This paper cites Machine learning prediction on spatial and environmental perception and work efficiency using electroencephalography including cross-subject scenarios,.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging Machine learning prediction on spatial and environmental perception and work efficiency using electroencephalography including cross-subject scenarios,

Reference 4

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Observation 4050f728-43b1-4356-80fe-01c810007d5b · outbound

This paper cites Electrooculography dataset for objective spatial naviga- tion assessment in healthy participants,.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging Electrooculography dataset for objective spatial naviga- tion assessment in healthy participants,

Reference 5

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Observation 5aeae8ed-c886-4f85-a178-38efdcf14788 · outbound

This paper cites Comprehensive human locomotion and electromyography dataset: Gait120,.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging Comprehensive human locomotion and electromyography dataset: Gait120,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T14:09:59.480612Z

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Observation 7b5872ce-2cd6-49f8-a4cd-c9365b2894f2 · outbound

This paper cites From pose to muscle: Multimodal learning for piano hand muscle electromyography,.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging From pose to muscle: Multimodal learning for piano hand muscle electromyography,

Reference 7

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Observation c12b488e-6f3a-42dc-bef8-c0f2bb9be288 · outbound

This paper cites Polysomnography in transition: Reassessing its role in the future of sleep medicine,.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging Polysomnography in transition: Reassessing its role in the future of sleep medicine,

Reference 8

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Observation 77e0cdc2-8a4a-4e49-99d9-c122511d29e8 · outbound

This paper cites Severity classification of obstructive sleep apnea using aasm and separ criteria: A cross-sectional reclassification analysis,.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging Severity classification of obstructive sleep apnea using aasm and separ criteria: A cross-sectional reclassification analysis,

Reference 9

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Observation bbf7c823-1ccb-4ee7-88a3-f446b89ebc82 · outbound

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How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging Unresolved cited work

Reference 10

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Observation 122ddf3f-fb7a-45c2-88e1-ed59be7d8bc5 · outbound

This paper cites Deepsleepnet: A model for automatic sleep stage scoring based on raw single-channel eeg,.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging Deepsleepnet: A model for automatic sleep stage scoring based on raw single-channel eeg,

Reference 11

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Observation 5747948f-1322-411e-b068-790de66db4ac · outbound

This paper cites U-sleep: resilient high-frequency sleep staging,.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging U-sleep: resilient high-frequency sleep staging,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T14:09:59.682457Z

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Observation 7b96d0a2-f755-4799-9486-d745c96d2662 · outbound

This paper cites XSleepNet: Multi-view sequential model for automatic sleep staging,.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging XSleepNet: Multi-view sequential model for automatic sleep staging,

Reference 13

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Observation 169e6a04-47cb-4def-a83d-ba8acf76330d · outbound

This paper cites Privacy in consumer wearable technologies: a living systematic analysis of data policies across leading manufacturers,.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging Privacy in consumer wearable technologies: a living systematic analysis of data policies across leading manufacturers,

Reference 14

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Observation 40be8ec9-eefb-4015-95b3-ec436840ff27 · outbound

This paper cites Heart rate variability in normal and pathological sleep,.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging Heart rate variability in normal and pathological sleep,

Reference 15

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Observation 3e09dfbe-5d25-4e70-a5ec-7a97bd98d972 · outbound

This paper cites Autonomic activity during human sleep as a function of time and sleep stage,.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging Autonomic activity during human sleep as a function of time and sleep stage,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T14:09:59.861540Z

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Observation 524004e8-336f-4efd-8330-a46526bebe63 · outbound

This paper cites Deep learning for automated sleep staging using instantaneous heart rate,.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging Deep learning for automated sleep staging using instantaneous heart rate,

Reference 17

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Observation c15641ef-1630-43f9-8b85-b2da9e256efa · outbound

This paper cites Sleep stage classification from heart-rate variability using long short-term memory neural networks,.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging Sleep stage classification from heart-rate variability using long short-term memory neural networks,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T14:09:59.943958Z

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Observation ecc04da4-1a5a-4b31-a39f-790ba2455f79 · outbound

This paper cites Sleep Staging from Electrocardiography and Respiration with Deep Learning.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging Sleep Staging from Electrocardiography and Respiration with Deep Learning

Reference 19

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Observation ca149291-29a0-43cf-b00a-0778cc4cc23a · outbound

This paper cites Tinysleepnet: An efficient deep learning model for sleep stage scoring based on raw single-channel eeg,.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging Tinysleepnet: An efficient deep learning model for sleep stage scoring based on raw single-channel eeg,

Reference 20

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Observation 93fe8654-ee59-4d76-9a7b-4d6faf51114e · outbound

This paper cites SleepTransformer: automatic sleep staging with interpretability and un- certainty quantification,.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging SleepTransformer: automatic sleep staging with interpretability and un- certainty quantification,

Reference 21

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Observation fdeca819-1f6f-4245-8f05-a9d99a41c1b2 · outbound

This paper cites A systematic review of sensing technologies for wearable sleep staging,.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging A systematic review of sensing technologies for wearable sleep staging,

Reference 22

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Observation 5af647ea-89f2-4613-bcf4-2a1ec24ef295 · outbound

This paper cites Sleep stage prediction with raw acceleration and photoplethysmography heart rate data derived from a consumer wearable device,.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging Sleep stage prediction with raw acceleration and photoplethysmography heart rate data derived from a consumer wearable device,

Reference 23

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Observation f86299aa-e02f-43b7-bdea-ddd22d6ce7ee · outbound

This paper cites Performance of seven consumer sleep-tracking devices compared with polysomnography,.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging Performance of seven consumer sleep-tracking devices compared with polysomnography,

Reference 24

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Observation efcf347e-0c9c-4ea0-b902-bc2525f4c3fc · outbound

This paper cites The promise of sleep: A multi-sensor approach for accurate sleep stage detection using the Oura ring,.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging The promise of sleep: A multi-sensor approach for accurate sleep stage detection using the Oura ring,

Reference 25

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Observation 0879387e-aa8e-4b07-9fb7-d9dff558eb38 · outbound

This paper cites An evaluation of cardiorespiratory and movement features with respect to sleep-stage classification,.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging An evaluation of cardiorespiratory and movement features with respect to sleep-stage classification,

Reference 26

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Observation bc025ce9-b65a-4283-b624-6369e51d4d59 · outbound

This paper cites SleepPPG-Net: a deep learning algorithm for robust sleep staging from continuous photoplethysmography.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging SleepPPG-Net: a deep learning algorithm for robust sleep staging from continuous photoplethysmography

Reference 27

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Observation 4bf8a6a4-859b-4c73-ae48-784ae977e560 · outbound

This paper cites The sleep heart health study: design, rationale, and methods,.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging The sleep heart health study: design, rationale, and methods,

Reference 28

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Observation 7ec3e289-1ccb-4739-b3de-9c7e36d94dbf · outbound

This paper cites The National Sleep Research Resource: towards a sleep data commons,.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging The National Sleep Research Resource: towards a sleep data commons,

Reference 29

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Observation d7211403-1137-413a-aad1-5deed63cfce1 · outbound

This paper cites Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG,.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG,

Reference 30

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Observation 4b697737-8a03-49a0-a45e-6e5fcbcc4ad8 · outbound

This paper cites PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals,.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals,

Reference 31

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This paper cites A real-time QRS detection algorithm,.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging A real-time QRS detection algorithm,

Reference 32

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This paper cites Error bounds for convolutional codes and an asymptoti- cally optimum decoding algorithm,.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging Error bounds for convolutional codes and an asymptoti- cally optimum decoding algorithm,

Reference 33

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Observation a95e0e0f-b855-498f-9d7a-20662bb8eab4 · outbound

This paper cites A coefficient of agreement for nominal scales,.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging A coefficient of agreement for nominal scales,

Reference 34

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Observation 0e3267ee-926a-40a7-b8e7-39dacfce2be0 · outbound

This paper cites Consumer sleep technology: An american academy of sleep medicine position statement,.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging Consumer sleep technology: An american academy of sleep medicine position statement,

Reference 35

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Observation 60692886-7278-4b5d-8d42-df2eec9bc174 · outbound

This paper cites Wearable technologies for developing sleep and circadian biomarkers: a summary of workshop discussions,.

How Far Can Wearable-Compatible Signals Go? A Controlled Decomposition of Non-EEG Sleep Staging Wearable technologies for developing sleep and circadian biomarkers: a summary of workshop discussions,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-01T14:10:00.694480Z

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Pith citing papers

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