Evidence map›Paper›PMID 41563493›Full record

ArticleLung2026

Oxygen Saturation Sample Entropy, a Novel Diagnostic Tool in Sleep Disordered Breathing.

Amar J Shah, Nawal Alotaibi, Maggie Cheung, Rodanthe Nixon, Eshrina Gosal, Anita Saigal, John R Hurst, Ali R Mani, Swapna Mandal

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Lung, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Decrypting network physiology for clinical practice.Frontiers in network physiology · 2026
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Amar J ShahRespiratory, TB and Sleep Innovations Lab, UCL Respiratory, University College London, London, UK.
Nawal AlotaibiRoyal Free London NHS Foundation Trust, Pond Street, London, NW3 2QG, UK.
Maggie CheungRoyal Free London NHS Foundation Trust, Pond Street, London, NW3 2QG, UK.
Rodanthe NixonRoyal Free London NHS Foundation Trust, Pond Street, London, NW3 2QG, UK.
Eshrina GosalRoyal Free London NHS Foundation Trust, Pond Street, London, NW3 2QG, UK.
Anita SaigalRespiratory, TB and Sleep Innovations Lab, UCL Respiratory, University College London, London, UK.
John R HurstRespiratory, TB and Sleep Innovations Lab, UCL Respiratory, University College London, London, UK.
Ali R ManiNetwork Physiology Laboratory, Division of Medicine, University College London, London, UK.
Swapna MandalRespiratory, TB and Sleep Innovations Lab, UCL Respiratory, University College London, London, UK. swapnamandal@nhs.net.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo assess whether a Network Physiology approach using Sample Entropy and Transfer Entropy can be applied to simple physiological signals obtained during sleep studies, to accurately distinguish between different types of sleep disordered breathing (SDB) and streamline the diagnostic process.

methodsRetrospective study on patients who underwent a sleep study between January 2017 and December 2021. The training dataset, used for algorithm development, included four clinically important groups: normal, obstructive sleep apnoea alone, sustained nocturnal hypoxemia with a high AHI (≥ 30/hr) and sustained nocturnal hypoxemia with a low AHI (< 30/hr). Mean, standard deviation, Sample Entropy and Transfer Entropy was calculated for heart rate, respiratory rate, oxygen saturation and nasal flow for each patient. Sample entropy is a measure of signal complexity. This was validated in a separate test dataset. ROC analysis was used.

resultsIn the training dataset (n = 105), the Sample Entropy of the oxygen saturation signal was significantly different in patients with SDB compared to normal studies. The area under a ROC curve for predicting obstructive sleep apnoea from normal studies and sustained hypoxia with a high AHI (≥ 30events/hr) compared to sustained hypoxia with a low AHI (AHI < 30events/hr) was 0.943 and 0.785 respectively. When tested in the test dataset (n = 80), oxygen saturation Sample Entropy above 0.1456 was 100% sensitive and 60% specific in diagnosing obstructive sleep apnoea. Patients with OSA had significantly increased Transfer Entropy from HR → SpO2, RR → SpO2 and NF → SpO2; and significantly decreased Transfer Entropy from SpO2 → RR.

interpretationNetwork Physiology mapping of oxygen saturation can help distinguish between different types of sleep disordered breathing and has the potential to support simplified global diagnostic pathways for sleep apnoea utilising oximetry alone.

Indexed as

OxygenOxygen SaturationSleep Apnea, ObstructiveSleep Apnea SyndromesAdultAgedAlgorithmsEntropyFemaleHeart RateHumansHypoxiaMaleMiddle AgedOximetryPolysomnographyOxygenNon-linear analysisOxygen saturationSample entropySleep disordered breathing

Identifiers

PMID41563493
PMCPMC12823762

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.