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Development and Validation of a Multimodal Deep Learning Model Using Peripheral Oxygen Saturation and Ballistocardiography Signals for Predicting Apnea-Hypopnea Index in Obstructive Sleep Apnea

3. november 2026 opdateret af: Ke-Yun, Chao, Fu Jen Catholic University

A Prospective Observational Study to Develop and Validate a Multimodal Deep Learning Model Integrating Peripheral Oxygen Saturation and Ballistocardiography Signals for the Prediction of Apnea-Hypopnea Index in Patients With Suspected Obstructive Sleep Apnea

Obstructive sleep apnea (OSA) is a common sleep disorder associated with cardiovascular, metabolic, and neurocognitive complications. The standard diagnostic test for OSA is in-laboratory polysomnography (PSG), which can be costly, labor-intensive, and inconvenient for repeated or large-scale screening. Therefore, there is a need for a convenient and non-invasive method to help identify individuals who may be at risk for OSA.

This prospective observational study aims to develop and clinically validate a multimodal deep learning model that integrates peripheral oxygen saturation (SpO₂) signals and ballistocardiography (BCG) data collected using a smart mattress system to estimate the apnea-hypopnea index (AHI). Adult participants undergoing routine overnight PSG for suspected OSA will be enrolled. During sleep, synchronized SpO₂ and BCG signals will be recorded non-invasively.

The performance of the deep learning model will be evaluated by comparing the predicted AHI values with AHI values derived from PSG. This study seeks to evaluate whether this practical, non-invasive, and scalable approach may support early identification of OSA risk and improve access to sleep disorder assessment in both clinical and home-based settings.

Studieoversigt

Status

Rekruttering

Betingelser

Detaljeret beskrivelse

This is a prospective observational cohort study designed to develop and validate a multimodal deep learning-based model for estimating obstructive sleep apnea (OSA) severity. Adult participants with suspected OSA who are scheduled to undergo overnight in-laboratory polysomnography (PSG) will be recruited at Fu Jen Catholic University Hospital.

During the PSG examination, participants will simultaneously undergo non-invasive physiological signal acquisition using a smart mattress system equipped with ballistocardiography (BCG) sensors, along with continuous peripheral oxygen saturation (SpO₂) monitoring. These signals will be time-synchronized with PSG recordings. No experimental intervention or alteration to standard clinical care will be performed.

Multimodal deep learning algorithms incorporating convolutional neural networks and sequence-based architectures will be developed to analyze SpO₂ and BCG signals and to estimate the apnea-hypopnea index (AHI). Model performance will be assessed by comparing predicted AHI values with PSG-derived AHI using predefined statistical metrics.

The primary objective is to evaluate the accuracy of AHI prediction using the proposed multimodal model. Secondary objectives include assessment of model discrimination for OSA severity classification and exploratory analyses of physiological signal features associated with sleep-disordered breathing. All analyses will be conducted using de-identified data. This study does not involve U.S. FDA-regulated drugs or devices and is not conducted under an investigational new drug application or investigational device exemption.

Undersøgelsestype

Observationel

Tilmelding (Anslået)

100

Kontakter og lokationer

Dette afsnit indeholder kontaktoplysninger for dem, der udfører undersøgelsen, og oplysninger om, hvor denne undersøgelse udføres.

Studiekontakt

Studiesteder

    • New Taipei City
      • New Taipei City, New Taipei City, Taiwan, 24352
        • Rekruttering
        • Fu Jen Catholic University Hospital
        • Kontakt:
          • Clinical Research Coordinator
          • Telefonnummer: +886-2-2905-2000
          • E-mail: Ck_qq@hotmail.com

Deltagelseskriterier

Forskere leder efter personer, der passer til en bestemt beskrivelse, kaldet berettigelseskriterier. Nogle eksempler på disse kriterier er en persons generelle helbredstilstand eller tidligere behandlinger.

Berettigelseskriterier

Aldre berettiget til at studere

20 år og ældre (Voksen, Ældre voksen)

Tager imod sunde frivillige

Ja

Køn, der er berettiget til at studere

Alle

Prøveudtagningsmetode

Ikke-sandsynlighedsprøve

Studiebefolkning

Adult individuals aged 20 years or older who are referred to Fu Jen Catholic University Hospital for in-laboratory polysomnography due to suspected obstructive sleep apnea will be invited to participate. The study population includes participants with a wide range of apnea-hypopnea index values, including individuals without obstructive sleep apnea based on polysomnography results.

Beskrivelse

Inclusion Criteria:

  • Adults aged 20 years or older.
  • Individuals referred for in-laboratory polysomnography due to suspected obstructive sleep apnea.
  • Able and willing to provide informed consent.

Exclusion Criteria:

  • Inability to provide informed consent.
  • Presence of conditions that may interfere with sleep signal acquisition, as determined by the investigator.
  • Prior diagnosis of sleep disorders other than obstructive sleep apnea that could confound study results.

Studieplan

Dette afsnit indeholder detaljer om studieplanen, herunder hvordan undersøgelsen er designet, og hvad undersøgelsen måler.

Hvordan er undersøgelsen tilrettelagt?

Design detaljer

  • Observationsmodeller: COHORT
  • Tidsperspektiver: PROSPECTIVE

Kohorter og interventioner

Gruppe / kohorte
Adults With Suspected Obstructive Sleep Apnea

This cohort includes adult participants with suspected obstructive sleep apnea who are scheduled to undergo overnight in-laboratory polysomnography. All participants will receive standard clinical polysomnography while simultaneously undergoing non-invasive physiological signal acquisition using a smart mattress system and peripheral oxygen saturation monitoring. No experimental intervention or treatment assignment is performed.

Hvad måler undersøgelsen?

Primære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
Mean absolute error of apnea-hypopnea index estimation
Tidsramme: Two overnight assessments per participant: one in-laboratory polysomnography night and one subsequent home sleep monitoring night.

Mean absolute error (MAE) will be calculated as the average absolute difference between the apnea-hypopnea index (AHI) predicted by the multimodal deep learning model using peripheral oxygen saturation (SpO2) and ballistocardiography (BCG) signals collected during home sleep monitoring and the AHI measured by in-laboratory polysomnography (PSG). The analysis will include participants with evaluable paired PSG and home monitoring data. MAE will be reported in events per hour, with lower values indicating better predictive performance.

Two overnight assessments per participant: one in-laboratory polysomnography night and one subsequent home sleep monitoring night.

Samarbejdspartnere og efterforskere

Det er her, du vil finde personer og organisationer, der er involveret i denne undersøgelse.

Sponsor

Samarbejdspartnere

Datoer for undersøgelser

Disse datoer sporer fremskridtene for indsendelser af undersøgelsesrekord og resumeresultater til ClinicalTrials.gov. Studieregistreringer og rapporterede resultater gennemgås af National Library of Medicine (NLM) for at sikre, at de opfylder specifikke kvalitetskontrolstandarder, før de offentliggøres på den offentlige hjemmeside.

Studer store datoer

Studiestart (Faktiske)

1. marts 2026

Primær færdiggørelse (Anslået)

30. juni 2028

Studieafslutning (Anslået)

30. juni 2028

Datoer for studieregistrering

Først indsendt

9. februar 2026

Først indsendt, der opfyldte QC-kriterier

3. november 2026

Først opslået (Faktiske)

6. november 2026

Opdateringer af undersøgelsesjournaler

Sidste opdatering sendt (Faktiske)

6. november 2026

Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier

3. november 2026

Sidst verificeret

1. marts 2026

Mere information

Begreber relateret til denne undersøgelse

Andre undersøgelses-id-numre

  • FJUH115580

Plan for individuelle deltagerdata (IPD)

Planlægger du at dele individuelle deltagerdata (IPD)?

INGEN

IPD-planbeskrivelse

Individual participant data will not be shared publicly.

Lægemiddel- og udstyrsoplysninger, undersøgelsesdokumenter

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Ingen

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Ingen

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