Multimodal Deep Learning Model for Predicting the Apnea-Hypopnea Index in Obstructive Sleep

March 3, 2026 updated by: Ke-Yun, Chao, Fu Jen Catholic University

A Multisensor Deep Neural Framework Combining Digital Auscultation, Oxygen Saturation, and Motion Data to Estimate the Apnea-Hypopnea Index in Obstructive Sleep Apnea

This study aims to develop a multimodal deep learning model that integrates noninvasive signals to predict the severity of obstructive sleep apnea. By establishing a clinically viable and user-friendly monitoring tool, the study seeks to enhance early screening accessibility and support the development of home-based sleep care systems.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

Obstructive sleep apnea is a common sleep disorder closely associated with cardiovascular, metabolic, and neuropsychiatric comorbidities. It is characterized by repeated upper airway collapse during sleep, leading to intermittent hypoxia and sleep fragmentation. Although polysomnography remains the diagnostic gold standard for obstructive sleep apnea, its high cost, complexity, and limited accessibility pose challenges for large-scale screening and early identification. Recent advancements in noninvasive sensing technologies-such as electronic stethoscopes, wearable oximeters, and under-mattress pressure sensors-have enabled low-burden physiological monitoring solutions, offering new opportunities for simplified obstructive sleep apnea detection. In this study, synchronized multimodal physiological data will be collected during overnight sleep, including respiratory sounds, continuous saturation measurements, and standard polysomnography waveforms. Signal preprocessing and feature extraction will be performed to ensure data quality and temporal alignment. A deep learning model will be developed using these multimodal signals as inputs. The apnea-hypopnea index will be derived from overnight polysomnography. The model will be trained to estimate apnea-hypopnea index values and classify obstructive sleep apnea severity according to established clinical thresholds.

Study Type

Observational

Enrollment (Estimated)

150

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • Taiwan
      • New Taipei City, Taiwan, 24352
        • Recruiting
        • Fu Jen Catholic University Hospital, Fu Jen Catholic University
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

patient of Affiliated University Hospital

Description

Inclusion Criteria:

  • age 30-75 years
  • clinically suspected obstructive sleep apnea and scheduled for polysomnography
  • willing and able to provide written informed consent

Exclusion Criteria:

  • intolerance to the electronic stethoscope or fingertip pulse oximeter
  • significant structural airway abnormalities
  • arrhythmia
  • neuromuscular disorders
  • pregnancy
  • hospitalization within the past 1 month
  • inability to provide informed consent or requiring legal guardian consent

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Time Frame
apnea-hypopnea index, sound waveforms, and the correlation between apnea-hypopnea index and ballistocardiography waveforms
Time Frame: one night
one night

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Fu Jen Catholic University

Investigators

  • Principal Investigator: Ke-Yun Chao, PhD, Fu Jen Catholic University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

September 5, 2025

Primary Completion (Estimated)

July 31, 2026

Study Completion (Estimated)

July 31, 2026

Study Registration Dates

First Submitted

February 26, 2026

First Submitted That Met QC Criteria

February 26, 2026

First Posted (Actual)

March 4, 2026

Study Record Updates

Last Update Posted (Actual)

March 5, 2026

Last Update Submitted That Met QC Criteria

March 3, 2026

Last Verified

February 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • FJUH114486

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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