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

NCT07447999 | Recruiting

• 1 other identifier: FJUH114486
• Study Type: observational
• Target: 150
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Obstructive Sleep Apnea (OSA); Polysomnography

Keywords

obstructive sleep apnea; polysomnography; ballistocardiography; electronic stethoscope; oxygen saturation

Outcome Measures

Primary Outcomes (1)

• apnea-hypopnea index, sound waveforms, and the correlation between apnea-hypopnea index and ballistocardiography waveforms

  one night

Interventions

electronic stethoscope DEVICE

digital device amplifying and recording cardiopulmonary sounds

fingertip pulse oximeter DEVICE

a small device placed on the finger to measure blood oxygen saturation (SpO₂) and pulse rate noninvasively.

pressure-sensing mattresses DEVICE

using ballistocardiography (BCG) for monitoring respiration and heart rate

Eligibility Criteria

• Age: 30 Years - 75 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

patient of Affiliated University Hospital

You may qualify if:

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

You may not qualify if:

• 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

Sponsors & Collaborators

• Fu Jen Catholic University: lead

Study Sites (1)

Fu Jen Catholic University Hospital, Fu Jen Catholic University

New Taipei City, 24352, Taiwan

RECRUITING

MeSH Terms

Conditions

Sleep Apnea, Obstructive

Condition Hierarchy (Ancestors)

Sleep Apnea Syndromes; Apnea; Respiration Disorders; Respiratory Tract Diseases; Sleep Disorders, Intrinsic; Dyssomnias; Sleep Wake Disorders; Nervous System Diseases

Study Officials

• Ke-Yun Chao, PhD

  Fu Jen Catholic University

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Ke-Yun Chao, PhD

CONTACT

+886-905-301-879; C00152@mail.fjuh.fju.edu.tw

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assistant Professor

Study Record Dates

• First Submitted: February 26, 2026
• First Posted: March 4, 2026
• Study Start: September 5, 2025
• Primary Completion (Estimated): July 31, 2026
• Study Completion (Estimated): July 31, 2026
• Last Updated: March 5, 2026

Record last verified: 2026-02

Data Sharing

• IPD Sharing: Will not share

Locations

TW (1)