Multimodal Deep Learning Model for Predicting the Apnea-Hypopnea Index in Obstructive Sleep
A Multisensor Deep Neural Framework Combining Digital Auscultation, Oxygen Saturation, and Motion Data to Estimate the Apnea-Hypopnea Index in Obstructive Sleep Apnea
Fu Jen Catholic University
150 participants
Sep 5, 2025
OBSERVATIONAL
Conditions
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.
Eligibility
Inclusion Criteria3
- age 30-75 years
- clinically suspected obstructive sleep apnea and scheduled for polysomnography
- willing and able to provide written informed consent
Exclusion Criteria7
- 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
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Interventions
digital device amplifying and recording cardiopulmonary sounds
a small device placed on the finger to measure blood oxygen saturation (SpO₂) and pulse rate noninvasively.
using ballistocardiography (BCG) for monitoring respiration and heart rate
Locations(1)
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NCT07447999