Multimodal Deep Learning Model for Predicting the Apnea-Hypopnea Index in Obstructive Sleep
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age30-75
SponsorFu Jen Catholic University
About this trial
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 criteria
This trial does not accept healthy volunteersQualifiers
age 30-75 years
clinically suspected obstructive sleep apnea and scheduled for polysomnography
willing and able to provide written informed consent
Disqualifiers
intolerance to the electronic stethoscope or fingertip pulse oximeter
significant structural airway abnormalities
arrhythmia
neuromuscular disorders
Trial population
patient of Affiliated University Hospital
Trial design
Design model
Cohort
Time perspective
Prospective
Treatments tested in this trial
electronic stethoscope
Devicedigital device amplifying and recording cardiopulmonary sounds
fingertip pulse oximeter
Devicea small device placed on the finger to measure blood oxygen saturation (SpO₂) and pulse rate noninvasively.
pressure-sensing mattresses
Deviceusing ballistocardiography (BCG) for monitoring respiration and heart rate
Treatment groups
No treatment groups listed
Trial outcomes
Primary outcomes
1
apnea-hypopnea index, sound waveforms, and the correlation between apnea-hypopnea index and ballistocardiography waveforms
Time frame
one nightSponsors and contacts
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Source ClinicalTrials.gov