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
Qualifiers
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 design
Treatments tested in this trial
- electronic stethoscope
- fingertip pulse oximeter
- pressure-sensing mattresses
Treatment groups
No treatment groups listed
Sponsors and collaborators
Source ClinicalTrials.gov