Development and Validation of a Multimodal Deep Learning Model Using Peripheral Oxygen Saturation and Ballistocardiography Signals for Predicting Apnea-Hypopnea Index in Obstructive Sleep Apnea
Key facts
- Where
- New Taipei City, TaiwanSee the site
Where in Taiwan
- Fu Jen Catholic University HospitalNew Taipei City, TaiwanContact: Clinical Research CoordinatorTaking people
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Contact the study team: see the registry record → Contacts and Locations
Print a page for my doctorTrial details
- Who runs it
- Fu Jen Catholic University
- Trial code
- NCT07923825
- Record updated
- 6 November 2026
- Original description
Obstructive sleep apnea (OSA) is a common sleep disorder associated with cardiovascular, metabolic, and neurocognitive complications. The standard diagnostic test for OSA is in-laboratory polysomnography (PSG), which can be costly, labor-intensive, and inconvenient for repeated or large-scale screening. Therefore, there is a need for a convenient and non-invasive method to help identify individuals who may be at risk for OSA.Read full original descriptionShow less
A Prospective Observational Study to Develop and Validate a Multimodal Deep Learning Model Integrating Peripheral Oxygen Saturation and Ballistocardiography Signals for the Prediction of Apnea-Hypopnea Index in Patients With Suspected Obstructive Sleep Apnea
Obstructive sleep apnea (OSA) is a common sleep disorder associated with cardiovascular, metabolic, and neurocognitive complications. The standard diagnostic test for OSA is in-laboratory polysomnography (PSG), which can be costly, labor-intensive, and inconvenient for repeated or large-scale screening. Therefore, there is a need for a convenient and non-invasive method to help identify individuals who may be at risk for OSA.
This prospective observational study aims to develop and clinically validate a multimodal deep learning model that integrates peripheral oxygen saturation (SpO₂) signals and ballistocardiography (BCG) data collected using a smart mattress system to estimate the apnea-hypopnea index (AHI). Adult participants undergoing routine overnight PSG for suspected OSA will be enrolled. During sleep, synchronized SpO₂ and BCG signals will be recorded non-invasively.
The performance of the deep learning model will be evaluated by comparing the predicted AHI values with AHI values derived from PSG. This study seeks to evaluate whether this practical, non-invasive, and scalable approach may support early identification of OSA risk and improve access to sleep disorder assessment in both clinical and home-based settings.
Inclusion: Adults aged 20 years or older; Individuals referred for in-laboratory polysomnography due to suspected obstructive sleep apnea; Able and willing to provide informed consent.
Exclusion: Prior diagnosis of sleep disorders other than obstructive sleep apnea that could confound study results; Presence of conditions that may interfere with sleep signal acquisition, as determined by the investigator; Inability to provide informed consent.