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
A legfontosabb adatok
- Ki végzi
- Fu Jen Catholic University
- Hol
- New Taipei City (Tajvan)A helyszín megnézése
- Azonosító
- Állapot
- Toboroz
- Résztvevők
- 100 fő, tervezett
- Kezdés
- 2026. márc. 1.
- Várható befejezés
- 2028. jún. 30.
- A vizsgálat típusa
- Megfigyeléses
- A regiszteradat frissítve
- 2026. november 6.
Ki vehet részt
- Életkor
- 20 év felett
- Nem
- Nők és férfiak
- Egészséges önkéntesek
- Részt vehetnek
A regiszter feltételei, szó szerint
Mind a 6 feltétel megnézéseKevesebb
Ki vehet részt
- 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
Ki nem vehet részt
- 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
Helyszínek itt: Tajvan
- Fu Jen Catholic University HospitalNew Taipei City, TajvanContact: Clinical Research CoordinatorRésztvevőt keres
A vizsgálatról
Mit akar kideríteni a vizsgálat
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.
A teljes eredeti leírásKevesebb
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.
A vizsgálat csoportja
- Adults With Suspected Obstructive Sleep Apnea
This cohort includes adult participants with suspected obstructive sleep apnea who are scheduled to undergo overnight in-laboratory polysomnography. All participants will receive standard clinical polysomnography while simultaneously undergoing non-invasive physiological signal acquisition using a smart mattress system and peripheral oxygen saturation monitoring. No experimental intervention or treatment assignment is performed.
Kérdések a vizsgálat csapatának
- Milyen gyakran kell bejárnom, és egy-egy alkalom mennyi ideig tart?
- Megtérítik az utazási költséget?
- Megtarthatom a saját orvosomat?
- Mi történik, ha ki akarok lépni?
Contact the study team: see the registry record → Contacts and Locations
Oldal nyomtatása az orvosomnakAdatok és források
- Regiszteradat
- Szerepel a ClinicalTrials.gov-on.
- Hivatalos cím
- 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
- A regiszteradat frissítve
- 2026. november 6.