Obstructive Sleep Apnea (OSA)NCT07447999
Multimodal Deep Learning Model for Predicting the Apnea-Hypopnea Index in Obstructive Sleep
An observational study of Obstructive Sleep Apnea (OSA) and Polysomnography, run by Fu Jen Catholic University at 1 site in Taiwan.
Trial 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… Read more about this study
- Who is it for?
- Ages 30 to 75 years
- Conditions the study is for
- Obstructive Sleep Apnea (OSA), Polysomnography
- What is being tested?
- electronic stethoscope, fingertip pulse oximeter and pressure-sensing mattresses
- Type of treatment
- Medical device
- How many people
- 150 participants
- Where
- 1 siteTaiwan
- Study dates
- 5 Sep 2025to 31 Jul 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 30 to 75 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: patient of Affiliated University Hospital
Who may be able to join
- age 30-75 years
- clinically suspected obstructive sleep apnea and scheduled for polysomnography
- willing and able to provide written informed consent
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Taiwan1 site, 1 recruiting
- Recruiting
Fu Jen Catholic University Hospital, Fu Jen Catholic University
New Taipei City, 24352
Ke-Yun Chao, PhD, +886-905-301-879, C00152@mail.fjuh.fju.edu.tw
About this study
Official title: A Multisensor Deep Neural Framework Combining Digital Auscultation, Oxygen Saturation, and Motion Data to Estimate the Apnea-Hypopnea Index in Obstructive Sleep Apnea
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.
Read the detailed description
Obstructive sleep apnea is a common sleep disorder closely associated with cardiovascular, metabolic, and neuropsychiatric comorbidities. It is characterized by repeated upper airway collapse during sleep, leading to intermittent hypoxia and sleep fragmentation. Although polysomnography remains the diagnostic gold standard for obstructive sleep apnea, its high cost, complexity, and limited accessibility pose challenges for large-scale screening and early identification. Recent advancements in noninvasive sensing technologies-such as electronic stethoscopes, wearable oximeters, and under-mattress pressure sensors-have enabled low-burden physiological monitoring solutions, offering new opportunities for simplified obstructive sleep apnea detection. In this study, synchronized multimodal physiological data will be collected during overnight sleep, including respiratory sounds, continuous saturation measurements, and standard polysomnography waveforms. Signal preprocessing and feature extraction will be performed to ensure data quality and temporal alignment. A deep learning model will be developed using these multimodal signals as inputs. The apnea-hypopnea index will be derived from overnight polysomnography. The model will be trained to estimate apnea-hypopnea index values and classify obstructive sleep apnea severity according to established clinical thresholds.
- Conditions
- Obstructive Sleep Apnea (OSA), Polysomnography
- Being tested
- electronic stethoscope, fingertip pulse oximeter, pressure-sensing mattresses
- Keywords
- obstructive sleep apnea, polysomnography, ballistocardiography, electronic stethoscope, oxygen saturation
- Medical subject headings
- Sleep Apnea, Obstructive
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
electronic stethoscopeDevice
digital device amplifying and recording cardiopulmonary sounds
fingertip pulse oximeterDevice
a small device placed on the finger to measure blood oxygen saturation (SpO₂) and pulse rate noninvasively.
pressure-sensing mattressesDevice
using ballistocardiography (BCG) for monitoring respiration and heart rate
What is being measured
The primary outcome is the main question the study is designed to answer.
apnea-hypopnea index, sound waveforms, and the correlation between apnea-hypopnea index and ballistocardiography waveforms
Measured: one night
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Mar 2026
- Study start5 Sep 2025
- Primary completion31 Jul 2026 (estimated)
- Study completion31 Jul 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Obstructive Sleep Apnea (OSA)Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 150 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Fu Jen Catholic University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Ke-Yun, Chao, Fu Jen Catholic University
- Investigators
- Ke-Yun Chao, PhD, Principal investigator, Fu Jen Catholic University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07447999
- Sponsor's protocol ID
- FJUH114486
- Status last verified
- Feb 2026
- First submitted
- 26 Feb 2026
- Last updated
- 5 Mar 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 5 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Multisensor Deep Neural Framework Combining Digital Auscultation, Oxygen Saturation, and Motion Data to Estimate the Apnea-Hypopnea Index in Obstructive Sleep Apnea
- ClinicalTrials.gov ID
- NCT07447999
- Acronym / sponsor ID
- FJUH114486
- Conditions
- Obstructive Sleep Apnea (OSA), Polysomnography
- Keywords
- obstructive sleep apnea, polysomnography, ballistocardiography, electronic stethoscope, oxygen saturation
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 150 participants (estimated)
- Lead sponsor
- Fu Jen Catholic UniversityUniversity, hospital or other
- Study start
- 5 Sep 2025 (actual)
- Primary completion
- 31 Jul 2026 (estimated)
- Study completion
- 31 Jul 2026 (estimated)
- First posted
- 4 Mar 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Obstructive sleep apnea is a common sleep disorder closely associated with cardiovascular, metabolic, and neuropsychiatric comorbidities. It is characterized by repeated upper airway collapse during sleep, leading to intermittent hypoxia and sleep fragmentation. Although polysomnography remains the diagnostic gold standard for obstructive sleep apnea, its high cost, complexity, and limited accessibility pose challenges for large-scale screening and early identification. Recent advancements in noninvasive sensing technologies-such as electronic stethoscopes, wearable oximeters, and under-mattress pressure sensors-have enabled low-burden physiological monitoring solutions, offering new opportunities for simplified obstructive sleep apnea detection. In this study, synchronized multimodal physiological data will be collected during overnight sleep, including respiratory sounds, continuous saturation measurements, and standard polysomnography waveforms. Signal preprocessing and feature extraction will be performed to ensure data quality and temporal alignment. A deep learning model will be developed using these multimodal signals as inputs. The apnea-hypopnea index will be derived from overnight polysomnography. The model will be trained to estimate apnea-hypopnea index values and classify obstructive sleep apnea severity according to established clinical thresholds.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 30 to 75 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- patient of Affiliated University Hospital
Key inclusion criteria (3 listed)
- age 30-75 years
- clinically suspected obstructive sleep apnea and scheduled for polysomnography
- willing and able to provide written informed consent
Key exclusion criteria (7 listed)
- intolerance to the electronic stethoscope or fingertip pulse oximeter
- significant structural airway abnormalities
- arrhythmia
- neuromuscular disorders
- pregnancy
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| electronic stethoscopedigital device amplifying and recording cardiopulmonary sounds | Device | – |
| fingertip pulse oximetera small device placed on the finger to measure blood oxygen saturation (SpO₂) and pulse rate noninvasively. | Device | – |
| pressure-sensing mattressesusing ballistocardiography (BCG) for monitoring respiration and heart rate | Device | – |
Primary outcome
apnea-hypopnea index, sound waveforms, and the correlation between apnea-hypopnea index and ballistocardiography waveforms
Time frame: one night
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 5 Mar 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Ke-Yun Chao, PhD principal investigator
Fu Jen Catholic University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Obstructive Sleep Apnea (OSA) and Polysomnography, run by Fu Jen Catholic University at 1 site in Taiwan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 5 Mar 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Taiwan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Ke-Yun Chao, PhD (+886-905-301-879, C00152@mail.fjuh.fju.edu.tw), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
apnea-hypopnea index, sound waveforms, and the correlation between apnea-hypopnea index and ballistocardiography waveforms (time frame: one night). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 5 Mar 2026.