Obstructive Sleep ApneaNCT07923825
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
An observational study of Obstructive Sleep Apnea, run by Fu Jen Catholic University at 1 site in Taiwan.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 20 years and olderHealthy volunteers can take part
- Conditions the study is for
- Obstructive Sleep Apnea
- How many people
- 100 participants
- Where
- 1 siteTaiwan
- Study dates
- 1 Mar 2026to 30 Jun 2028
- 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 20 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Adult individuals aged 20 years or older who are referred to Fu Jen Catholic University Hospital for in-laboratory polysomnography due to suspected obstructive sleep apnea will be invited to participate. The study population includes participants with a wide range of apnea-hypopnea index values, including individuals without obstructive sleep apnea based on polysomnography results.
Who may be able to join
- 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.
Who cannot join
- Inability to provide informed consent.
- Presence of conditions that may interfere with sleep signal acquisition, as determined by the investigator.
- Prior diagnosis of sleep disorders other than obstructive sleep apnea that could confound study results.
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
New Taipei City, New Taipei City, 24352
Clinical Research Coordinator, +886-2-2905-2000, Ck_qq@hotmail.com
About this study
Official title: 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.
Read the detailed description
This is a prospective observational cohort study designed to develop and validate a multimodal deep learning-based model for estimating obstructive sleep apnea (OSA) severity. Adult participants with suspected OSA who are scheduled to undergo overnight in-laboratory polysomnography (PSG) will be recruited at Fu Jen Catholic University Hospital.
During the PSG examination, participants will simultaneously undergo non-invasive physiological signal acquisition using a smart mattress system equipped with ballistocardiography (BCG) sensors, along with continuous peripheral oxygen saturation (SpO₂) monitoring. These signals will be time-synchronized with PSG recordings. No experimental intervention or alteration to standard clinical care will be performed.
Multimodal deep learning algorithms incorporating convolutional neural networks and sequence-based architectures will be developed to analyze SpO₂ and BCG signals and to estimate the apnea-hypopnea index (AHI). Model performance will be assessed by comparing predicted AHI values with PSG-derived AHI using predefined statistical metrics.
The primary objective is to evaluate the accuracy of AHI prediction using the proposed multimodal model. Secondary objectives include assessment of model discrimination for OSA severity classification and exploratory analyses of physiological signal features associated with sleep-disordered breathing. All analyses will be conducted using de-identified data. This study does not involve U.S. FDA-regulated drugs or devices and is not conducted under an investigational new drug application or investigational device exemption.
- Conditions
- Obstructive Sleep Apnea
- Keywords
- Obstructive Sleep Apnea, Ballistocardiography, Peripheral Oxygen Saturation, Deep Learning, Home Sleep Monitoring, Apnea-Hypopnea Index
- 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.
| Study group | Role | What they receive |
|---|---|---|
| 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Mean absolute error of apnea-hypopnea index estimation
Mean absolute error (MAE) will be calculated as the average absolute difference between the apnea-hypopnea index (AHI) predicted by the multimodal deep learning model using peripheral oxygen saturation (SpO2) and ballistocardiography (BCG) signals collected during home sleep monitoring and the AHI measured by in-laboratory polysomnography (PSG). The analysis will include participants with evaluable paired PSG and home monitoring data. MAE will be reported in events per hour, with lower values indicating better predictive performance.
Measured: Two overnight assessments per participant: one in-laboratory polysomnography night and one subsequent home sleep monitoring night.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Nov 2026
- Study start1 Mar 2026
- Primary completion30 Jun 2028 (estimated)
- Study completion30 Jun 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Obstructive Sleep ApneaStudies 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
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Fu Jen Catholic University
- Sponsor type
- University, hospital or other
- Collaborators
- Fu Jen Catholic University Hospital
- Responsible party
- Principal investigator, Ke-Yun, Chao, Fu Jen Catholic University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07923825
- Sponsor's protocol ID
- FJUH115580
- Status last verified
- Mar 2026
- First submitted
- 9 Feb 2026
- Last updated
- 6 Nov 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data will not be shared publicly.
Study at a glance
From the registry record, last updated by the sponsor on 6 Nov 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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
- ClinicalTrials.gov ID
- NCT07923825
- Acronym / sponsor ID
- FJUH115580
- Conditions
- Obstructive Sleep Apnea
- Keywords
- Obstructive Sleep Apnea, Ballistocardiography, Peripheral Oxygen Saturation, Deep Learning, Home Sleep Monitoring, Apnea-Hypopnea Index
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- Fu Jen Catholic UniversityUniversity, hospital or other
- Collaborators
- Fu Jen Catholic University Hospital
- Study start
- 1 Mar 2026 (actual)
- Primary completion
- 30 Jun 2028 (estimated)
- Study completion
- 30 Jun 2028 (estimated)
- First posted
- 6 Nov 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.
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.
Detailed description
This is a prospective observational cohort study designed to develop and validate a multimodal deep learning-based model for estimating obstructive sleep apnea (OSA) severity. Adult participants with suspected OSA who are scheduled to undergo overnight in-laboratory polysomnography (PSG) will be recruited at Fu Jen Catholic University Hospital.
During the PSG examination, participants will simultaneously undergo non-invasive physiological signal acquisition using a smart mattress system equipped with ballistocardiography (BCG) sensors, along with continuous peripheral oxygen saturation (SpO₂) monitoring. These signals will be time-synchronized with PSG recordings. No experimental intervention or alteration to standard clinical care will be performed.
Multimodal deep learning algorithms incorporating convolutional neural networks and sequence-based architectures will be developed to analyze SpO₂ and BCG signals and to estimate the apnea-hypopnea index (AHI). Model performance will be assessed by comparing predicted AHI values with PSG-derived AHI using predefined statistical metrics.
The primary objective is to evaluate the accuracy of AHI prediction using the proposed multimodal model. Secondary objectives include assessment of model discrimination for OSA severity classification and exploratory analyses of physiological signal features associated with sleep-disordered breathing. All analyses will be conducted using de-identified data. This study does not involve U.S. FDA-regulated drugs or devices and is not conducted under an investigational new drug application or investigational device exemption.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 20 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Adult individuals aged 20 years or older who are referred to Fu Jen Catholic University Hospital for in-laboratory polysomnography due to suspected obstructive sleep apnea will be invited to participate. The study population includes participants with a wide range of apnea-hypopnea index values, including individuals without obstructive sleep apnea based on polysomnography results.
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (3 listed)
- Inability to provide informed consent.
- Presence of conditions that may interfere with sleep signal acquisition, as determined by the investigator.
- Prior diagnosis of sleep disorders other than obstructive sleep apnea that could confound study results.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Mean absolute error of apnea-hypopnea index estimation
Time frame: Two overnight assessments per participant: one in-laboratory polysomnography night and one subsequent home sleep monitoring night.
Mean absolute error (MAE) will be calculated as the average absolute difference between the apnea-hypopnea index (AHI) predicted by the multimodal deep learning model using peripheral oxygen saturation (SpO2) and ballistocardiography (BCG) signals collected during home sleep monitoring and the AHI measured by in-laboratory polysomnography (PSG). The analysis will include participants with evaluable paired PSG and home monitoring data. MAE will be reported in events per hour, with lower values indicating better predictive performance.
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 6 Nov 2026.
- Individual participant dataSharing planned: No. Individual participant data will not be shared publicly.
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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.
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, 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 3 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 6 Nov 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 Principal Investigator (+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?
Mean absolute error of apnea-hypopnea index estimation (time frame: Two overnight assessments per participant: one in-laboratory polysomnography night and one subsequent home sleep monitoring 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 6 Nov 2026.