Obstructive Sleep ApneaNCT06522815
Updating Deep Learning Algorithms for OSA Monitoring
A study testing CART-I plus and Polysomnography in people with Obstructive Sleep Apnea and Obstructive Sleep Apnea-hypopnea, run by Sky Labs at 1 site in South Korea.
Trial summary
The objective is to enhance the reliability of the algorithm to match that of Level 1 polysomnography by leveraging the diverse data obtained from Level 1 polysomnography to refine the deep learning algorithm. Read more about this study
- Who is it for?
- Ages 19 years and older, women onlyHealthy volunteers can take part
- Conditions the study is for
- Obstructive Sleep Apnea, Obstructive Sleep Apnea-hypopnea
- What is being tested?
- CART-I plus and Polysomnography
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 107 participants
- Where
- 1 siteSouth Korea
- Study dates
- 19 Oct 2022to 11 Jul 2025
- Phase
- Interventional
- Are there results?
- None posted in the registry
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 19 years and older
- Women only
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Patients scheduled for Level 1 polysomnography at a sleep center who meet all of the following criteria:
- Aged 19 years or older
- Have listened to and understood a thorough explanation of the clinical study and voluntarily agreed to participate
Who cannot join
- Under 19 years of age
- Unable to collect normal signals during the pre-test or wearing of the CART-I PLUS device
- Refuse to participate in the clinical study
- Have cognitive impairments to the extent that they cannot understand the explanation of the clinical study and therefore cannot make a voluntary decision to participate (e.g., legally incompetent individuals)
Trial locations and contacts
1 site in 1 country.
Yujung Kang contact
South Korea1 site, 1 recruiting
- Recruiting
Gangnam Severance Hospital
Seoul
Won Joo Kim, PhD, kzoo@yuhs.ac
Gerrard Kim, gerrard.kim@skylabs.io
About this study
Official title: Deep Learning Algorithm Update Using Real Patients for Out-of-hospital Obstructive Sleep Apnea Monitoring
The objective is to enhance the reliability of the algorithm to match that of Level 1 polysomnography by leveraging the diverse data obtained from Level 1 polysomnography to refine the deep learning algorithm.
Read the detailed description
Patients undergoing Level 1 polysomnography are equipped with the CART-I PLUS device, for collecting polysomnography data alongside concurrent photoplethysmography (PPG) signals.
The collected data is categorized into apnea, hypopnea, and normal segments based on the polysomnography results. Utilizing the PPG and accelerometer (ACC) signals from the CART-I PLUS, metrics such as SaO2 (oxygen saturation), respiratory rate, heart rate (HR), heart rate variability (HRV), and body movement are calculated for each segment. These metrics, along with the PPG and ACC signals, are then used to develop a deep learning model that classifies the segments into apnea, hypopnea, or normal.
Participants are divided into training and validation sets. The deep learning model is trained on data from the participants in the training set, and its performance is evaluated using the validation set.
The algorithm is constructed using convolutional neural networks (CNN), recurrent neural networks (RNN), attention mechanisms, and other advanced techniques recognized for their efficacy in classification tasks, specifically for identifying apnea, hypopnea, and normal segments.
- Being tested
- CART-I plus, Polysomnography
- 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.
CART-I plusDevice
CART-I PLUS collects signals in two ways:
ECG: Utilizes the metal on the inner and outer sides as electrodes to detect subtle electrical changes resulting from the contraction and relaxation of the heart muscle.
PPG: Emits LED light into the blood vessels inside the finger and collects the signal reflected by the blood flow, thereby gathering data on the pulse and functional oxygen saturation (SpO2) of arterial hemoglobin.
In this clinical trial, PPG signals will be continuously collected during the polysomnography using the PPG method.
PolysomnographyDevice
In polysomnography, the following data are collected:
Electrocardiogram (ECG), Electroencephalogram (EEG), Electromyogram (EMG), Electrooculogram (EOG), Oxygen Saturation (SpO2) Respiratory Analysis, Body Position Monitoring
| Study group | Role | What they receive |
|---|---|---|
| Level 1 polysomnography patient | Experimental | For patients undergoing Level 1 polysomnography, simultaneous collection of photoplethysmography (PPG) signals and polysomnography data is performed using the CART-I PLUS device. Receives: Device: CART-I plus; Device: Polysomnography |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of the algorithm and the 95% confidence interval
Present the accuracy of the algorithm and the 95% confidence interval. If the lower bound of the 95% confidence interval exceeds a minimum accuracy of 0.85, it is considered clinically significant.
Measured: 11 hours
Secondary outcomes (1)
Accuracy and 95% confidence intervals for each interval
Present the accuracy and 95% confidence intervals for each interval. Additionally, precision, recall, ROC curve, and AUC may be presented. The performance comparison between algorithms will use the bootstrap method, and a p-value less than 0.05 will be considered statistically significant.
Measured: 11 hours
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Jul 2024
- Study start19 Oct 2022
- Primary completion22 Apr 2025 (estimated)
- Study completion11 Jul 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)Papers the sponsor has linked to this study.
- 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 107 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Sky Labs
- Sponsor type
- Industry (pharma, biotech, devices)
- Collaborators
- Gangnam Severance Hospital
- Responsible party
- Sponsor
- Investigators
- Won Joo Kim, MD, PhD, Principal investigator, Gangnam Severance Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06522815
- Sponsor's protocol ID
- 3-2022-0207
- Status last verified
- Jul 2024
- First submitted
- 22 Jul 2024
- Last updated
- 26 Jul 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Peppard PE, Young T, Palta M, Skatrud J. Prospective study of the association between sleep-disordered breathing and hypertension. N Engl J Med. 2000 May 11;342(19):1378-84. doi: 10.1056/NEJM200005113421901.
PubMed 10805822Arzt M, Young T, Finn L, Skatrud JB, Bradley TD. Association of sleep-disordered breathing and the occurrence of stroke. Am J Respir Crit Care Med. 2005 Dec 1;172(11):1447-51. doi: 10.1164/rccm.200505-702OC. Epub 2005 Sep 1.
PubMed 16141444Doherty LS, Kiely JL, Swan V, McNicholas WT. Long-term effects of nasal continuous positive airway pressure therapy on cardiovascular outcomes in sleep apnea syndrome. Chest. 2005 Jun;127(6):2076-84. doi: 10.1378/chest.127.6.2076.
PubMed 15947323Kim J, In K, Kim J, You S, Kang K, Shim J, Lee S, Lee J, Lee S, Park C, Shin C. Prevalence of sleep-disordered breathing in middle-aged Korean men and women. Am J Respir Crit Care Med. 2004 Nov 15;170(10):1108-13. doi: 10.1164/rccm.200404-519OC. Epub 2004 Sep 3.
PubMed 15347562
Study at a glance
From the registry record, last updated by the sponsor on 26 Jul 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep Learning Algorithm Update Using Real Patients for Out-of-hospital Obstructive Sleep Apnea Monitoring
- ClinicalTrials.gov ID
- NCT06522815
- Acronym / sponsor ID
- 3-2022-0207
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 107 participants (estimated)
- Lead sponsor
- Sky LabsIndustry (pharma, biotech, devices)
- Collaborators
- Gangnam Severance Hospital
- Study start
- 19 Oct 2022 (actual)
- Primary completion
- 22 Apr 2025 (estimated)
- Study completion
- 11 Jul 2025 (estimated)
- First posted
- 26 Jul 2024 (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.
The objective is to enhance the reliability of the algorithm to match that of Level 1 polysomnography by leveraging the diverse data obtained from Level 1 polysomnography to refine the deep learning algorithm.
Detailed description
Patients undergoing Level 1 polysomnography are equipped with the CART-I PLUS device, for collecting polysomnography data alongside concurrent photoplethysmography (PPG) signals.
The collected data is categorized into apnea, hypopnea, and normal segments based on the polysomnography results. Utilizing the PPG and accelerometer (ACC) signals from the CART-I PLUS, metrics such as SaO2 (oxygen saturation), respiratory rate, heart rate (HR), heart rate variability (HRV), and body movement are calculated for each segment. These metrics, along with the PPG and ACC signals, are then used to develop a deep learning model that classifies the segments into apnea, hypopnea, or normal.
Participants are divided into training and validation sets. The deep learning model is trained on data from the participants in the training set, and its performance is evaluated using the validation set.
The algorithm is constructed using convolutional neural networks (CNN), recurrent neural networks (RNN), attention mechanisms, and other advanced techniques recognized for their efficacy in classification tasks, specifically for identifying apnea, hypopnea, and normal segments.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Level 1 polysomnography patient | Experimental | Device: CART-I plus, Device: PolysomnographyFor patients undergoing Level 1 polysomnography, simultaneous collection of photoplethysmography (PPG) signals and polysomnography data is performed using the CART-I PLUS device. |
Who may qualify
- Ages
- 19 years and older
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- Patients scheduled for Level 1 polysomnography at a sleep center who meet all of the following criteria:
- Aged 19 years or older
- Have listened to and understood a thorough explanation of the clinical study and voluntarily agreed to participate
Key exclusion criteria (4 listed)
- Under 19 years of age
- Unable to collect normal signals during the pre-test or wearing of the CART-I PLUS device
- Refuse to participate in the clinical study
- Have cognitive impairments to the extent that they cannot understand the explanation of the clinical study and therefore cannot make a voluntary decision to participate (e.g., legally incompetent individuals)
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 |
|---|---|---|
| CART-I plusCART-I PLUS collects signals in two ways: ECG: Utilizes the metal on the inner and outer sides as electrodes to detect subtle electrical changes resulting from the contraction and relaxation of the heart muscle. PPG: Emits LED light into the blood vessels inside the finger and collects the signal reflected by the blood flow, thereby gathering data on the pulse and functional oxygen saturation (SpO2) of arterial hemoglobin. In this clinical trial, PPG signals will be continuously collected during the polysomnography using the PPG method. | Device | Level 1 polysomnography patient |
| PolysomnographyIn polysomnography, the following data are collected: Electrocardiogram (ECG), Electroencephalogram (EEG), Electromyogram (EMG), Electrooculogram (EOG), Oxygen Saturation (SpO2) Respiratory Analysis, Body Position Monitoring | Device | Level 1 polysomnography patient |
Primary outcome
Accuracy of the algorithm and the 95% confidence interval
Time frame: 11 hours
Present the accuracy of the algorithm and the 95% confidence interval. If the lower bound of the 95% confidence interval exceeds a minimum accuracy of 0.85, it is considered clinically significant.
1 secondary outcome
Accuracy and 95% confidence intervals for each interval
Time frame: 11 hours
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Jul 2024.
- Background: PubMed 10805822Peppard PE, Young T, Palta M, Skatrud J. Prospective study of the association between sleep-disordered breathing and hypertension. N Engl J Med. 2000 May 11;342(19):1378-84. doi: 10.1056/NEJM200005113421901.
- Background: PubMed 16141444Arzt M, Young T, Finn L, Skatrud JB, Bradley TD. Association of sleep-disordered breathing and the occurrence of stroke. Am J Respir Crit Care Med. 2005 Dec 1;172(11):1447-51. doi: 10.1164/rccm.200505-702OC. Epub 2005 Sep 1.
- Background: PubMed 15947323Doherty LS, Kiely JL, Swan V, McNicholas WT. Long-term effects of nasal continuous positive airway pressure therapy on cardiovascular outcomes in sleep apnea syndrome. Chest. 2005 Jun;127(6):2076-84. doi: 10.1378/chest.127.6.2076.
- Background: PubMed 15347562Kim J, In K, Kim J, You S, Kang K, Shim J, Lee S, Lee J, Lee S, Park C, Shin C. Prevalence of sleep-disordered breathing in middle-aged Korean men and women. Am J Respir Crit Care Med. 2004 Nov 15;170(10):1108-13. doi: 10.1164/rccm.200404-519OC. Epub 2004 Sep 3.
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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. This study is not recruiting at the moment.
Yujung Kang contact
Won Joo Kim, MD, PhD principal investigator
Gangnam Severance Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing CART-I plus and Polysomnography in people with Obstructive Sleep Apnea and Obstructive Sleep Apnea-hypopnea, run by Sky Labs at 1 site in South Korea.
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 4 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: status not confirmed, as of the sponsor's last update on 26 Jul 2024. 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 South Korea. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Gerrard Kim (1599-7149, gerrard.kim@skylabs.io), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of the algorithm and the 95% confidence interval (time frame: 11 hours). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06522815, which also lists 4 linked publications.
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 26 Jul 2024.