ApneaNCT05868694
A Study of Breathing Sound-based Classification of Patients With Breathing Disorders
An observational study of Apnea, run by Huai'an No.1 People's Hospital at 1 site in China.
Trial summary
Sleep-disordered breathing can damage the cardiovascular system, and may also lead to dysregulation of the autonomic nervous system, endocrine disorders, and hemodynamic changes, causing multi-system and multi-organ damage. Read more about this study
- Who is it for?
- Ages 18 to 75 yearsHealthy volunteers can take part
- Conditions the study is for
- Apnea
- What is being tested?
- polysomnography
- How many people
- 200 participants
- Where
- 1 siteChina
- Study dates
- 1 Dec 2021to 1 Jun 2024
- How long will I take part?
- 3 Days
- Phase
- Observational
- 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 18 to 75 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: 1. age between 18 to 75 yrs; 2. Patients diagnosed with apnea syndrome;
Who may be able to join
- the age of the patient is 18-75 years old;
- patients with confirmed PSG with AHI ≥ 5 times/hour, with or without daytime sleepiness, hypertension, and diabetes;
- sleep-disordered breathing has not been treated;
- informed consent of patients
Who cannot join
- pregnancy;
- have other diseases that are not suitable for participation in this study
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Department Of Respiratory Medicine,Huai'an First People's Hospital,Nanjing Medical University
Huai'an, Jiangsu, 223300
Jing Xu, Master, 13912080023, xj680390@126.com
About this study
Official title: Huai'an First People's Hospital
Sleep-disordered breathing can damage the cardiovascular system, and may also lead to dysregulation of the autonomic nervous system, endocrine disorders, and hemodynamic changes, causing multi-system and multi-organ damage. Screening for potential central-type patients among patients with respiratory disorders can help provide scientific diagnosis and treatment decisions, thus achieving precise treatment. Currently, research on the identification of sleep-disordered breathing phenotypes is in its infancy. Sleep-disordered breathing phenotypes, such as obstructive and central respiratory events, vary widely among individuals. Compared to indirect methods such as RIP and SpO2, changes in breathing sounds and snoring during sleep can more directly reflect airway obstruction. Different types of sleep-disordered breathing exhibit different characteristics in terms of snoring. Patients with obstructive sleep apnea experience narrowing or blockage of the airway due to relaxation of the throat muscles during sleep, which leads to breathing pauses and hypopnea events, resulting in decreased blood oxygen levels, arousal, and snoring. Central sleep apnea is caused by problems with the brainstem or respiratory control center, leading to breathing pauses. Snoring is usually not very prominent in patients with central sleep apnea. This study aims to screen for potential central-type patients by analyzing upper airway sounds of patients with sleep-disordered breathing, in order to achieve precise treatment.
Read the detailed description
Screening for central apnea from obstructive apnea is important for the precise treatment of respiratory disorders. Based on the above assumptions that the time domain and acoustic variability of respiratory sound signals contain key information about the degree of upper respiratory tract obstruction and the role of respiratory effort, this study proposes a sleep breathing disorder category identification model based on respiratory sound analysis.
A microphone device and sound card are used to capture the patient's audio signal overnight and transmit it to the Raspberry Pi for processing and storage. The microphone device is worn at the neckline of the patient to collect the sound signal of breathing, which ensures that the sound signal is less affected by the sleeping position. Sleep and wakefulness are then separated from breathing sound signals throughout the night and the patient's sleep period is analyzed individually. The apnea location is determined in 30s frames, and in apnea event detection, if the sound stops and lasts for more than 10 seconds, it may be a apnea event. Taking the sound signal of 20s to 30s before apnea as the analysis object, the OpenSmile and Tsfresh feature extraction tools are used to extract acoustic features and envelope features, respectively. The acoustic signature reflects the frequency domain information of apnea, and the envelope feature reflects the time domain signature of apnea. Fusion of acoustic and envelope features enables analysis of airway obstruction and respiratory effort in patients with respiratory disorders.
Finally, a machine learning model is established using acoustic features and envelope features as inputs, and each apnea event is classified one by one. In this study, two centers are included, namely the Sleep Therapy Center of the First People's Hospital of Huai'an and the Sleep Therapy Center of the Jiangsu Provincial People's Hospital. Sleep audio data for 167 and 62 cases are expected to be included. The training and validation sets used for modeling are 90 cases, using ten-fold cross-validation, the internal test set is expected to include 77 sleep audio data, and the audio data of 62 patients collected from Jiangsu Provincial People's Hospital are used as the external test set.
- Conditions
- Apnea
- Being tested
- polysomnography
- Keywords
- snoring sounds, respiratory sounds, obstructive apnea, central apnea, heart failure
- Medical subject headings
- Apnea, Respiratory Sounds, Sleep Apnea, Obstructive, Sleep Apnea, Central, Heart Failure
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
polysomnographyDiagnostic test
Polysomnography is mainly used to diagnose sleep breathing disorders, including sleep apnea syndrome, snoring, upper airway resistance syndrome, and also used for the auxiliary diagnosis of other sleep disorders, such as: narcolepsy, restless legs syndrome, insomnia classification, etc.
| Study group | Role | What they receive |
|---|---|---|
| Group 1 | Patients suspected of having obstructive apnea Receives: Diagnostic Test: polysomnography |
What is being measured
The primary outcome is the main question the study is designed to answer.
The accuracy of the binary classification of obstructive apnea and central apnea
Based on the binary classification of events, obstructive apnea is the negative class and central apnea is the positive class. Accuracy is the ratio of the predicted correct positive plus negative class to the total event.
Measured: 3 days
The recall of the binary classification of obstructive apnea and central apnea
According to the binary classification of events, obstructive apnea is negative and central apnea is positive. Recall represents the proportion of all positive events in the dataset that the model correctly classifies as positive.
Measured: 3 days
Secondary outcomes (2)
The accuracy of the patient's apnea detection
Apnea detection in 30-second segments. Segments with apnea are positive and segments without apnea are negative. Accuracy is the ratio of the predicted correct positive plus negative classes to the total fragment.
Measured: 3 days
The recall of the patient's apnea detection.
30-second apnea detection. Segments with apnea are positive and those without apnea are negative. Recall indicates the ratio of the correctly classified positive fragments of the model to all positive fragments in the dataset.
Measured: 3 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 May 2023
- Study start1 Dec 2021
- Primary completion1 Dec 2023 (estimated)
- Study completion1 Jun 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for 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
- Cross-sectional
- Enrolment
- 200 participants (estimated)
- Target duration
- 3 Days
Sponsor and investigators
- Lead sponsor
- Huai'an No.1 People's Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- The First Affiliated Hospital with Nanjing Medical University, Nanjing University of Science and Technology
- Responsible party
- Principal investigator, Xu J, Huai'an No.1 People's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05868694
- Sponsor's protocol ID
- YX-2021-061-01
- Status last verified
- Jun 2023
- First submitted
- 2 May 2023
- Last updated
- 13 Jun 2023
- Results posted
- No
- Data monitoring committee
- Yes
- 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 13 Jun 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Huai'an First People's Hospital
- ClinicalTrials.gov ID
- NCT05868694
- Acronym / sponsor ID
- YX-2021-061-01
- Conditions
- Apnea
- Keywords
- snoring sounds, respiratory sounds, obstructive apnea, central apnea, heart failure
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Huai'an No.1 People's HospitalUniversity, hospital or other
- Collaborators
- The First Affiliated Hospital with Nanjing Medical University, Nanjing University of Science and Technology
- Study start
- 1 Dec 2021 (actual)
- Primary completion
- 1 Dec 2023 (estimated)
- Study completion
- 1 Jun 2024 (estimated)
- First posted
- 22 May 2023 (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.
Sleep-disordered breathing can damage the cardiovascular system, and may also lead to dysregulation of the autonomic nervous system, endocrine disorders, and hemodynamic changes, causing multi-system and multi-organ damage. Screening for potential central-type patients among patients with respiratory disorders can help provide scientific diagnosis and treatment decisions, thus achieving precise treatment. Currently, research on the identification of sleep-disordered breathing phenotypes is in its infancy. Sleep-disordered breathing phenotypes, such as obstructive and central respiratory events, vary widely among individuals. Compared to indirect methods such as RIP and SpO2, changes in breathing sounds and snoring during sleep can more directly reflect airway obstruction. Different types of sleep-disordered breathing exhibit different characteristics in terms of snoring. Patients with obstructive sleep apnea experience narrowing or blockage of the airway due to relaxation of the throat muscles during sleep, which leads to breathing pauses and hypopnea events, resulting in decreased blood oxygen levels, arousal, and snoring. Central sleep apnea is caused by problems with the brainstem or respiratory control center, leading to breathing pauses. Snoring is usually not very prominent in patients with central sleep apnea. This study aims to screen for potential central-type patients by analyzing upper airway sounds of patients with sleep-disordered breathing, in order to achieve precise treatment.
Detailed description
Screening for central apnea from obstructive apnea is important for the precise treatment of respiratory disorders. Based on the above assumptions that the time domain and acoustic variability of respiratory sound signals contain key information about the degree of upper respiratory tract obstruction and the role of respiratory effort, this study proposes a sleep breathing disorder category identification model based on respiratory sound analysis.
A microphone device and sound card are used to capture the patient's audio signal overnight and transmit it to the Raspberry Pi for processing and storage. The microphone device is worn at the neckline of the patient to collect the sound signal of breathing, which ensures that the sound signal is less affected by the sleeping position. Sleep and wakefulness are then separated from breathing sound signals throughout the night and the patient's sleep period is analyzed individually. The apnea location is determined in 30s frames, and in apnea event detection, if the sound stops and lasts for more than 10 seconds, it may be a apnea event. Taking the sound signal of 20s to 30s before apnea as the analysis object, the OpenSmile and Tsfresh feature extraction tools are used to extract acoustic features and envelope features, respectively. The acoustic signature reflects the frequency domain information of apnea, and the envelope feature reflects the time domain signature of apnea. Fusion of acoustic and envelope features enables analysis of airway obstruction and respiratory effort in patients with respiratory disorders.
Finally, a machine learning model is established using acoustic features and envelope features as inputs, and each apnea event is classified one by one. In this study, two centers are included, namely the Sleep Therapy Center of the First People's Hospital of Huai'an and the Sleep Therapy Center of the Jiangsu Provincial People's Hospital. Sleep audio data for 167 and 62 cases are expected to be included. The training and validation sets used for modeling are 90 cases, using ten-fold cross-validation, the internal test set is expected to include 77 sleep audio data, and the audio data of 62 patients collected from Jiangsu Provincial People's Hospital are used as the external test set.
Study design
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Target follow-up
- 3 Days
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Group 1 | – | Diagnostic Test: polysomnographyPatients suspected of having obstructive apnea |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- 1. age between 18 to 75 yrs; 2. Patients diagnosed with apnea syndrome;
Key inclusion criteria (4 listed)
- the age of the patient is 18-75 years old;
- patients with confirmed PSG with AHI ≥ 5 times/hour, with or without daytime sleepiness, hypertension, and diabetes;
- sleep-disordered breathing has not been treated;
- informed consent of patients
Key exclusion criteria (2 listed)
- pregnancy;
- have other diseases that are not suitable for participation in this study
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 |
|---|---|---|
| polysomnographyPolysomnography is mainly used to diagnose sleep breathing disorders, including sleep apnea syndrome, snoring, upper airway resistance syndrome, and also used for the auxiliary diagnosis of other sleep disorders, such as: narcolepsy, restless legs syndrome, insomnia classification, etc. | Diagnostic test | Group 1 |
Primary outcome
The accuracy of the binary classification of obstructive apnea and central apnea
Time frame: 3 days
Based on the binary classification of events, obstructive apnea is the negative class and central apnea is the positive class. Accuracy is the ratio of the predicted correct positive plus negative class to the total event.
The recall of the binary classification of obstructive apnea and central apnea
Time frame: 3 days
According to the binary classification of events, obstructive apnea is negative and central apnea is positive. Recall represents the proportion of all positive events in the dataset that the model correctly classifies as positive.
2 secondary outcomes
The accuracy of the patient's apnea detection
Time frame: 3 days
The recall of the patient's apnea detection.
Time frame: 3 days
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 Jun 2023.
- 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. This study is not recruiting at the moment.
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 Apnea, run by Huai'an No.1 People's Hospital at 1 site in China.
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 4 inclusion and 2 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 13 Jun 2023. 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 China. 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 Biao Xue, Doctor (15850573313, bxue0909@njust.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The accuracy of the binary classification of obstructive apnea and central apnea (time frame: 3 days). The study lists 2 primary outcomes in all. 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 13 Jun 2023.