Out-of-hospital Cardiac Arrest (OHCA)AIR-CPRNCT07431710
The AIR-CPR Study: AI-Guided Chest Compressions
An observational study of Out-of-hospital Cardiac Arrest (OHCA) and Cardiopulmonary Resuscitation (CPR), run by Far Eastern Memorial Hospital at 1 site in Taiwan.
Trial summary
The AIR-CPR project aims to improve survival rates for patients with Out-of-Hospital Cardiac Arrest (OHCA) by utilizing Artificial Intelligence (AI) to optimize chest compression locations. Read more about this study
- Who is it for?
- Ages 20 years and older
- Conditions the study is for
- Out-of-hospital Cardiac Arrest (OHCA), Cardiopulmonary Resuscitation (CPR), Aortic Valve Compression
- What is being tested?
- Device: AI-Enhanced Arterial Waveform Monitor (AIR-CPR App), AI-Guided Chest Compression Repositioning and Transesophageal Echocardiography (TEE)
- Type of treatment
- Medical device, Surgery or procedure
- How many people
- 255 participants
- Where
- 1 siteTaiwan
- Study dates
- 6 Jan 2025to 31 Dec 2027
- 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
- No healthy volunteers
- Adult, Older adult
Study population: The study population consists of adult patients experiencing non-traumatic out-of-hospital cardiac arrest who are transported to the Emergency Department of Far Eastern Memorial Hospital. This facility is the only medical center in New Taipei City and handles approximately 30% of the city's OHCA cases, ensuring a diverse and high-volume sample of patients receiving advanced life support.
Who may be able to join
- Adults aged 20 years or older.
- Patients with out-of-hospital cardiac arrest (OHCA) undergoing 3.cardiopulmonary resuscitation (CPR) in the emergency department.
- Cardiac arrest caused by non-traumatic factors.
Who cannot join
- Pregnant patients.
- Patients with obvious signs of death.
- Patients with a signed "Do Not Resuscitate" (DNR) order.
- Patients requiring extracorporeal cardio-pulmonary resuscitation (ECPR).
- Patients requiring Resuscitative Endovascular Balloon Occlusion of the Aorta (REBOA).
- Cardiac arrest caused by massive hemorrhage, aortic emergencies, tension pneumothorax, cardiac tamponade, or pulmonary embolism.
Show all 9Show fewer
- History of severe aortic valve disease or previous aortic valve surgery.
- Patients for whom TEE or femoral arterial catheterization is contraindicated.
- Situations where the medical team is unable to perform TEE or femoral arterial catheterization during CPR.
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
Far Eastern Memorinal Hospital
New Taipei City, Banqiao, 220
Sheng-En Chu, physician, 886-2-77281843, ianchu300@msn.com
About this study
Official title: Utilizing Artificial Intelligence to Optimize Chest Compression Region During Cardio-pulmonary Resuscitation for Patients With Out-of-hospital Cardiac Arrest.
The AIR-CPR project aims to improve survival rates for patients with Out-of-Hospital Cardiac Arrest (OHCA) by utilizing Artificial Intelligence (AI) to optimize chest compression locations. Current guidelines recommend a standardized compression point (the lower half of the sternum), yet recent research indicates that this position can compress the aortic valve in approximately 48.7% of patients, significantly reducing the chances of successful resuscitation.
This study will develop a deep learning model based on YOLO v8 to analyze real-time arterial pressure waveforms to identify proper aortic valve opening and closing. By identifying specific waveform features that humans cannot easily distinguish, the AI will guide rescuers to adjust the compression site-typically toward the left ventricle-to ensure optimal blood output. The project seeks to transform CPR from a standardized "one-size-fits-all" approach into a personalized, precision medicine intervention.
Read the detailed description
This three-year prospective study is designed to develop and clinically validate an "AI-Enhanced Arterial Waveform Monitor" to guide precision CPR.
- Research Hypothesis and Objectives The study tests the hypothesis that AI can accurately predict aortic valve compression (confirmed by Transesophageal Echocardiography, TEE) by analyzing arterial pressure waveforms, thereby allowing rescuers to find the optimal compression site that avoids the aortic valve and maximizes cardiac output.
- Implementation Phases The project is divided into five distinct stages: Case Preparation: Enrollment of 150 OHCA patients to collect synchronized TEE video and arterial pressure data. Arterial Waveform Detection Model: Development of an algorithm to automatically segment continuous pressure signals into single-compression waveform samples. Compression Region Detection Model: Training a YOLO v8-based model integrated with patient physiological data (age, sex, medical history) to distinguish between "compressed" and "non-compressed" aortic valve states. Clinical External Testing: Enrolling an additional 75 patients to verify model accuracy against TEE "gold standard" findings. Feasibility Assessment: Deploying the model as a "Resuscitation Support App" in 30 real-world clinical cases to evaluate its real-time guidance capability, speed, and impact on patient outcomes.
- Technical Methodology Data Extraction: Using binarization and interpolation curve fitting to extract high-quality numerical data directly from physiological monitor screens. AI Architecture: Utilizing an improved YOLO v8 framework combined with an Attention-based architecture and Fully-connected neural networks to incorporate complex patient heterogeneities. Clinical Intervention: When the AI identifies aortic valve compression, rescuers will be prompted to adjust the compression location (typically downward and to the left) until the valve is no longer obstructed.
- Outcome Measures The study will evaluate the Identification Success Rate (AI vs. TEE), Avoidance Success Rate (successful repositioning), and traditional resuscitation metrics including ROSC, survival to discharge, and favorable neurologic outcomes.
- Conditions
- Out-of-hospital Cardiac Arrest (OHCA), Cardiopulmonary Resuscitation (CPR), Aortic Valve Compression, Precision Resuscitation
- Being tested
- Device: AI-Enhanced Arterial Waveform Monitor (AIR-CPR App), AI-Guided Chest Compression Repositioning, Transesophageal Echocardiography (TEE)
- Keywords
- Artificial Intelligence, Transesophageal Echocardiography, Arterial Pressure Waveform, Chest Compression Location, Personalized Resuscitation, Hemodynamic Monitoring
- Medical subject headings
- Out-of-Hospital Cardiac Arrest
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Device: AI-Enhanced Arterial Waveform Monitor (AIR-CPR App)Device
A deep learning application based on the YOLO v8 architecture that analyzes real-time arterial pressure waveforms from a femoral A-line. It identifies whether the current chest compression location is causing aortic valve compression (as confirmed by TEE) and provides immediate feedback to the resuscitation team.
AI-Guided Chest Compression RepositioningProcedure or surgery
When the AI application indicates aortic valve compression, the rescuer adjusts the mechanical chest compression (LUCAS) position. Based on literature and AI feedback, the adjustment typically involves moving the compression point downward and toward the left parasternal line to avoid the aortic valve and optimize left ventricular output.
Transesophageal Echocardiography (TEE)Diagnostic test
Used as the "Gold Standard" throughout the study. TEE is performed during CPR to record the actual opening and closing of the aortic valve and the deformation of cardiac chambers, providing the labels for AI training and the verification for clinical testing.
| Study group | Role | What they receive |
|---|---|---|
| OHCA Patients Receiving AI-Enhanced Resuscitation. | Adult patients (20 years or older) with non-traumatic Out-of-Hospital Cardiac Arrest (OHCA) who receive Advanced Life Support (ALS) at the Far Eastern Memorial Hospital Emergency Department. This cohort provides the data for AI training (Years 1-2) and participates in the clinical validation of the AI-guided compression technique (Year 3). Receives: Device: Device: AI-Enhanced Arterial Waveform Monitor (AIR-CPR App); Procedure: AI-Guided Chest Compression Repositioning; Diagnostic Test: Transesophageal Echocardiography (TEE) |
What is being measured
The primary outcome is the main question the study is designed to answer.
AI Identification Accuracy of Aortic Valve Compression
The accuracy of the AI model in identifying whether the aortic valve is compressed or open during CPR, using Transesophageal Echocardiography (TEE) as the gold standard for verification.
Measured: Collected during the clinical testing phase and feasibility assessment (Years 2 and 3).
Secondary outcomes (5)
Successful Avoidance of Aortic Valve Compression
The percentage of cases where the resuscitation team successfully adjusted the chest compression location to stop aortic valve compression based on AI app feedback, confirmed by TEE.
Measured: During the clinical feasibility assessment (Year 3).
Time Consumed for Compression Adjustment
The time interval between the first arterial waveform detection and the completion of the chest compression repositioning.
Measured: During the clinical feasibility assessment (Year 3).
Rate of Return of Spontaneous Circulation (ROSC)
Incidence of ROSC and sustained ROSC (maintained for $\ge 20$ minutes), as well as survival rates to hospital admission and discharge.
Measured: From the start of the emergency department resuscitation until hospital discharge or death (up to approximately 30 days).
Favorable Neurologic Outcome at Discharge
Assessment of neurological status using the Cerebral Performance Category (CPC 1-2) or Modified Rankin Scale (mRS 0-2).
Measured: At the time of hospital discharge (up to approximately 30 days).
Chest Compression Fraction (CCF)
The proportion of total resuscitation time during which chest compressions were performed, ensuring that AI-guided adjustments do not negatively impact the continuity of compressions.
Measured: During the clinical feasibility assessment (Year 3).
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Feb 2026
- Study start6 Jan 2025
- Primary completion31 Dec 2027 (estimated)
- Study completion31 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (49)Papers the sponsor has linked to this study.
- Other trials for Out-of-hospital Cardiac Arrest (OHCA)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
- 255 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Far Eastern Memorial Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- National Health Research Institutes, Taiwan
- Responsible party
- Principal investigator, Sheng-En Chu, Far Eastern Memorial Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07431710
- Sponsor's protocol ID
- 113046-F
- Acronym
- AIR-CPR
- Status last verified
- Feb 2026
- First submitted
- 19 Feb 2026
- Last updated
- 24 Feb 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Miao X, Wu Y, Wang J, Gao Y, Mao X, Yin J. Generative Semi-supervised Learning for Multivariate Time Series Imputation. Proceedings of the AAAI Conference on Artificial Intelligence 2021;35(10):8983-8991. DOI: 10.1609/aaai.v35i10.17086.
Salloum R, Kuo CCJ. ECG-based biometrics using recurrent neural networks. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)2017:2062-2066.
Mitra A, Kundu P, Gupta R. Hyperparameter Optimization of AutoRegressive Integrated Moving Average (ARIMA) Model-based Synthesis of Electrocardiogram. 2022 International Conference for Advancement in Technology (ICONAT)2022:1-6.
Emergency Research. Content current as of: 09/21/2015 https://www.fda.gov/science-research/clinical-trials-and-human-subject-protection/protection-human-subjects-informed-consent-and-waiver-informed-consent-requirements-certain.
Karhade J, Dash S, Ghosh SK, Dash DK, Tripathy RK. Time-Frequency-Domain Deep Learning Framework for the Automated Detection of Heart Valve Disorders Using PCG Signals. IEEE Transactions on Instrumentation and Measurement 2022;71:1-11. DOI: 10.1109/TIM.2022.3163156.
Zhu Z, Wang H, Zhao T, et al. Classification of Cardiac Abnormalities From ECG Signals Using SE-ResNet. 2020 Computing in Cardiology2020:1-4.
Nagasawa T, Iuchi K, Takahashi R, et al. Blood Pressure Estimation by Photoplethysmogram Decomposition into Hyperbolic Secant Waves. Applied Sciences 2022;12(4):1798. (https://www.mdpi.com/2076-3417/12/4/1798).
Chirinos JA, Akers SR, Vierendeels JA, Segers P. A Unified Mechanism for the Water Hammer Pulse and Pulsus Bisferiens in Severe Aortic Regurgitation: Insights from Wave Intensity Analysis. Artery Res. 2018 Mar;21:9-12. doi: 10.1016/j.artres.2017.12.002. Epub 2017 Dec 21. No abstract available.
PubMed 29576810Lah K, Krizmaric M, Grmec S. The dynamic pattern of end-tidal carbon dioxide during cardiopulmonary resuscitation: difference between asphyxial cardiac arrest and ventricular fibrillation/pulseless ventricular tachycardia cardiac arrest. Crit Care. 2011;15(1):R13. doi: 10.1186/cc9417. Epub 2011 Jan 11.
PubMed 21223550Emergency Medical Services Resuscitation Success Statistics, as provided by the Fire Department of the Ministry of the Interior, Taiwan. https://www.nfa.gov.tw/cht/index.php?act=download&ids=14489. 2021.
Chu SE, Huang CY, Cheng CY, Chan CH, Chen HA, Chang CH, Tsai KC, Chiu KM, Ma MH, Chiang WC, Sun JT. Cardiopulmonary Resuscitation Without Aortic Valve Compression Increases the Chances of Return of Spontaneous Circulation in Out-of-Hospital Cardiac Arrest: A Prospective Observational Cohort Study. Crit Care Med. 2024 Sep 1;52(9):1367-1379. doi: 10.1097/CCM.0000000000006336. Epub 2024 May 23.
PubMed 38780398Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997 Nov 15;9(8):1735-80. doi: 10.1162/neco.1997.9.8.1735.
PubMed 9377276Xu L, Wang X, Wang R, Liu S, Xu M. Engineered Macrophages: A Safe-by-Design Approach for the Tumor Targeting Delivery of Sub-5 nm Gold Nanoparticles. Small. 2023 Jan;19(1):e2205474. doi: 10.1002/smll.202205474. Epub 2022 Nov 13.
PubMed 36372550Sert A. Is there a new method for determining aortic root dilatation in children? Pediatr Nephrol. 2020 Dec;35(12):2383. doi: 10.1007/s00467-020-04765-3. Epub 2020 Sep 23. No abstract available.
PubMed 32964319Garcia-Sanz MT, Canive-Gomez JC, Garcia-Couceiro N, Senin-Rial L, Alonso-Acuna S, Barreiro-Garcia A, Lopez-Val E, Valdes L, Gonzalez-Barcala FJ. Factors associated with the incidence of serious adverse events in patients admitted with COPD acute exacerbation. Ir J Med Sci. 2017 May;186(2):477-483. doi: 10.1007/s11845-016-1431-9. Epub 2016 Mar 18.
PubMed 27083455
Study at a glance
From the registry record, last updated by the sponsor on 24 Feb 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Utilizing Artificial Intelligence to Optimize Chest Compression Region During Cardio-pulmonary Resuscitation for Patients With Out-of-hospital Cardiac Arrest.
- ClinicalTrials.gov ID
- NCT07431710
- Acronym / sponsor ID
- AIR-CPR / 113046-F
- Conditions
- Out-of-hospital Cardiac Arrest (OHCA), Cardiopulmonary Resuscitation (CPR), Aortic Valve Compression, Precision Resuscitation
- Keywords
- Artificial Intelligence, Transesophageal Echocardiography, Arterial Pressure Waveform, Chest Compression Location, Personalized Resuscitation, Hemodynamic Monitoring
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 255 participants (estimated)
- Lead sponsor
- Far Eastern Memorial HospitalUniversity, hospital or other
- Collaborators
- National Health Research Institutes, Taiwan
- Study start
- 6 Jan 2025 (actual)
- Primary completion
- 31 Dec 2027 (estimated)
- Study completion
- 31 Dec 2027 (estimated)
- First posted
- 24 Feb 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.
The AIR-CPR project aims to improve survival rates for patients with Out-of-Hospital Cardiac Arrest (OHCA) by utilizing Artificial Intelligence (AI) to optimize chest compression locations. Current guidelines recommend a standardized compression point (the lower half of the sternum), yet recent research indicates that this position can compress the aortic valve in approximately 48.7% of patients, significantly reducing the chances of successful resuscitation.
This study will develop a deep learning model based on YOLO v8 to analyze real-time arterial pressure waveforms to identify proper aortic valve opening and closing. By identifying specific waveform features that humans cannot easily distinguish, the AI will guide rescuers to adjust the compression site-typically toward the left ventricle-to ensure optimal blood output. The project seeks to transform CPR from a standardized "one-size-fits-all" approach into a personalized, precision medicine intervention.
Detailed description
This three-year prospective study is designed to develop and clinically validate an "AI-Enhanced Arterial Waveform Monitor" to guide precision CPR.
- Research Hypothesis and Objectives The study tests the hypothesis that AI can accurately predict aortic valve compression (confirmed by Transesophageal Echocardiography, TEE) by analyzing arterial pressure waveforms, thereby allowing rescuers to find the optimal compression site that avoids the aortic valve and maximizes cardiac output.
- Implementation Phases The project is divided into five distinct stages: Case Preparation: Enrollment of 150 OHCA patients to collect synchronized TEE video and arterial pressure data. Arterial Waveform Detection Model: Development of an algorithm to automatically segment continuous pressure signals into single-compression waveform samples. Compression Region Detection Model: Training a YOLO v8-based model integrated with patient physiological data (age, sex, medical history) to distinguish between "compressed" and "non-compressed" aortic valve states. Clinical External Testing: Enrolling an additional 75 patients to verify model accuracy against TEE "gold standard" findings. Feasibility Assessment: Deploying the model as a "Resuscitation Support App" in 30 real-world clinical cases to evaluate its real-time guidance capability, speed, and impact on patient outcomes.
- Technical Methodology Data Extraction: Using binarization and interpolation curve fitting to extract high-quality numerical data directly from physiological monitor screens. AI Architecture: Utilizing an improved YOLO v8 framework combined with an Attention-based architecture and Fully-connected neural networks to incorporate complex patient heterogeneities. Clinical Intervention: When the AI identifies aortic valve compression, rescuers will be prompted to adjust the compression location (typically downward and to the left) until the valve is no longer obstructed.
- Outcome Measures The study will evaluate the Identification Success Rate (AI vs. TEE), Avoidance Success Rate (successful repositioning), and traditional resuscitation metrics including ROSC, survival to discharge, and favorable neurologic outcomes.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| OHCA Patients Receiving AI-Enhanced Resuscitation. | – | Device: Device: AI-Enhanced Arterial Waveform Monitor (AIR-CPR App), Procedure: AI-Guided Chest Compression Repositioning, Diagnostic Test: Transesophageal Echocardiography (TEE)Adult patients (20 years or older) with non-traumatic Out-of-Hospital Cardiac Arrest (OHCA) who receive Advanced Life Support (ALS) at the Far Eastern Memorial Hospital Emergency Department. This cohort provides the data for AI training (Years 1-2) and participates in the clinical validation of the AI-guided compression technique (Year 3). |
Who may qualify
- Ages
- 20 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study population consists of adult patients experiencing non-traumatic out-of-hospital cardiac arrest who are transported to the Emergency Department of Far Eastern Memorial Hospital. This facility is the only medical center in New Taipei City and handles approximately 30% of the city's OHCA cases, ensuring a diverse and high-volume sample of patients receiving advanced life support.
Key inclusion criteria (3 listed)
- Adults aged 20 years or older.
- Patients with out-of-hospital cardiac arrest (OHCA) undergoing 3.cardiopulmonary resuscitation (CPR) in the emergency department.
- Cardiac arrest caused by non-traumatic factors.
Key exclusion criteria (9 listed)
- Pregnant patients.
- Patients with obvious signs of death.
- Patients with a signed "Do Not Resuscitate" (DNR) order.
- Patients requiring extracorporeal cardio-pulmonary resuscitation (ECPR).
- Patients requiring Resuscitative Endovascular Balloon Occlusion of the Aorta (REBOA).
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 |
|---|---|---|
| Device: AI-Enhanced Arterial Waveform Monitor (AIR-CPR App)A deep learning application based on the YOLO v8 architecture that analyzes real-time arterial pressure waveforms from a femoral A-line. It identifies whether the current chest compression location is causing aortic valve compression (as confirmed by TEE) and provides immediate feedback to the resuscitation team. | Device | OHCA Patients Receiving AI-Enhanced Resuscitation. |
| AI-Guided Chest Compression RepositioningWhen the AI application indicates aortic valve compression, the rescuer adjusts the mechanical chest compression (LUCAS) position. Based on literature and AI feedback, the adjustment typically involves moving the compression point downward and toward the left parasternal line to avoid the aortic valve and optimize left ventricular output. | Procedure or surgery | OHCA Patients Receiving AI-Enhanced Resuscitation. |
| Transesophageal Echocardiography (TEE)Used as the "Gold Standard" throughout the study. TEE is performed during CPR to record the actual opening and closing of the aortic valve and the deformation of cardiac chambers, providing the labels for AI training and the verification for clinical testing. | Diagnostic test | OHCA Patients Receiving AI-Enhanced Resuscitation. |
Primary outcome
AI Identification Accuracy of Aortic Valve Compression
Time frame: Collected during the clinical testing phase and feasibility assessment (Years 2 and 3).
The accuracy of the AI model in identifying whether the aortic valve is compressed or open during CPR, using Transesophageal Echocardiography (TEE) as the gold standard for verification.
5 secondary outcomes
Successful Avoidance of Aortic Valve Compression
Time frame: During the clinical feasibility assessment (Year 3).
Time Consumed for Compression Adjustment
Time frame: During the clinical feasibility assessment (Year 3).
Rate of Return of Spontaneous Circulation (ROSC)
Time frame: From the start of the emergency department resuscitation until hospital discharge or death (up to approximately 30 days).
Favorable Neurologic Outcome at Discharge
Time frame: At the time of hospital discharge (up to approximately 30 days).
Chest Compression Fraction (CCF)
Time frame: During the clinical feasibility assessment (Year 3).
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 24 Feb 2026.
- Results publicationMiao X, Wu Y, Wang J, Gao Y, Mao X, Yin J. Generative Semi-supervised Learning for Multivariate Time Series Imputation. Proceedings of the AAAI Conference on Artificial Intelligence 2021;35(10):8983-8991. DOI: 10.1609/aaai.v35i10.17086.
- Results publicationSalloum R, Kuo CCJ. ECG-based biometrics using recurrent neural networks. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)2017:2062-2066.
- Results publicationMitra A, Kundu P, Gupta R. Hyperparameter Optimization of AutoRegressive Integrated Moving Average (ARIMA) Model-based Synthesis of Electrocardiogram. 2022 International Conference for Advancement in Technology (ICONAT)2022:1-6.
- Results publicationEmergency Research. Content current as of: 09/21/2015 https://www.fda.gov/science-research/clinical-trials-and-human-subject-protection/protection-human-subjects-informed-consent-and-waiver-informed-consent-requirements-certain.
- Results publicationKarhade J, Dash S, Ghosh SK, Dash DK, Tripathy RK. Time-Frequency-Domain Deep Learning Framework for the Automated Detection of Heart Valve Disorders Using PCG Signals. IEEE Transactions on Instrumentation and Measurement 2022;71:1-11. DOI: 10.1109/TIM.2022.3163156.
- Results publicationZhu Z, Wang H, Zhao T, et al. Classification of Cardiac Abnormalities From ECG Signals Using SE-ResNet. 2020 Computing in Cardiology2020:1-4.
- Results publicationNagasawa T, Iuchi K, Takahashi R, et al. Blood Pressure Estimation by Photoplethysmogram Decomposition into Hyperbolic Secant Waves. Applied Sciences 2022;12(4):1798. (https://www.mdpi.com/2076-3417/12/4/1798).
- Results publication: PubMed 29576810Chirinos JA, Akers SR, Vierendeels JA, Segers P. A Unified Mechanism for the Water Hammer Pulse and Pulsus Bisferiens in Severe Aortic Regurgitation: Insights from Wave Intensity Analysis. Artery Res. 2018 Mar;21:9-12. doi: 10.1016/j.artres.2017.12.002. Epub 2017 Dec 21. No abstract available.
- Results publication: PubMed 21223550Lah K, Krizmaric M, Grmec S. The dynamic pattern of end-tidal carbon dioxide during cardiopulmonary resuscitation: difference between asphyxial cardiac arrest and ventricular fibrillation/pulseless ventricular tachycardia cardiac arrest. Crit Care. 2011;15(1):R13. doi: 10.1186/cc9417. Epub 2011 Jan 11.
- Results publicationEmergency Medical Services Resuscitation Success Statistics, as provided by the Fire Department of the Ministry of the Interior, Taiwan. https://www.nfa.gov.tw/cht/index.php?act=download&ids=14489. 2021.
- Results publication: PubMed 38780398Chu SE, Huang CY, Cheng CY, Chan CH, Chen HA, Chang CH, Tsai KC, Chiu KM, Ma MH, Chiang WC, Sun JT. Cardiopulmonary Resuscitation Without Aortic Valve Compression Increases the Chances of Return of Spontaneous Circulation in Out-of-Hospital Cardiac Arrest: A Prospective Observational Cohort Study. Crit Care Med. 2024 Sep 1;52(9):1367-1379. doi: 10.1097/CCM.0000000000006336. Epub 2024 May 23.
- Results publication: PubMed 9377276Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997 Nov 15;9(8):1735-80. doi: 10.1162/neco.1997.9.8.1735.
- Individual participant dataSharing planned: No
Related trials for Out-of-hospital Cardiac Arrest (OHCA)
Recruiting
Mean Arterial Pressure After Out-of-hospital Cardiac Arrest
Cardiac Arrest, Out-of-hospital Cardiac Arrest (OHCA)
Recruiting
Ventilation Performance and Feedback Simulation Trial
Out-of-hospital Cardiac Arrest (OHCA)
Recruiting
Does Increasing the Compression Pause From 3 to 5 Seconds in Mechanical Compression Devices Increase Ventilation Success Rate and Return of Spontaneous Circulation?
Cardiac Arrest (CA)
Active, not recruitingObservational study
Frontal EEG in OHCA Feasibility Study
Out-Of-Hospital Cardiac Arrest, BIS
RecruitingObservational study
Core Temperature in Patients With OHCA
Out-Of-Hospital Cardiac Arrest, Accidental Hypothermia
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 Out-of-hospital Cardiac Arrest (OHCA) and Cardiopulmonary Resuscitation (CPR), run by Far Eastern Memorial Hospital 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 9 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 24 Feb 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 Sheng-En Chu, physician (886-2-7728-1843, ianchu300@msn.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
AI Identification Accuracy of Aortic Valve Compression (time frame: Collected during the clinical testing phase and feasibility assessment (Years 2 and 3).). 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 NCT07431710, which also lists 49 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 24 Feb 2026.