Artificial Intelligence (AI) Analysis of Synchronized Phonocardiography (PCG) and Electrocardiogram(ECG)
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Heart Failure. Basic parameters: 18 years — 100 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
A Deep-learning-based Multi-modal Phonocardiogram(PCG) and Electrocardiogram(ECG) Processing Framework for Screening Depressed Left Ventricular Ejection Fraction (dLVEF) Using a Wearable Cardiac Patch
Overview
The diagnosis of depressed left ventricular ejection fraction (dLVEF) (EF\<50%) depends on golden standard ultrasound cardiography (UCG). A wearable synchronized phonocardiography (PCG) and electrocardiogram (ECG) device can assist in the diagnosis of dLVEF, which can both expedite access to life-saving therapies and reduce the need for costly testing.
Detailed description
The synchronized PCG and ECG is wirelessly paired with the WenXin Mobile application, allowing for simultaneous recording and visualization of PCG and ECG. These features uniquely enable this device to accumulate large sets of acoustic data on patients both with and without heart failure(HF).
This study is a Case-control study. In this study, the investigators seek to develop an artificial intelligence (AI) analysis system to identify dLVEF (EF\<50%) by PCG and ECG. All adults (aged ≥18 years) planned for UCG were eligible to participate (inpatients and outpatients). Specifically, the investigators will attempt to develop machine learning algorithms to learn synchronized PCG and ECG of patients with dLVEF. Then we use these algorithms to identify dLVEF subjects. The investigators anticipate to demonstrate the wearable cardiac patch with synchronized PCG and ECG can reliably and accurately diagnose dLVEF in the primary care setting.
Primary outcome measures
- Determination of Heart Failure Disease [Time frame: one time assessment at baseline (approx. 5 minutes)]
Eligibility criteria
Inclusion criteria
- Attendance at RuiJin hospital for UCG
- Signed dated informed consent
- Commit to follow the research procedures and cooperate in the implementation of the whole process research
- UCG has been completed
- Age ≥ 18
- At least 8 consecutive cycles of sinus rhythm can be recorded
Exclusion criteria
- Patients with pacemakers
- Complete left bundle branch block or block or QRS wave widening>120ms
- Left chest skin damaged or allergic to patch
- Refusal to participate
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Case-control
Study locations
China · 3 centers
- Ruijin Hospital, Shanghai Jiaotong School of Medicine — Shanghai
- Shanghai Chest Hospital, Shanghai Jiao Tong University School Of Medicine — Shanghai
- Shanghai East Hospital — Shanghai
Identifiers
NCT: NCT06009718 · RJH-PEG