Artificial Intelligence (AI) Analysis of Synchronized Phonocardiography (PCG) and Electrocardiogram(ECG)

Study Overview
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Brief Summary
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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
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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.

Official Title
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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

Conditions
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Heart Failure

Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 100 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Model training group<br>Compare the results of PCG and ECG with UCG, and conduct model training analysis | |
| Model validation group<br>Compare the results of PCG and ECG with UCG, and conduct model validation analysis | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Determination of Heart Failure Disease | Heart Failure Disease was determined by EMAT （millisecond, ms）calculate from synchronized PCG and ECG signals using an artificial intelligence (AI) guided model. | one time assessment at baseline (approx. 5 minutes) | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Phonocardiography (PCG), Electrocardiogram (ECG), Heart Failure (HF), depressed left ventricular ejection fraction (dLVEF)