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.

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.

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

This is one-center cohort study. Patients undergo echocardiogram at Ruijin Hospital.