AI-Optimized Single-Feature Recognition Model for Heart Failure

Development and Application of a Single-Feature Recognition Model for Heart Failure With Artificial Intelligence-Optimized Algorithms

This prospective, single-center and observational study aims to develop and validate the single-feature artificial intelligence algorithm based on data collected via the wearable ECG patches in patients with heart failure (HF).

The main question: Does the algorithm, using synchronized ECG and accelerometer signals from the ECG patches, achieve accurate detection of heart sounds (S1, S2, and in some patients S3, S4) compared with the Eko CORE 500 digital stethoscope in patients with acute exacerbation of HF? It aims to answer: Participants with confirmed HF (NYHA classification II-IV) will first undergo a 2-minute session of simultaneous ECG patches and digital stethoscope recordings, followed by standard 12-lead ECG, and then the repeated ECG patches and 2-minute heart sound recording session. Data will be used for algorithm training and validation. The primary endpoint is the accuracy of heart sound detection via the Vivalink ECG patches compared with the Eko CORE 500 digital stethoscope.

Inclusion Criteria:

• Age ≥ 18 years old;
• Body mass index (BMI) < 35 kg/m²;
• Diagnosed with heart failure: according to "Chinese Guidelines for Diagnosis and Treatment of Heart Failure in 2024", "ESC Guidelines for Diagnosis and Treatment of Acute and Chronic Heart Failure in 2021", and "AHA/ACC/HFSA Guidelines for Management of Heart Failure in 2022";
• NYHA classification II - IV;
• Able to fully understand the purpose and process of the trial, and willing to sign the informed consent form.

Exclusion Criteria:

• Physical disabilities that prevent safe and thorough testing;
• Open wounds on the chest or the skin is allergic to the patches;
• Large amount of pericardial effusion, pericardial tamponade, pleural friction rub, pneumothorax, and a large amount of pleural effusion, which may affect data collection;
• Patients with severe comorbidities or unstable conditions, which may interfere with data collection during the study period;
• Other situations where the investigator believes the subject is not suitable to participate in this trial, such as those that may increase trial risk, affect the protocol compliance, or impair the subject's ability to complete the trial due to physical or psychological diseases or conditions.