AI-Optimized Single-Feature Recognition Model for Heart Failure

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.

Recruiting

Interested in participating?

Request Info

Key information

Conditions

Cardiac Sound Heart Failure - NYHA II - IV

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Second Affiliated Hospital, School of Medicine, Zhejiang University

Hangzhou, Zhejiang, China

Location status: Recruiting

Location contact

Bing Yang

CONTACT

[email protected]

+86 13395719536

Xiaojie Xie

PRINCIPAL_INVESTIGATOR

Who can participate

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Primary outcomes

Heart sounds include S1 and S2

Time frame: 10-15 minutes

In some patients, S3 and S4 are also present.

Sponsors and collaborators

Lead sponsor

Vivalink

Industry

Collaborators

Second Affiliated Hospital, School of Medicine, Zhejiang University

Registry information

Official study title

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

Important dates

Study start: 2026
Primary completion: 2027
Study completion: 2027
First posted: Jun 25, 2026
Registry last updated: Jul 21, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Recruiting