Deep learning predicts cardiac output from seismocardiographic signals in heart failure.

ABSTRACT:

Background

Determination of cardiac output (CO) is essential to the clinical management of cardiovascular compromise. However, the invasiveness, procedural risks, and reliance on specialized infrastructure limit accessibility and scalability of standard-of-care right heart catheterization (RHC). Seismocardiography (SCG), a non-invasive technique which records subtle chest wall vibrations generated by cardiac mechanical activity, may offer a promising alternative for CO determination.

Objectives

To develop and evaluate a deep learning model for estimating CO directly from SCG, electrocardiogram (ECG), and body mass index (BMI) in heart failure patients undergoing RHC.

Methods

We trained a deep convolutional neural network for CO estimation using an open-access dataset

SUBMITTER: Wang J 

PROVIDER: S-EPMC12338910 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature