Deep Learning Predicts Cardiac Output from Seismocardiographic Signals in Heart Failure.

ABSTRACT: 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. To explore this potential, we developed and evaluated a deep learning model for estimating CO directly from SCG, electrocardiogram (ECG), and body mass index (BMI) in heart failure patients undergoing RHC. We trained a deep convolutional neural network for CO estimation using an open-access dataset comprising 73 heart fa

SUBMITTER: Wang J 

PROVIDER: S-EPMC12821336 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature