Deep learning algorithm for detection of acute heart failure using standard ECG waveforms.

ABSTRACT:

Aims

To develop and evaluate a deep learning model for immediate and accurate diagnosis of acute heart failure(HF) using standard 12-lead electrocardiogram(ECG) waveforms collected from a large cohort of patients.

Methods and results

We retrospectively analysed patients aged > 18 years who underwent transthoracic echocardiogram, n-terminal pro-B type natriuretic peptide (NT-proBNP) evaluation, and ECG within one week of clinical diagnosis at Samsung Medical from 1 February 2011 and 31 December 2022. The cohort included 1949 acute HF patients and a control group of 24 603 patients with normal NT-proBNP levels and no significant cardiac dysfunction. Four deep learning models (1D-CNN-Res, 1D-CNN-Dense, CRT-Net without transformer, CRT-Net) and their ensemble were develop

SUBMITTER: Lee SM 

PROVIDER: S-EPMC12853116 | biostudies-literature | 2026 Mar

REPOSITORIES: biostudies-literature