Deep Learning Algorithm for Detection of Acute Heart Failure Using Standard ECG Waveforms

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 We retrospectively analyzed 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 February 1, 2011 and December 31, 2022. The cohort included 1,949 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 developed using an 8:2 stratified split, ensuring no patient overlap. An external validation was performed using MIMIC-IV dataset, which comprised 7,868 acute HF patients and 16,025 controls. The performance was evaluated using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score. Results The ensemble model demonstrated the best diagnostic performance with an AUROC of 0.997 and F1-score of 0.649 and the external validation showed AUROC of 0.842 and F1-score of 0.640. Notably, F1-score indicated diagnostic performance across a diverse range of ejection fraction values and demographic subgroups. Post-hoc analysis of false-positive cases revealed underlying cardiovascular risks, highlighting the model’s utility in identifying high-risk patients. Conclusions The proposed deep learning models demonstrated remarkable performance in diagnosing acute HF. These findings support its potential utility in facilitating early diagnosis and improving clinical outcomes.