Deep learning of ECG waveforms for diagnosis of heart failure with a reduced left ventricular ejection fraction.

ABSTRACT: The performance and clinical implications of the deep learning aided algorithm using electrocardiogram of heart failure (HF) with reduced ejection fraction (DeepECG-HFrEF) were evaluated in patients with acute HF. The DeepECG-HFrEF algorithm was trained to identify left ventricular systolic dysfunction (LVSD), defined by an ejection fraction (EF) < 40%. Symptomatic HF patients admitted at Seoul National University Hospital between 2011 and 2014 were included. The performance of DeepECG-HFrEF was determined using the area under the receiver operating characteristic curve (AUC) values. The 5-year mortality according to DeepECG-HFrEF results was analyzed using the Kaplan-Meier method. A total of 690 patients contributing 18,449 ECGs were included with final 1291 ECGs eligible for the study (m

SUBMITTER: Choi J 

PROVIDER: S-EPMC9392508 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature