Automatic Triage of 12-Lead ECGs Using Deep Convolutional Neural Networks.

ABSTRACT: BACKGROUND The correct interpretation of the ECG is pivotal for the accurate diagnosis of many cardiac abnormalities, and conventional computerized interpretation has not been able to reach physician-level accuracy in detecting (acute) cardiac abnormalities. This study aims to develop and validate a deep neural network for comprehensive automated ECG triage in daily practice. METHODS AND RESULTS We developed a 37-layer convolutional residual deep neural network on a data set of free-text physician-annotated 12-lead ECGs. The deep neural network was trained on a data set with 336.835 recordings from 142.040 patients and validated on an independent validation data set (n=984), annotated by a panel of 5 cardiologists electrophysiologists. The 12-lead ECGs were acquired in all noncardiology de

SUBMITTER: van de Leur RR 

PROVIDER: S-EPMC7660886 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature