Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network.

ABSTRACT: Computerized electrocardiogram (ECG) interpretation plays a critical role in the clinical ECG workflow¹. Widely available digital ECG data and the algorithmic paradigm of deep learning² present an opportunity to substantially improve the accuracy and scalability of automated ECG analysis. However, a comprehensive evaluation of an end-to-end deep learning approach for ECG analysis across a wide variety of diagnostic classes has not been previously reported. Here, we develop a deep neural network (DNN) to classify 12 rhythm classes using 91,232 single-lead ECGs from 53,549 patients who used a single-lead ambulatory ECG monitoring device. When validated against an independent test dataset annotated by a consensus committee of board-certified practicing cardiologists, the

SUBMITTER: Hannun AY 

PROVIDER: S-EPMC6784839 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature