Automatic diagnosis of the 12-lead ECG using a deep neural network

Abstract

The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples. This technology has recently achieved striking success in a variety of task and there are great expectations on how it might improve clinical practice. Here we present a DNN model trained in a dataset with more than 2 million labeled exams analyzed by the Telehealth Network of Minas Gerais and collected under the scope of the CODE (Clinical Outcomes in Digital Electrocardiology) study. The DNN outperform cardiology resident medical doctors in recognizing 6 types of abnormalities in 12-lead ECG recordings, with F1 scores above 80% and specificity over 99%. These results indicate ECG analysis based on DNNs, previously studied in a single-lead setup, generalizes well to 12-lead exams, taking the technology closer to the standard clinical practice.

A preliminary version of this work titled: "Automatic Diagnosis of Short-Duration 12-Lead ECG using a Deep Convolutional Network " was presented in the Machine Learning for Health Workshop at NeurIPS 2018 and was made available under a different identifier: arXiv:1811.12194. The current version subsumes all previous versions

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