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

Antônio H. Ribeiro (), Manoel Horta Ribeiro, Gabriela M. M. Paixão, Derick M. Oliveira, Paulo R. Gomes, Jéssica A. Canazart, Milton P. S. Ferreira, Carl R. Andersson, Peter W. Macfarlane, Wagner Meira, Thomas B. Schön () and Antonio Luiz P. Ribeiro ()
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Antônio H. Ribeiro: Universidade Federal de Minas Gerais
Manoel Horta Ribeiro: Universidade Federal de Minas Gerais
Gabriela M. M. Paixão: Universidade Federal de Minas Gerais
Derick M. Oliveira: Universidade Federal de Minas Gerais
Paulo R. Gomes: Universidade Federal de Minas Gerais
Jéssica A. Canazart: Universidade Federal de Minas Gerais
Milton P. S. Ferreira: Universidade Federal de Minas Gerais
Carl R. Andersson: Uppsala University
Peter W. Macfarlane: University of Glasgow
Wagner Meira: Universidade Federal de Minas Gerais
Thomas B. Schön: Uppsala University
Antonio Luiz P. Ribeiro: Universidade Federal de Minas Gerais

Nature Communications, 2020, vol. 11, issue 1, 1-9

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-15432-4

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