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

Author

Listed:
  • Antônio H. Ribeiro

    (Universidade Federal de Minas Gerais
    Uppsala University)

  • Manoel Horta Ribeiro

    (Universidade Federal de Minas Gerais)

  • Gabriela M. M. Paixão

    (Universidade Federal de Minas Gerais
    Telehealth Center from Hospital das Clínicas da Universidade Federal de Minas Gerais)

  • Derick M. Oliveira

    (Universidade Federal de Minas Gerais)

  • Paulo R. Gomes

    (Universidade Federal de Minas Gerais
    Telehealth Center from Hospital das Clínicas da Universidade Federal de Minas Gerais)

  • Jéssica A. Canazart

    (Universidade Federal de Minas Gerais
    Telehealth Center from Hospital das Clínicas da 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 Jr.

    (Universidade Federal de Minas Gerais)

  • Thomas B. Schön

    (Uppsala University)

  • Antonio Luiz P. Ribeiro

    (Universidade Federal de Minas Gerais
    Telehealth Center from Hospital das Clínicas da Universidade Federal de Minas Gerais)

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.

Suggested Citation

  • 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, 2020. "Automatic diagnosis of the 12-lead ECG using a deep neural network," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-15432-4
    DOI: 10.1038/s41467-020-15432-4
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    Cited by:

    1. Veer Sangha & Bobak J. Mortazavi & Adrian D. Haimovich & Antônio H. Ribeiro & Cynthia A. Brandt & Daniel L. Jacoby & Wade L. Schulz & Harlan M. Krumholz & Antonio Luiz P. Ribeiro & Rohan Khera, 2022. "Automated multilabel diagnosis on electrocardiographic images and signals," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    2. Christian Bock & Joan Elias Walter & Bastian Rieck & Ivo Strebel & Klara Rumora & Ibrahim Schaefer & Michael J. Zellweger & Karsten Borgwardt & Christian Müller, 2024. "Enhancing the diagnosis of functionally relevant coronary artery disease with machine learning," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    3. Jintai Chen & Shuai Huang & Ying Zhang & Qing Chang & Yixiao Zhang & Dantong Li & Jia Qiu & Lianting Hu & Xiaoting Peng & Yunmei Du & Yunfei Gao & Danny Z. Chen & Abdelouahab Bellou & Jian Wu & Huiyin, 2024. "Congenital heart disease detection by pediatric electrocardiogram based deep learning integrated with human concepts," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    4. Shuaijian Yang & Jinhao Cheng & Jin Shang & Chen Hang & Jie Qi & Leni Zhong & Qingyan Rao & Lei He & Chenqi Liu & Li Ding & Mingming Zhang & Samit Chakrabarty & Xingyu Jiang, 2023. "Stretchable surface electromyography electrode array patch for tendon location and muscle injury prevention," Nature Communications, Nature, vol. 14(1), pages 1-16, December.

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