IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-39472-8.html
   My bibliography  Save this article

Practical intelligent diagnostic algorithm for wearable 12-lead ECG via self-supervised learning on large-scale dataset

Author

Listed:
  • Jiewei Lai

    (Southern Medical University
    Guangdong Provincial Key Laboratory of Medical Image Processing
    Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology)

  • Huixin Tan

    (Southern Medical University
    Guangdong Provincial Key Laboratory of Medical Image Processing
    Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology)

  • Jinliang Wang

    (CardioCloud Medical Technology (Beijing) Co., Ltd.)

  • Lei Ji

    (Chinese PLA General Hospital)

  • Jun Guo

    (Chinese PLA General Hospital)

  • Baoshi Han

    (Chinese PLA General Hospital)

  • Yajun Shi

    (Chinese PLA General Hospital)

  • Qianjin Feng

    (Southern Medical University
    Guangdong Provincial Key Laboratory of Medical Image Processing
    Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology)

  • Wei Yang

    (Southern Medical University
    Guangdong Provincial Key Laboratory of Medical Image Processing
    Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology)

Abstract

Cardiovascular disease is a major global public health problem, and intelligent diagnostic approaches play an increasingly important role in the analysis of electrocardiograms (ECGs). Convenient wearable ECG devices enable the detection of transient arrhythmias and improve patient health by making it possible to seek intervention during continuous monitoring. We collected 658,486 wearable 12-lead ECGs, among which 164,538 were annotated, and the remaining 493,948 were without diagnostic. We present four data augmentation operations and a self-supervised learning classification framework that can recognize 60 ECG diagnostic terms. Our model achieves an average area under the receiver-operating characteristic curve (AUROC) and average F1 score on the offline test of 0.975 and 0.575. The average sensitivity, specificity and F1-score during the 2-month online test are 0.736, 0.954 and 0.468, respectively. This approach offers real-time intelligent diagnosis, and detects abnormal segments in long-term ECG monitoring in the clinical setting for further diagnosis by cardiologists.

Suggested Citation

  • Jiewei Lai & Huixin Tan & Jinliang Wang & Lei Ji & Jun Guo & Baoshi Han & Yajun Shi & Qianjin Feng & Wei Yang, 2023. "Practical intelligent diagnostic algorithm for wearable 12-lead ECG via self-supervised learning on large-scale dataset," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39472-8
    DOI: 10.1038/s41467-023-39472-8
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-39472-8
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-023-39472-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Varun Gupta & Nitin Kumar Saxena & Abhas Kanungo & Anmol Gupta & Parvin Kumar & Salim, 2022. "A review of different ECG classification/detection techniques for improved medical applications," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1037-1051, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      More about this item

      Statistics

      Access and download statistics

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39472-8. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.