A novel method based on convolutional neural networks for deriving standard 12-lead ECG from serial 3-lead ECG

  • Lu-di Wang
  • Wei Zhou
  • Ying Xing
  • Na Liu
  • Mahmood Movahedipour
  • Xiao-guang ZhouEmail author


Reconstruction of a 12-lead electrocardiogram (ECG) from a serial 3-lead ECG has been researched in the past to satisfy the need for more wearing comfort and ambulatory situations. The accuracy and real-time performance of traditional methods need to be improved. In this study, we present a novel method based on convolutional neural networks (CNNs) for the synthesis of missing precordial leads. The results show that the proposed method receives better similarity and consumes less time using the PTB database. Particularly, the presented method shows outstanding performance in reconstructing the pathological ECG signal, which is crucial for cardiac diagnosis. Our CNN-based method is shown to be more accurate and time-saving for deployment in non-hospital situations to synthesize a standard 12-lead ECG from a reduced lead-set ECG recording. This is promising for real cardiac care.

Key words

Convolutional neural networks (CNNs) Electrocardiogram (ECG) synthesis E-health 

CLC number

TP18 R540.4+1 


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Copyright information

© Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Automation SchoolBeijing University of Posts and TelecommunicationsBeijingChina
  2. 2.Department of NeuroscienceUppsala UniversityUppsalaSweden
  3. 3.School of Economic and ManagementBeijing University of Posts and TelecommunicationsBeijingChina
  4. 4.Academic Center for Education, Culture and Research (ACECR)TehranIran

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