Electronic Science and Technology ›› 2022, Vol. 35 ›› Issue (3): 45-50.doi: 10.16180/j.cnki.issn1007-7820.2022.03.007

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Arrhythmia Recognition Based on GAN-CNN

Peng CHEN,Zilong LIU   

  1. School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China
  • Received:2020-10-25 Online:2022-03-15 Published:2022-04-02
  • Supported by:
    National Natural Science Foundation of China(61573246)


ECG analysis is an important basis for doctors to diagnose arrhythmia. The judgment of arrhythmia helps patients understand their physical conditions in time and find potential diseases. However, ECG analysis is not only time-consuming and labor-intensive, but also relies on clinical experience. Therefore, the efficiency of ECG analysis has always been limited by the number of doctors and work efficiency. The development of deep learning technology provides a foundation for the development of computer-aided diagnosis systems. In this study, a one-dimensional ECG signal is converted into a two-dimensional gray image, and a GAN-CNN network is used to solve the problem of ECG data imbalance, which can simultaneously realize the recognition of 7 types of arrhythmia and normal heartbeat. The experiment is verified by the MIT-BIH arrhythmia database. The average accuracy rate reaches 99.32%, and the sensitivity and specificity are 99.69% and 98.91%, respectively.

Key words: electrocardiogram, arrhythmia, deep learning, auxiliary diagnosis, electrocardiograph signal, GAN-CNN, two-dimensional image, unbalanced electrocardiogram data

CLC Number: 

  • TP183