Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (6A): 240900073-8.doi: 10.11896/jsjkx.240900073

• Intelligent Medical Engineering • Previous Articles     Next Articles

Research on Electrocardiogram Classification and Recognition Algorithm Based on Transfer Learning

CHEN Qirui1, WANG Baohui1, DAI Chencheng2   

  1. 1 College of Software,Beihang University,Beijing 100191,China
    2 Beijing Anzhen Hospital,Capital Medical University,Beijing 100013,China
  • Online:2025-06-16 Published:2025-06-12
  • About author:DAI Chencheng,born in 1979,Ph.D,Chief Physician,children’s arrhythmia,Beijing anzhen hospital affiliated to ca-pital medical university.

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Abstract: As the pace of urban life continues to accelerate,more and more people are troubled by cardiovascular diseases.Electrocardiogram is a key means of diagnosing heart disease,but in the faced of the growing number of patients,limited medical resources cannot meet the huge demand for electrocardiogram interpretation.Therefore,how to use computers to automatically classify and identify electrocardiograms has become an urgent need.This study is based on the clinical data set provided by Anzhen hospital.According to statistics,there are problems in the data set such as small total data,uneven data distribution,and some data are not labeled.Based on this,this paper uses a semi-supervised learning method to label the unlabeled data,and the algorithm labeling accuracy reaches 91.4%.Secondly,this paper uses transfer learning to train the model.The MMD value of the source data set and the target data set used in this paper is 1.99,and the distribution of the two has a high similarity.Compared with other training methods,this algorithm can achieve better learning results on data sets with a small total amount of data and uneven data distribution; on the actual outpatient data set,our method makes model’s accuracy reach 0.973,recall rate reaches 0.866,and F1 value reaches 0.932.Compared with not using transfer learning,accuracy is improved by 0.423,recall rate is improved by 0.274,and F1 value is improved by 0.384.This results show that the algorithm has good generalization ability and adaptability,and can provide strong support for clinical practice.

Key words: ECG, Transfer learning, Semi-supervised learning

CLC Number: 

  • TP312
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