Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (2): 608-615.DOI: 10.11772/j.issn.1001-9081.2019071172

• Frontier & interdisciplinary applications • Previous Articles     Next Articles

Auxiliary diagnosis method of myocardial infarction based on fusion of statistical features and entropy features

Zhizhong WANG1, Longlong QIAN1, Chuang HAN1, Li SHI2()   

  1. 1.College of Electrical Engineering,Zhengzhou University,Zhengzhou Henan 450000,China
    2.Department of Automation,Tsinghua University,Beijing 100000,China
  • Received:2019-07-08 Revised:2019-08-17 Accepted:2019-08-27 Online:2019-09-11 Published:2020-02-10
  • Contact: Li SHI
  • About author:WANG Zhizhong, born in 1982, Ph. D., associate professor. His research interests include biological signal detection and processing.
    QIAN Longlong, born in 1993, M. S. candidate. His research interests include ECG signal analysis, intelligent diagnosis.
    HAN Chuang, born in 1991, Ph. D. candidate. His research interests include ECG signal analysis, intelligent diagnosis.
  • Supported by:
    the National Natural Science Foundation of China(61673353);the National Natural Science Foundation of China Young Scientists(61603344);the Henan Province Higher Education Key Research Project(15A120017)


王治忠1, 钱龙龙1, 韩闯1, 师丽2()   

  1. 1.郑州大学 电气工程学院,郑州 450000
    2.清华大学 自动化系,北京 100000
  • 通讯作者: 师丽
  • 作者简介:王治忠(1982—),男,山东蓬莱人,副教授,博士,主要研究方向:生物信号检测与处理
  • 基金资助:


Aiming at the problem of low clinical practicability and accuracy in the clinical diagnosis of myocardial infarction, an auxiliary diagnosis method of myocardial infarction based on 12-lead ElectroCardioGram (ECG) signal was proposed. Firstly, denoising and data enhancement were performed on the 12-lead ECG signals. Secondly, aiming at the ECG signals of each lead, the statistical features including standard deviation, kurtosis coefficient and skewness coefficient were extracted respectively to reflect the morphological characteristics of ECG signals, meanwhile the entropy features including Shannon entropy, sample entropy, fuzzy entropy, approximate entropy and permutation entropy were extracted to characterize the time and frequency spectrum complexity, the new mode generation probability, the regularity and the unpredictability of the ECG signal time series as well as detect the small changes of ECG signals. Thirdly, the statistical features and entropy features of ECG signals were fused. Finally, based on the random forest algorithm, the performance of algorithm was analyzed and verified in both intra-patient and inter-patient modes, and the cross-validation technology was used to avoid over-fitting. Experimental results show that, the accuracy and F1 value of the proposed method in the intra-patient modes are 99.98% and 99.99% respectively, the accuracy and F1 value of the proposed method in the inter-patient mode are 94.56% and 97.05% respectively; and compared with the detection method based on single-lead ECG, the detection of myocardial infarction with 12-lead ECG is more logical for doctors’ clinical diagnosis.

Key words: myocardial infarction, statistical feature, entropy feature, random forest algorithm, cross-validation, 12-lead ElectroCardioGram (ECG)



关键词: 心肌梗死, 统计特征, 熵特征, 随机森林算法, 交叉验证, 12导联心电图

CLC Number: