Application of an Improved Fisher Criteria in Feature Extraction of Similar ECG Patterns

  • Ding-fei Ge
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7390)


The abnormal changes of myocardial infarction (MI) appeared in Electrocardiogram (ECG) are low-level signals. The patterns to represent MI ECGs are usually extremely similar between different classes. In addition, the using of conventional 12-lead ECG generates large amounts of time-series data. Conventional Linear (Fisher) discriminant analysis (LDA) faces the problems of singular matrix and limited number of the extracted features. An improved Fisher criteria (IFC) based method was employed to discriminate ECG’s in current study. The singular matrix problem could be overcome, and more features could be extracted at the same time. The data in the analysis including healthy control (HC), MI in early stage (MIES) and acute MI (AMI) were collected from PTB diagnostic ECG database. The results show that the proposed method can obtain more effective features, and classification accuracy based on IFC can be improved than that of conventional LDA based method.


Myocardial infarction ECG Feature extraction Hyperdimensional data Classification 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Joo, T.H., Schmitt, P.W., Hampton, D.R.: Enhanced Acute Myocardial Infarction Detection Algorithm using Local and Global Signal Morphology. Computer in Cardiology 25(12), 285–288 (1998)Google Scholar
  2. 2.
    Eggers, K.M., Johan, E., Mikael, D.: Artificial Neural Network Algorithms for Early Diagnosis of Acute Myocardial Infarction and Prediction of Infarct Size in Chest Pain pa-tients. International Journal of Cardiology 114(3), 366–374 (2007)CrossRefGoogle Scholar
  3. 3.
    Lehmann, G., Schmitt, C., Schmitt, V.: Electrocardiographic Algorithm for Assignment of Occluded Vessel in Acute Myocardial Infarction. International Journal of Cardiology 89(1), 79–85 (2003)CrossRefGoogle Scholar
  4. 4.
    Willems, J.L.: The diagnostic Performance of Computer Programs for The Interpretation of Electrocardiograms. New England Journal of Medicine 325(25), 1767–1773 (1991)CrossRefGoogle Scholar
  5. 5.
    Sadao, F., Senya, K.: Application of Feature Extraction Scheme to The Discrimination of Electrocardiogram. T. IEE Japan 121-A(8), 725–730 (2001)Google Scholar
  6. 6.
    Kuo, B.C., Landgrebe, D.A.: Nonparametric Weighted Feature Extraction for Classification. Geoscience and Remote Sensing 42(5), 1096–1105 (2004)CrossRefGoogle Scholar
  7. 7.
    Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press Limited, United States of America (1990)zbMATHGoogle Scholar
  8. 8.
    Tompkins, W.: Biomedical Digital Signal Processing. Prentice Hall, New Jersey (1993)Google Scholar
  9. 9.
    Ge, D.F., Sun, L.H., Zhou, J.: Discrimination of Myocardial Infarction Stages by Subjective Feature Extraction. Computer Methods and Programms in Biomedicine 95(3), 270–279 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ding-fei Ge
    • 1
  1. 1.School of Information and Electronic EngineeringZhejiang University of Science and TechnologyHangzhouChina

Personalised recommendations