Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
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)
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)
Willems, J.L.: The diagnostic Performance of Computer Programs for The Interpretation of Electrocardiograms. New England Journal of Medicine 325(25), 1767–1773 (1991)
Sadao, F., Senya, K.: Application of Feature Extraction Scheme to The Discrimination of Electrocardiogram. T. IEE Japan 121-A(8), 725–730 (2001)
Kuo, B.C., Landgrebe, D.A.: Nonparametric Weighted Feature Extraction for Classification. Geoscience and Remote Sensing 42(5), 1096–1105 (2004)
Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press Limited, United States of America (1990)
Tompkins, W.: Biomedical Digital Signal Processing. Prentice Hall, New Jersey (1993)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ge, Df. (2012). Application of an Improved Fisher Criteria in Feature Extraction of Similar ECG Patterns. In: Huang, DS., Ma, J., Jo, KH., Gromiha, M.M. (eds) Intelligent Computing Theories and Applications. ICIC 2012. Lecture Notes in Computer Science(), vol 7390. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31576-3_46
Download citation
DOI: https://doi.org/10.1007/978-3-642-31576-3_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31575-6
Online ISBN: 978-3-642-31576-3
eBook Packages: Computer ScienceComputer Science (R0)