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ECG Classification Using ICA Features and Support Vector Machines

  • Yang Wu
  • Liqing Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7062)

Abstract

Classification accuracy is vital in practical application of automatic ECG diagnostics. This paper aims at enhancing accuracy of ECG signals classification. We propose a statistical method to segment heartbeats from ECG signal as precisely as possible, and use the combination of independent component analysis (ICA) features and temporal feature to describe multi-lead ECG signals. To obtain the most discriminant features of different class, we introduce the minimal-redundancy-maximal-relevance feature selection method. Finally, we designed a vote strategy to make the decision from different classifiers. We test our method on the MIT-BIT Arrhythmia Database, achieving a high accuracy.

Keywords

ECG segmentation ICA feature extraction SVM 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yang Wu
    • 1
  • Liqing Zhang
    • 1
  1. 1.Department of Computer Science and EngineeringShanghai Jiaotong UniversityShanghaiChina

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