A Two-Stage Feature Extraction Approach for ECG Signals

  • Essam H. HousseinEmail author
  • Moataz Kilany
  • Aboul Ella Hassanien
  • Vaclav Snasel
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 565)


This paper investigate various techniques of extracting features from the electrocardiogram (ECG) signal in order to analyze the ECG signals to detect the heart disease. Feature extraction, is a one of the widespread process of decompose the ECG data. This paper introduce a two-stage feature extraction approach to extract features from ECG signals for different types of arrhythmias. Firstly, Modified Pan-Tomkins Algorithm (MPTA) is implemented to remove noise and extract nine features. Then the proposed Improved Feature Extraction Algorithm (IFEA) is applied to extract additionally ten different features from the ECG signal. The MIT-BIH arrhythmia database have been used to test the proposed approach. It is obvious from the results that the proposed approach shows a high classification in terms of the following four statistical measures: Accuracy (Ac) 98.37%, Recall 48.29%, Precision 43.91%, F Measure 45.31%, and Specificity (Sp) 93.30%, respectively.


ECG Feature extraction Wavelet transform Pan-Tompkins Algorithm 


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Essam H. Houssein
    • 1
    • 4
    Email author
  • Moataz Kilany
    • 1
    • 4
  • Aboul Ella Hassanien
    • 2
    • 4
  • Vaclav Snasel
    • 3
  1. 1.Faculty of Computers and InformationMinia UniversityMinyaEgypt
  2. 2.Faculty of Computers and InformationCairo UniversityCairoEgypt
  3. 3.Department of CS and IT4InnovationsVSB-TU of OstravaOstravaCzech Republic
  4. 4.Scientific Research Group in Egypt (SRGE)CairoEgypt

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