Fiducial feature reduction analysis for electrocardiogram (ECG) based biometric recognition

  • Authors:

  • M. M. Tantawi;K. Revett;A. Salem;M. F. Tolba

  • Affiliations:

  • Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt;Faculty of Informatics and Computer Science, The British University in Egypt, El Sherouk City, Egypt;Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt;Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

  • Venue:
  • Journal of Intelligent Information Systems
  • Year:
  • 2013

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Abstract

Although the electrocardiogram (ECG) has been a reliable diagnostic tool for decades, its deployment in the context of biometrics is relatively recent. Its robustness to falsification, the evidence it carries about aliveness and its rich feature space has rendered the deployment of ECG based biometrics an interesting prospect. The rich feature space contains fiducial based information such as characteristic peaks which reflect the underlying physiological properties of the heart. The principal goal of this study is to quantitatively evaluate the information content of the fiducial based feature set in terms of their effect on subject and heart beat classification accuracy (ECG data acquired from the PhysioNet ECG repository). To this end, a comprehensive set of fiducial based features was extracted from a collection of ECG records. This feature set was subsequently reduced using a variety of feature extraction/selection methods such as principle component analysis (PCA), linear discriminant analysis (LDA), information-gain ratio (IGR), and rough sets (in conjunction with the PASH algorithm). The performance of the reduced feature set was examined and the results evaluated with respect to the full feature set in terms of the overall classification accuracy and false (acceptance/rejection) ratios (FAR/FRR). The results of this study indicate that the PASH algorithm, deployed within the context of rough sets, reduced the dimensionality of the feature space maximally, while maintaining maximal classification accuracy.