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Identifying Individuals Using Fourier and Discriminant Analysis of Electrocardiogram

  • Ranjeet Srivastva
  • Yogendra Narain Singh
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 834)

Abstract

From the last one and a half decades, the electrocardiogram (ECG) has emerged as a new modality for human identification. The research shows that the people heartbeats recorded using diagnostic method called ECG exhibit discriminatory features that can distinguish themselves. The ECG as a biometric inherently provides liveness detection and robustness against falsification. This paper presents a novel method of ECG analysis for human identification using Fourier and linear discriminant analysis, which does not require detection of fiducial points of ECG wave. The method utilizes autocorrelation coefficients of filtered ECG signal, to extract significant features of it. The performance of the proposed method is evaluated on MIT-BIH arrhythmia and QT database of physionet. The experimental results show the equal error rate (EER) of 0.17% and 0.03% on MIT-BIH arrhythmia and QT database, respectively that outperform the other methods on these databases.

Keywords

Individual identification Electrocardiogram Fourier transform Discriminant analysis 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Information TechnologyBabu Banarasi Das Northern India Institute of TechnologyLucknowIndia
  2. 2.Department of Computer Science and EngineeringInstitute of Engineering and TechnologyLucknowIndia

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