Normalizing electrocardiograms of both healthy persons and cardiovascular disease patients for biometric authentication

PLoS One. 2013 Aug 20;8(8):e71523. doi: 10.1371/journal.pone.0071523. eCollection 2013.


Although electrocardiogram (ECG) fluctuates over time and physical activity, some of its intrinsic measurements serve well as biometric features. Considering its constant availability and difficulty in being faked, the ECG signal is becoming a promising factor for biometric authentication. The majority of the currently available algorithms only work well on healthy participants. A novel normalization and interpolation algorithm is proposed to convert an ECG signal into multiple template cycles, which are comparable between any two ECGs, no matter the sampling rates or health status. The overall accuracies reach 100% and 90.11% for healthy participants and cardiovascular disease (CVD) patients, respectively.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Biometric Identification*
  • Cardiovascular Diseases / diagnostic imaging*
  • Electrocardiography / standards*
  • Health*
  • Humans
  • Reference Standards
  • Signal Processing, Computer-Assisted
  • Ultrasonography

Grants and funding

It was supported in part by the Shenzhen Research Grant ZDSY20120617113021359, China 973 program (2011CB512003 and 2010CB732606–6) and NSFC 31000447. Computing resources were partly provided by the Dawning supercomputing clusters at Shenzhen Institute of Advanced Integration Technology, Chinese Academy of Sciences. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.