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Recognition of individual heart rate patterns with cepstral vectors

D J Curcie1, W Craelius

  • 1Department of Biomedical Engineering, Rutgers University Piscataway, NJ 08854, USA.

Biological Cybernetics
|August 1, 1997
PubMed

Insights

Heart rate patterns, represented as heart-rate cepstral vectors (HRCVs), can identify individuals with 93% accuracy. This research highlights the unique, individual characteristics within heart rate variations for potential diagnostic and forensic applications.

Area of Science:

  • Biometrics
  • Cardiology
  • Signal Processing

Background:

  • Heart rate patterns possess potential diagnostic and forensic information.
  • Individual heart rate variability (HRV) characteristics are not fully explored for identification purposes.

Purpose of the Study:

  • To investigate the potential of heart rate patterns for individual identification.
  • To represent heart rate patterns as 12-dimensional heart-rate cepstral vectors (HRCVs).

Main Methods:

  • Heart rate patterns were transformed into 12-dimensional HRCVs using linear predictive coding (LPC).
  • A codebook of HRCVs was created from 12 cardiac patients' ECG data.
  • Statistical classification used minimal weighted distances between test and codebook vectors, with weights based on inter- to intrasubject variance ratios.

Main Results:

  • HRCV coefficients accurately reproduced the heart rate variability (HRV) spectrum.
  • Individual identification using HRCV distances achieved 93% reliability within the studied group.

Conclusions:

  • Heart rate variations represent a unique individual characteristic.
  • HRCVs provide a viable method for representing and identifying individuals based on their heart rate patterns.

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