Assessment of the potential of morphological ECG features for person identification

This study investigates the potential of ECG morphological feature set for person identification. The measurements are done over 145 pairs of ECG recordings from healthy subjects, acquired 5 years apart. Time, amplitude, area and slope descriptors of the QRS-T pattern are analyzed in 4 ECG leads, forming quasi-orthogonal lead system (II&III, V1, V5). The inter-subject variation, the difference of means in 1st vs. 2nd recording measurements, as well as the cross-correlation between features are estimated. Thus, 2 area and 4 amplitude descriptors of the QRS complex are highlighted. The population heterogeneity in the space of the selected features is verified via Factor analysis by Principal components extraction method. It confirms the orthogonality of the 6 features (each of them has significant factor loading for a particular factor). The analysis shows that the first 3 factors have eigenvalues higher than 1, both for the measurements in the 1st and the 2nd ECG recording and they accumulate respectively 68% and 64 % of the total data variation, which is a sign for their person identification potential.

[1]  Ivaylo I Christov,et al.  Real time electrocardiogram QRS detection using combined adaptive threshold , 2004, Biomedical engineering online.

[2]  L. Biel,et al.  ECG analysis: a new approach in human identification , 1999, IMTC/99. Proceedings of the 16th IEEE Instrumentation and Measurement Technology Conference (Cat. No.99CH36309).

[3]  Guy Carrault,et al.  Biometric identification of individuals based on the ECG. Which conditions? , 2011, 2011 Computing in Cardiology.

[4]  I. Khalil,et al.  ECG biometric recognition in different physiological conditions using robust normalized QRS complexes , 2012, 2012 Computing in Cardiology.

[5]  Brenda K. Wiederhold,et al.  ECG to identify individuals , 2005, Pattern Recognit..

[6]  M. Baldereschi,et al.  The Italian Longitudinal Study on Aging (ILSA): Design and methods , 1994, Aging.

[7]  Clemens Elster,et al.  Verification of humans using the electrocardiogram , 2007, Pattern Recognit. Lett..

[8]  G Bortolan,et al.  Premature ventricular contraction classification by the Kth nearest-neighbours rule , 2005, Physiological measurement.

[9]  I. Christov,et al.  Q-onset and T-end delineation: assessment of the performance of an automated method with the use of a reference database , 2007, Physiological measurement.

[10]  Lei Yang,et al.  A Human ECG Identification System Based on Ensemble Empirical Mode Decomposition , 2013, Sensors.

[11]  Dimitrios Hatzinakos,et al.  Analysis of Human Electrocardiogram for Biometric Recognition , 2008, EURASIP J. Adv. Signal Process..

[12]  A. Camm,et al.  QT-RR relationship in healthy subjects exhibits substantial intersubject variability and high intrasubject stability. , 2002, American journal of physiology. Heart and circulatory physiology.

[13]  D. Hatzinakos,et al.  ECG Biometric Recognition Without Fiducial Detection , 2006, 2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference.

[14]  M Matveev,et al.  Lead selection for ECG screening. , 1977, Advances in cardiology.

[15]  Dimitrios Hatzinakos,et al.  ECG biometric analysis in cardiac irregularity conditions , 2009, Signal Image Video Process..