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.

Assessment of the potential of morphological ECG features for person identification

Bortolan G;
2016

Abstract

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.
2016
Istituto di Neuroscienze - IN -
Inglese
Computing in Cardiology
42
921
924
9781509006854
http://www.scopus.com/record/display.url?eid=2-s2.0-84964027465&origin=inward
Sì, ma tipo non specificato
06-09/09/2015
ECG
1
none
Jekova, I.; Christov, I.; Krasteva, V.; Bortolan, G.; Matveev, M.
273
info:eu-repo/semantics/conferenceObject
04 Contributo in convegno::04.01 Contributo in Atti di convegno
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/358176
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? ND
social impact