Authors:
André Lourenço
1
;
Hugo Silva
2
;
Paulo Leite
3
;
Renato Lourenço
3
and
Ana Fred
4
Affiliations:
1
DEETC, ISEL-IPL, Instituto de Telecomunicações and IST-UTL, Portugal
;
2
Instituto de Telecomunicações, IST-UTL, PLUX - Wireless Biosignals and S.A., Portugal
;
3
DEETC and ISEL-IPL, Portugal
;
4
Instituto de Telecomunicações and IST-UTL, Portugal
Keyword(s):
Biometrics, Electrocardiography (ECG), Biosignal processing, QRS-complexes detection, Real-time segmentation.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Biometrics
;
Biometrics and Pattern Recognition
;
Cardiovascular Signals
;
Detection and Identification
;
Multimedia
;
Multimedia Signal Processing
;
Pattern Recognition
;
Real-Time Systems
;
Telecommunications
Abstract:
In biometric recognition based on Electrocardiographic (ECG) signals, there are two main approaches for feature extraction: fiducial and non-fiducial. Fiducial methods use points of interest within single heartbeat waveforms, obtained by segmenting the ECG signal using QRS complexes as a reference. In this paper we study several QRS detection algorithms, with the purpose of determining what is the best algorithm in the context of finger based ECG biometrics using fiducial approaches; our main focus is the real-time segmentation of ECG signals resulting on a set of single heart beats. We propose a method combining the adaptive characteristics of the algorithm by Christov, with the strategy of the widely adopted Engelse and Zeelenberg algorithm. Experimental results obtained for real-world data show that online approaches are competitive with offline versions, and represent a contribution for the realization of real-time biometric recognition.