Paper Info
Title
QRS detection-free electrocardiogram biometrics in the reconstructed phase space
Abstract
Most electrocardiogram (ECG) biometrics are based on detection of the QRS wave and comparison of structural features. ECG parameters are extracted from the waveform, but the process is arduous for noisy signals. Comparison based on phase space trajectory from a cardiac cycle as well as waveform comparison avoids the detection of ECG characteristic points, but has an alignment-free advantage. In this paper, we develop a QRS detection-free ECG biometric based on the phase space trajectory of the ECG signal. The multi-loop trajectory from a 5-s ECG epoch is condensed to a single-loop coarse-grained structure. The normalized spatial correlation (nSC), the mutual nearest point match (MNPM), and the mutual nearest point distance (MNPD) are considered as means of quantifying the similarity or dissimilarity between coarse-grained structures. We test our method on a population of 100 subjects. The accuracies of personal identification achieved for a single-lead ECG are 96%, 95%, and 96% for the MNPD, nSC, and MNPM methods respectively. When we analyze the phase space trajectory of a three-lead ECG, the accuracies increase to 99%, 98%, and 98% respectively. The coarse-grained phase space trajectory of an ECG signal is unambiguous and easy to compute, rendering ECGs a practical alternative to other biometrics.
Year
DOI
Venue
2013
10.1016/j.patrec.2012.11.005
Pattern Recognition Letters
Keywords
Field
DocType
5-s ecg epoch,qrs detection-free electrocardiogram biometrics,ecg signal,multi-loop trajectory,coarse-grained phase space trajectory,ecg parameter,coarse-grained structure,single-lead ecg,reconstructed phase space,three-lead ecg,ecg characteristic point,phase space trajectory,biometrics
Computer vision,Population,Spatial correlation,Pattern recognition,Phase space,Waveform,Artificial intelligence,QRS complex,Biometrics,Cardiac cycle,Mathematics,Trajectory
Journal
Volume
Issue
ISSN
34
5
0167-8655
Citations 
PageRank 
References 
5
0.40
8
Authors
2
Name
Order
Citations
PageRank
Shih-Chin Fang1642.80
Hsiao-Lung Chan217619.98