MATEC Web Conf.
Volume 125, 201721st International Conference on Circuits, Systems, Communications and Computers (CSCC 2017)
|Number of page(s)||4|
|Published online||04 October 2017|
Assessment of the stability of morphological ECG features and their potential for person verification/identification
1 Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria
2 Institute of Neuroscience, IN-CNR, Padova, Italy
* Corresponding author: email@example.com
This study investigates the potential of a set of ECG morphological features for person verification/identification. The measurements are done over 145 pairs of ECG recordings from healthy subjects, acquired 5 years apart (T1, T2 = T1+5 years). Time, amplitude, area and slope descriptors of the QRS-T pattern are analysed in 4 ECG leads, forming quasi-orthogonal lead system (II&III, V1, V5). The correspondence between feature values in T1 and T2 is verified via factor analysis by principal components extraction method; correlation analysis applied over the measurements in T1 and T2; synthesis of regression equations for prediction of features’ values in T2 based on T1 measurements; and cluster analysis for assessment of the correspondence between measured and predicted feature values. Thus, 11 amplitude descriptors of the QRS complex are highlighted as stable, i.e. keeping their strong correlation (≥0.7) within a certain factor, weak correlation (<0.3) with the features from the remaining factors and presenting high correlation in the two measurement periods that is a sign for their person verification/identification potential. The observed coincidence between feature values measured in T2 and predicted via the designed regression models (r=0.93) suggests about the confidence of person identification via the proposed morphological features.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.