Subject identification via ECG fiducial-based systems: Influence of the type of QT interval correction

• Published in: Computer methods and programs in biomedicine Vol. 121; no. 3; pp. 127 - 136
• Main Authors: Gargiulo, Francesco, Fratini, Antonio, Sansone, Mario, Sansone, Carlo
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier Ireland Ltd 01.10.2015
• Subjects: Biometrics identification systems; Classification; Electrocardiogram; Electrocardiography - methods; Heart - physiology; Heart Rate; Humans; Internal Medicine; Other; QT interval correction; Biometrics identification systems; QT interval correction; Electrocardiogram; Classification
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2015.05.012

•Electrocardiography (ECG) has been recently proposed as biometric trait for identification purposes.•The influence of the QT interval correction method on the performance of ECG-based identification systems is analyzed.•ECG signals were collected from the Physionet open access database.•A public domain software was used for fiducial points detection.•Results suggested that QT correction is always required to improve the performance. Electrocardiography (ECG) has been recently proposed as biometric trait for identification purposes. Intra-individual variations of ECG might affect identification performance. These variations are mainly due to Heart Rate Variability (HRV). In particular, HRV causes changes in the QT intervals along the ECG waveforms. This work is aimed at analysing the influence of seven QT interval correction methods (based on population models) on the performance of ECG-fiducial-based identification systems. In addition, we have also considered the influence of training set size, classifier, classifier ensemble as well as the number of consecutive heartbeats in a majority voting scheme. The ECG signals used in this study were collected from thirty-nine subjects within the Physionet open access database. Public domain software was used for fiducial points detection. Results suggested that QT correction is indeed required to improve the performance. However, there is no clear choice among the seven explored approaches for QT correction (identification rate between 0.97 and 0.99). MultiLayer Perceptron and Support Vector Machine seemed to have better generalization capabilities, in terms of classification performance, with respect to Decision Tree-based classifiers. No such strong influence of the training-set size and the number of consecutive heartbeats has been observed on the majority voting scheme.