Irena Jekova1, Vessela Krasteva2, Remo Leber3, Ramun Schmid3, Raphael Twerenbold4, Christian Müller4, Tobias Reichlin4, Roger Abächerli5. 1. Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Bulgaria. Electronic address: irena@biomed.bas.bg. 2. Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Bulgaria. 3. SCHILLER AG, Research and Signal Processing, Baar, Switzerland. 4. University Hospital of Basel, Cardiovascular Research Institute (CRIB), Basel, Switzerland. 5. SCHILLER AG, Research and Signal Processing, Baar, Switzerland; University Hospital of Basel, Cardiovascular Research Institute (CRIB), Basel, Switzerland.
Abstract
BACKGROUND: Electrocardiogram (ECG) biometrics is an advanced technology, not yet covered by guidelines on criteria, features and leads for maximal authentication accuracy. OBJECTIVE: This study aims to define the minimal set of morphological metrics in 12-lead ECG by optimization towards high reliability and security, and validation in a person verification model across a large population. METHODS: A standard 12-lead resting ECG database from 574 non-cardiac patients with two remote recordings (>1year apart) was used. A commercial ECG analysis module (Schiller AG) measured 202 morphological features, including lead-specific amplitudes, durations, ST-metrics, and axes. Coefficient of variation (CV, intersubject variability) and percent-mean-absolute-difference (PMAD, intrasubject reproducibility) defined the optimization (PMAD/CV→min) and restriction (CV<30%) criteria for selection of the most stable and distinctive features. Linear discriminant analysis (LDA) validated the non-redundant feature set for person verification. RESULTS AND CONCLUSIONS: Maximal LDA verification sensitivity (85.3%) and specificity (86.4%) were validated for 11 optimal features: R-amplitude (I,II,V1,V2,V3,V5), S-amplitude (V1,V2), Tnegative-amplitude (aVR), and R-duration (aVF,V1).
BACKGROUND: Electrocardiogram (ECG) biometrics is an advanced technology, not yet covered by guidelines on criteria, features and leads for maximal authentication accuracy. OBJECTIVE: This study aims to define the minimal set of morphological metrics in 12-lead ECG by optimization towards high reliability and security, and validation in a person verification model across a large population. METHODS: A standard 12-lead resting ECG database from 574 non-cardiac patients with two remote recordings (>1year apart) was used. A commercial ECG analysis module (Schiller AG) measured 202 morphological features, including lead-specific amplitudes, durations, ST-metrics, and axes. Coefficient of variation (CV, intersubject variability) and percent-mean-absolute-difference (PMAD, intrasubject reproducibility) defined the optimization (PMAD/CV→min) and restriction (CV<30%) criteria for selection of the most stable and distinctive features. Linear discriminant analysis (LDA) validated the non-redundant feature set for person verification. RESULTS AND CONCLUSIONS: Maximal LDA verification sensitivity (85.3%) and specificity (86.4%) were validated for 11 optimal features: R-amplitude (I,II,V1,V2,V3,V5), S-amplitude (V1,V2), Tnegative-amplitude (aVR), and R-duration (aVF,V1).
Authors: Kazi T Haq; Katherine J Lutz; Kyle K Peters; Natalie E Craig; Evan Mitchell; Anish K Desai; Nathan W L Stencel; Elsayed Z Soliman; João A C Lima; Larisa G Tereshchenko Journal: J Electrocardiol Date: 2021-10-02 Impact factor: 1.438