Perspectives of human verification via binary QRS template matching of single-lead and 12-lead electrocardiogram

<div><p>Objective</p><p>This study aims to validate the 12-lead electrocardiogram (ECG) as a biometric modality based on two straightforward binary QRS template matching characteristics. Different perspectives of the human verification problem are considered, regarding the optimal lead selection and stability over sample size, gender, age, heart rate (HR).</p><p>Methods</p><p>A clinical 12-lead resting ECG database, including a population of 460 subjects with two-session recordings (>1 year apart) is used. Cost-effective strategies for extraction of personalized QRS patterns (100ms) and binary template matching estimate similarity in the time scale (matching time) and dissimilarity in the amplitude scale (mismatch area). The two-class person verification task, taking the decision to validate or to reject the subject identity is managed by linear discriminant analysis (LDA). Non-redundant LDA models for different lead configurations (I,II,III,aVF,aVL,aVF,V1-V6) are trained on the first half of 230 subjects by stepwise feature selection until maximization of the area under the receiver operating characteristic curve (ROC AUC). The operating point on the training ROC at equal error rate (EER) is tested on the independent dataset (second half of 230 subjects) to report unbiased validation of test-ROC AUC and true verification rate (TVR = 100-EER). The test results are further evaluated in groups by sample size, gender, age, HR.</p><p>Results and discussion</p><p>The optimal QRS pattern projection for single-lead ECG biometric modality is found in the frontal plane sector (60°-0°) with best (Test-AUC/TVR) for lead II (0.941/86.8%) and slight accuracy drop for -aVR (-0.017/-1.4%), I (-0.01/-1.5%). Chest ECG leads have degrading accuracy from V1 (0.885/80.6%) to V6 (0.799/71.8%). The multi-lead ECG improves verification: 6-chest (0.97/90.9%), 6-limb (0.986/94.3%), 12-leads (0.995/97.5%). The QRS pattern matching model shows stable performance for verification of 10 to 230 individuals; insignificant degradation of TVR in women by (1.2–3.6%), adults ≥70 years (3.7%), younger <40 years (1.9%), HR<60bpm (1.2%), HR>90bpm (3.9%), no degradation for HR change (0 to >20bpm).</p></div

Example of 12-lead average beat patterns from three different subjects (with identity named IDx, IDy, IDz), which are aligned by maximal cross-correlation in lead I to a reference pattern.

<p>The vertical red lines encompass the synchronously extracted 12-lead QRS pattern in a window [-30ms; 70ms] around the R-peak of the reference pattern.</p

Test-TVR of single and multi-lead ECG sets.

<p>Single leads are ordered according to their spatial neighborhood, i.e. limb leads are presented in ascending order of their spatial angle in the frontal plane (given in brackets, from -30° to 120°); chest leads V1-V6 are presented according to their standard order in the horizontal plane.</p

Performance of 12-lead LDA model in function of the number of subjects in the test database.

<p>TAR, TRR and TVR are reported as mean value (min-max range) after test of all possible combinations of 10, 50, 100, 150, 200, 230 subjects within the total test database with 230 subjects. The differences between groups are not statistically significant (p>0.05).</p

Human verification performance of single and multi-lead ECG sets: AUC of the training and test ROC.

<p>The bolded values highlight the maximal AUC of the test-ROC for single limb leads, single chest leads, and the multi-lead sets.</p

Age-specific performance of 12-lead LDA model, evaluated for 230 subjects in the test database, divided into six age groups.

<p>The differences between groups are not statistically significant (p>0.05).</p

Median value (quartile range) of tEQU and aDIF features for 12 ECG leads (S1 File).

<p>Statistically different distributions of 460 equal (IDS1 = IDS2) vs. 211140 different (IDS1≠IDS2) identity pairs are found in all leads (p<0.001).</p

Perspectives of human verification via binary QRS template matching of single-lead and 12-lead electrocardiogram - Fig 3

<p>Example of 12-lead QRS pattern matching between S1 and S2 sessions, taking recordings from equal ID subjects (left panel) and different ID subjects (right panel). (A) The grey approximation span around each QRS pattern (white trace) represents the ones in the corresponding 2D binary matrix binQRS(100x80). (B) The green zones represent the ones in the binary AND matrix for computation of tEQU, matching the time equivalence between the two patterns (black traces). (C) The red elements represent the ones in the binary NAND matrix for computation of aDIF, matching the area difference between the two patterns (black traces).</p

Human verification performance of single and multi-lead ECG sets for the EER operating point on the training ROC (Train-TAR = Train-TRR = Train-TVR).

<p>The observed performance on the independent test set has a slight bias Test-TAR>Test-TRR. The bolded values highlight the maximal TVR on the test set for single limb leads, single chest leads, and the multi-lead sets.</p

Perspectives of human verification via binary QRS template matching of single-lead and 12-lead electrocardiogram - Fig 9

<p>HR-specific performance of 12-lead LDA model, evaluated for 230 subjects in the test database, divided into: (A) 5 groups based on the absolute HR value in S1 session; (B) 3 groups based on the absolute HR change between S1 and S2 sessions (ΔHR). The differences between groups are not statistically significant (p>0.05), except TAR for ≥90 bpm (*p = 0.012).</p