Biometric verification by cross-correlation analysis of 12-lead ECG patterns: Ranking of the most reliable peripheral and chest leads

Electrocardiogram (ECG)-based biometrics relies on the most stable and unique beat patterns, i.e. those with maximal intra-subject and minimal inter-subject waveform differences seen from different leads. We investigated methodology to evaluate those differences, aiming to rank the most prominent si...

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Published inJournal of Electrocardiology Vol. 50; no. 6; pp. 847 - 854
Main Authors Krasteva, Vessela, Jekova, Irena, Abächerli, Roger
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 01.11.2017
Elsevier BV
Elsevier Science Ltd
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ISSN0022-0736
1532-8430
1532-8430
DOI10.1016/j.jelectrocard.2017.08.021

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Abstract Electrocardiogram (ECG)-based biometrics relies on the most stable and unique beat patterns, i.e. those with maximal intra-subject and minimal inter-subject waveform differences seen from different leads. We investigated methodology to evaluate those differences, aiming to rank the most prominent single and multi-lead ECG sets for biometric verification across a large population. A clinical standard 12-lead resting ECG database, including 460 pairs of remote recordings (distanced 1year apart) was used. Inter-subject beat waveform differences were studied by cross-correlation and amplitude relations of average PQRST (500ms) and QRS (100ms) patterns, using 8 features/lead in 12-leads. Biometric verification models based on stepwise linear discriminant classifier were trained on the first half of records. True verification rate (TVR) on the remaining test data was further reported as a common mean of the correctly verified equal subjects (true acceptance rate) and correctly rejected different subjects (true rejection rate). In single-lead ECG human identity applications, we found maximal TVR (87–89%) for the frontal plane leads (I, −aVR, II) within (0–60°) sector. Other leads were ranked: inferior (85%), lateral to septal (82–81%), with intermittent V3 drop (77.6%), suggesting anatomical landmark displacements. ECG pattern view from multi-lead sets improved TVR: chest (91.3%), limb (94.6%), 12-leads (96.3%).
AbstractList Electrocardiogram (ECG)-based biometrics relies on the most stable and unique beat patterns, i.e. those with maximal intra-subject and minimal inter-subject waveform differences seen from different leads. We investigated methodology to evaluate those differences, aiming to rank the most prominent single and multi-lead ECG sets for biometric verification across a large population. A clinical standard 12-lead resting ECG database, including 460 pairs of remote recordings (distanced 1year apart) was used. Inter-subject beat waveform differences were studied by cross-correlation and amplitude relations of average PQRST (500ms) and QRS (100ms) patterns, using 8 features/lead in 12-leads. Biometric verification models based on stepwise linear discriminant classifier were trained on the first half of records. True verification rate (TVR) on the remaining test data was further reported as a common mean of the correctly verified equal subjects (true acceptance rate) and correctly rejected different subjects (true rejection rate). In single-lead ECG human identity applications, we found maximal TVR (87-89%) for the frontal plane leads (I, -aVR, II) within (0-60°) sector. Other leads were ranked: inferior (85%), lateral to septal (82-81%), with intermittent V3 drop (77.6%), suggesting anatomical landmark displacements. ECG pattern view from multi-lead sets improved TVR: chest (91.3%), limb (94.6%), 12-leads (96.3%).
Electrocardiogram (ECG)-based biometrics relies on the most stable and unique beat patterns, i.e. those with maximal intra-subject and minimal inter-subject waveform differences seen from different leads. We investigated methodology to evaluate those differences, aiming to rank the most prominent single and multi-lead ECG sets for biometric verification across a large population. A clinical standard 12-lead resting ECG database, including 460 pairs of remote recordings (distanced 1year apart) was used. Inter-subject beat waveform differences were studied by cross-correlation and amplitude relations of average PQRST (500ms) and QRS (100ms) patterns, using 8 features/lead in 12-leads. Biometric verification models based on stepwise linear discriminant classifier were trained on the first half of records. True verification rate (TVR) on the remaining test data was further reported as a common mean of the correctly verified equal subjects (true acceptance rate) and correctly rejected different subjects (true rejection rate). In single-lead ECG human identity applications, we found maximal TVR (87–89%) for the frontal plane leads (I, −aVR, II) within (0–60°) sector. Other leads were ranked: inferior (85%), lateral to septal (82–81%), with intermittent V3 drop (77.6%), suggesting anatomical landmark displacements. ECG pattern view from multi-lead sets improved TVR: chest (91.3%), limb (94.6%), 12-leads (96.3%).
Background Electrocardiogram (ECG)-based biometrics relies on the most stable and unique beat patterns, i.e. those with maximal intra-subject and minimal inter-subject waveform differences seen from different leads. We investigated methodology to evaluate those differences, aiming to rank the most prominent single and multi-lead ECG sets for biometric verification across a large population. Methods A clinical standard 12-lead resting ECG database, including 460 pairs of remote recordings (distanced 1 year apart) was used. Inter-subject beat waveform differences were studied by cross-correlation and amplitude relations of average PQRST (500 ms) and QRS (100 ms) patterns, using 8 features/lead in 12-leads. Biometric verification models based on stepwise linear discriminant classifier were trained on the first half of records. True verification rate (TVR) on the remaining test data was further reported as a common mean of the correctly verified equal subjects (true acceptance rate) and correctly rejected different subjects (true rejection rate). Results and conclusions In single-lead ECG human identity applications, we found maximal TVR (87–89%) for the frontal plane leads (I, −aVR, II) within (0–60°) sector. Other leads were ranked: inferior (85%), lateral to septal (82–81%), with intermittent V3 drop (77.6%), suggesting anatomical landmark displacements. ECG pattern view from multi-lead sets improved TVR: chest (91.3%), limb (94.6%), 12-leads (96.3%).
Electrocardiogram (ECG)-based biometrics relies on the most stable and unique beat patterns, i.e. those with maximal intra-subject and minimal inter-subject waveform differences seen from different leads. We investigated methodology to evaluate those differences, aiming to rank the most prominent single and multi-lead ECG sets for biometric verification across a large population.BACKGROUNDElectrocardiogram (ECG)-based biometrics relies on the most stable and unique beat patterns, i.e. those with maximal intra-subject and minimal inter-subject waveform differences seen from different leads. We investigated methodology to evaluate those differences, aiming to rank the most prominent single and multi-lead ECG sets for biometric verification across a large population.A clinical standard 12-lead resting ECG database, including 460 pairs of remote recordings (distanced 1year apart) was used. Inter-subject beat waveform differences were studied by cross-correlation and amplitude relations of average PQRST (500ms) and QRS (100ms) patterns, using 8 features/lead in 12-leads. Biometric verification models based on stepwise linear discriminant classifier were trained on the first half of records. True verification rate (TVR) on the remaining test data was further reported as a common mean of the correctly verified equal subjects (true acceptance rate) and correctly rejected different subjects (true rejection rate).METHODSA clinical standard 12-lead resting ECG database, including 460 pairs of remote recordings (distanced 1year apart) was used. Inter-subject beat waveform differences were studied by cross-correlation and amplitude relations of average PQRST (500ms) and QRS (100ms) patterns, using 8 features/lead in 12-leads. Biometric verification models based on stepwise linear discriminant classifier were trained on the first half of records. True verification rate (TVR) on the remaining test data was further reported as a common mean of the correctly verified equal subjects (true acceptance rate) and correctly rejected different subjects (true rejection rate).In single-lead ECG human identity applications, we found maximal TVR (87-89%) for the frontal plane leads (I, -aVR, II) within (0-60°) sector. Other leads were ranked: inferior (85%), lateral to septal (82-81%), with intermittent V3 drop (77.6%), suggesting anatomical landmark displacements. ECG pattern view from multi-lead sets improved TVR: chest (91.3%), limb (94.6%), 12-leads (96.3%).RESULTS AND CONCLUSIONSIn single-lead ECG human identity applications, we found maximal TVR (87-89%) for the frontal plane leads (I, -aVR, II) within (0-60°) sector. Other leads were ranked: inferior (85%), lateral to septal (82-81%), with intermittent V3 drop (77.6%), suggesting anatomical landmark displacements. ECG pattern view from multi-lead sets improved TVR: chest (91.3%), limb (94.6%), 12-leads (96.3%).
Author Jekova, Irena
Abächerli, Roger
Krasteva, Vessela
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Issue 6
Keywords Linear discriminant analysis
ECG biometrics
QRS, PQRST patterns
True verification rate
Human identity recognition
Cross-correlation analysis
Language English
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Snippet Electrocardiogram (ECG)-based biometrics relies on the most stable and unique beat patterns, i.e. those with maximal intra-subject and minimal inter-subject...
Background Electrocardiogram (ECG)-based biometrics relies on the most stable and unique beat patterns, i.e. those with maximal intra-subject and minimal...
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SubjectTerms Adolescent
Adult
Aged
Aged, 80 and over
Biometrics
Biometry
Biometry - methods
Correlation analysis
Cross-correlation analysis
Databases, Factual
Discriminant Analysis
ECG biometrics
Electrocardiography
Electrocardiography - methods
Female
Human identity recognition
Humans
Linear discriminant analysis
Male
Middle Aged
QRS, PQRST patterns
Retrospective Studies
Switzerland
Thorax
True verification rate
Title Biometric verification by cross-correlation analysis of 12-lead ECG patterns: Ranking of the most reliable peripheral and chest leads
URI https://www.clinicalkey.com/#!/content/1-s2.0-S0022073617302625
https://dx.doi.org/10.1016/j.jelectrocard.2017.08.021
https://cir.nii.ac.jp/crid/1871709542474441344
https://www.ncbi.nlm.nih.gov/pubmed/28916172
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https://www.proquest.com/docview/1940060090
Volume 50
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