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 in | Journal of Electrocardiology Vol. 50; no. 6; pp. 847 - 854 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
United States
Elsevier Inc
01.11.2017
Elsevier BV Elsevier Science Ltd |
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Online Access | Get full text |
ISSN | 0022-0736 1532-8430 1532-8430 |
DOI | 10.1016/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%). |
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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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Cites_doi | 10.1109/TIM.2015.2503863 10.1016/j.patrec.2007.01.014 10.1016/j.jelectrocard.2016.07.021 10.1109/LSP.2016.2531996 10.3390/s16040570 10.1016/j.protcy.2014.10.236 10.1007/s11042-016-3694-6 10.1155/2015/135676 10.1109/19.930458 10.3390/s17020410 10.1155/2015/401975 10.3390/s150715067 |
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Keywords | Linear discriminant analysis ECG biometrics QRS, PQRST patterns True verification rate Human identity recognition Cross-correlation analysis |
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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 |
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