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Individual Biometric Identification Using Multi-Cycle Electrocardiographic Waveform Patterns
The electrocardiogram (ECG) waveform conveys information regarding the electrical property of the heart. The patterns vary depending on the individual heart characteristics. ECG features can be potentially used for biometric recognition. This study presents a new method using the entire ECG waveform...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948610/ https://www.ncbi.nlm.nih.gov/pubmed/29597283 http://dx.doi.org/10.3390/s18041005 |
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author | Lee, Wonki Kim, Seulgee Kim, Daeeun |
author_facet | Lee, Wonki Kim, Seulgee Kim, Daeeun |
author_sort | Lee, Wonki |
collection | PubMed |
description | The electrocardiogram (ECG) waveform conveys information regarding the electrical property of the heart. The patterns vary depending on the individual heart characteristics. ECG features can be potentially used for biometric recognition. This study presents a new method using the entire ECG waveform pattern for matching and demonstrates that the approach can potentially be employed for individual biometric identification. Multi-cycle ECG signals were assessed using an ECG measuring circuit, and three electrodes can be patched on the wrists or fingers for considering various measurements. For biometric identification, our-fold cross validation was used in the experiments for assessing how the results of a statistical analysis will generalize to an independent data set. Four different pattern matching algorithms, i.e., cosine similarity, cross correlation, city block distance, and Euclidean distances, were tested to compare the individual identification performances with a single channel of ECG signal (3-wire ECG). To evaluate the pattern matching for biometric identification, the ECG recordings for each subject were partitioned into training and test set. The suggested method obtained a maximum performance of 89.9% accuracy with two heartbeats of ECG signals measured on the wrist and 93.3% accuracy with three heartbeats for 55 subjects. The performance rate with ECG signals measured on the fingers improved up to 99.3% with two heartbeats and 100% with three heartbeats of signals for 20 subjects. |
format | Online Article Text |
id | pubmed-5948610 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-59486102018-05-17 Individual Biometric Identification Using Multi-Cycle Electrocardiographic Waveform Patterns Lee, Wonki Kim, Seulgee Kim, Daeeun Sensors (Basel) Article The electrocardiogram (ECG) waveform conveys information regarding the electrical property of the heart. The patterns vary depending on the individual heart characteristics. ECG features can be potentially used for biometric recognition. This study presents a new method using the entire ECG waveform pattern for matching and demonstrates that the approach can potentially be employed for individual biometric identification. Multi-cycle ECG signals were assessed using an ECG measuring circuit, and three electrodes can be patched on the wrists or fingers for considering various measurements. For biometric identification, our-fold cross validation was used in the experiments for assessing how the results of a statistical analysis will generalize to an independent data set. Four different pattern matching algorithms, i.e., cosine similarity, cross correlation, city block distance, and Euclidean distances, were tested to compare the individual identification performances with a single channel of ECG signal (3-wire ECG). To evaluate the pattern matching for biometric identification, the ECG recordings for each subject were partitioned into training and test set. The suggested method obtained a maximum performance of 89.9% accuracy with two heartbeats of ECG signals measured on the wrist and 93.3% accuracy with three heartbeats for 55 subjects. The performance rate with ECG signals measured on the fingers improved up to 99.3% with two heartbeats and 100% with three heartbeats of signals for 20 subjects. MDPI 2018-03-28 /pmc/articles/PMC5948610/ /pubmed/29597283 http://dx.doi.org/10.3390/s18041005 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Lee, Wonki Kim, Seulgee Kim, Daeeun Individual Biometric Identification Using Multi-Cycle Electrocardiographic Waveform Patterns |
title | Individual Biometric Identification Using Multi-Cycle Electrocardiographic Waveform Patterns |
title_full | Individual Biometric Identification Using Multi-Cycle Electrocardiographic Waveform Patterns |
title_fullStr | Individual Biometric Identification Using Multi-Cycle Electrocardiographic Waveform Patterns |
title_full_unstemmed | Individual Biometric Identification Using Multi-Cycle Electrocardiographic Waveform Patterns |
title_short | Individual Biometric Identification Using Multi-Cycle Electrocardiographic Waveform Patterns |
title_sort | individual biometric identification using multi-cycle electrocardiographic waveform patterns |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948610/ https://www.ncbi.nlm.nih.gov/pubmed/29597283 http://dx.doi.org/10.3390/s18041005 |
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