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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...

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Detalles Bibliográficos
Autores principales: Lee, Wonki, Kim, Seulgee, Kim, Daeeun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
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.
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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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