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ECG Signal as Robust and Reliable Biometric Marker: Datasets and Algorithms Comparison

In this paper, the possibility of using the ECG signal as an unequivocal biometric marker for authentication and identification purposes has been presented. Furthermore, since the ECG signal was acquired from 4 sources using different measurement equipment, electrodes positioning and number of patie...

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Autores principales: Pelc, Mariusz, Khoma, Yuriy, Khoma, Volodymyr
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6566823/
https://www.ncbi.nlm.nih.gov/pubmed/31121807
http://dx.doi.org/10.3390/s19102350
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author Pelc, Mariusz
Khoma, Yuriy
Khoma, Volodymyr
author_facet Pelc, Mariusz
Khoma, Yuriy
Khoma, Volodymyr
author_sort Pelc, Mariusz
collection PubMed
description In this paper, the possibility of using the ECG signal as an unequivocal biometric marker for authentication and identification purposes has been presented. Furthermore, since the ECG signal was acquired from 4 sources using different measurement equipment, electrodes positioning and number of patients as well as the duration of the ECG record acquisition, we have additionally provided an estimation of the extent of information available in the ECG record. To provide a more objective assessment of the credibility of the identification method, some selected machine learning algorithms were used in two combinations: with and without compression. The results that we have obtained confirm that the ECG signal can be acclaimed as a valid biometric marker that is very robust to hardware variations, noise and artifacts presence, that is stable over time and that is scalable across quite a solid (~100) number of users. Our experiments indicate that the most promising algorithms for ECG identification are LDA, KNN and MLP algorithms. Moreover, our results show that PCA compression, used as part of data preprocessing, does not only bring any noticeable benefits but in some cases might even reduce accuracy.
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spelling pubmed-65668232019-06-17 ECG Signal as Robust and Reliable Biometric Marker: Datasets and Algorithms Comparison Pelc, Mariusz Khoma, Yuriy Khoma, Volodymyr Sensors (Basel) Article In this paper, the possibility of using the ECG signal as an unequivocal biometric marker for authentication and identification purposes has been presented. Furthermore, since the ECG signal was acquired from 4 sources using different measurement equipment, electrodes positioning and number of patients as well as the duration of the ECG record acquisition, we have additionally provided an estimation of the extent of information available in the ECG record. To provide a more objective assessment of the credibility of the identification method, some selected machine learning algorithms were used in two combinations: with and without compression. The results that we have obtained confirm that the ECG signal can be acclaimed as a valid biometric marker that is very robust to hardware variations, noise and artifacts presence, that is stable over time and that is scalable across quite a solid (~100) number of users. Our experiments indicate that the most promising algorithms for ECG identification are LDA, KNN and MLP algorithms. Moreover, our results show that PCA compression, used as part of data preprocessing, does not only bring any noticeable benefits but in some cases might even reduce accuracy. MDPI 2019-05-22 /pmc/articles/PMC6566823/ /pubmed/31121807 http://dx.doi.org/10.3390/s19102350 Text en © 2019 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
Pelc, Mariusz
Khoma, Yuriy
Khoma, Volodymyr
ECG Signal as Robust and Reliable Biometric Marker: Datasets and Algorithms Comparison
title ECG Signal as Robust and Reliable Biometric Marker: Datasets and Algorithms Comparison
title_full ECG Signal as Robust and Reliable Biometric Marker: Datasets and Algorithms Comparison
title_fullStr ECG Signal as Robust and Reliable Biometric Marker: Datasets and Algorithms Comparison
title_full_unstemmed ECG Signal as Robust and Reliable Biometric Marker: Datasets and Algorithms Comparison
title_short ECG Signal as Robust and Reliable Biometric Marker: Datasets and Algorithms Comparison
title_sort ecg signal as robust and reliable biometric marker: datasets and algorithms comparison
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6566823/
https://www.ncbi.nlm.nih.gov/pubmed/31121807
http://dx.doi.org/10.3390/s19102350
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