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ECG Identification Based on the Gramian Angular Field and Tested with Individuals in Resting and Activity States

In the last decade, biosignals have attracted the attention of many researchers when designing novel biometrics systems. Many of these works use cardiac signals and their representation as electrocardiograms (ECGs). Nowadays, these solutions are even more realistic since we can acquire reliable ECG...

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Autores principales: Camara, Carmen, Peris-Lopez, Pedro, Safkhani, Masoumeh, Bagheri, Nasour
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9862128/
https://www.ncbi.nlm.nih.gov/pubmed/36679733
http://dx.doi.org/10.3390/s23020937
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author Camara, Carmen
Peris-Lopez, Pedro
Safkhani, Masoumeh
Bagheri, Nasour
author_facet Camara, Carmen
Peris-Lopez, Pedro
Safkhani, Masoumeh
Bagheri, Nasour
author_sort Camara, Carmen
collection PubMed
description In the last decade, biosignals have attracted the attention of many researchers when designing novel biometrics systems. Many of these works use cardiac signals and their representation as electrocardiograms (ECGs). Nowadays, these solutions are even more realistic since we can acquire reliable ECG records by using wearable devices. This paper moves in that direction and proposes a novel approach for an ECG identification system. For that, we transform the ECG recordings into Gramian Angular Field (GAF) images, a time series encoding technique well-known in other domains but not very common with biosignals. Specifically, the time series is transformed using polar coordinates, and then, the cosine sum of the angles is computed for each pair of points. We present a proof-of-concept identification system built on a tuned VGG19 convolutional neural network using this approach. We confirm our proposal’s feasibility through experimentation using two well-known public datasets: MIT-BIH Normal Sinus Rhythm Database (subjects at a resting state) and ECG-GUDB (individuals under four specific activities). In both scenarios, the identification system reaches an accuracy of 91%, and the False Acceptance Rate (FAR) is eight times higher than the False Rejection Rate (FRR).
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spelling pubmed-98621282023-01-22 ECG Identification Based on the Gramian Angular Field and Tested with Individuals in Resting and Activity States Camara, Carmen Peris-Lopez, Pedro Safkhani, Masoumeh Bagheri, Nasour Sensors (Basel) Article In the last decade, biosignals have attracted the attention of many researchers when designing novel biometrics systems. Many of these works use cardiac signals and their representation as electrocardiograms (ECGs). Nowadays, these solutions are even more realistic since we can acquire reliable ECG records by using wearable devices. This paper moves in that direction and proposes a novel approach for an ECG identification system. For that, we transform the ECG recordings into Gramian Angular Field (GAF) images, a time series encoding technique well-known in other domains but not very common with biosignals. Specifically, the time series is transformed using polar coordinates, and then, the cosine sum of the angles is computed for each pair of points. We present a proof-of-concept identification system built on a tuned VGG19 convolutional neural network using this approach. We confirm our proposal’s feasibility through experimentation using two well-known public datasets: MIT-BIH Normal Sinus Rhythm Database (subjects at a resting state) and ECG-GUDB (individuals under four specific activities). In both scenarios, the identification system reaches an accuracy of 91%, and the False Acceptance Rate (FAR) is eight times higher than the False Rejection Rate (FRR). MDPI 2023-01-13 /pmc/articles/PMC9862128/ /pubmed/36679733 http://dx.doi.org/10.3390/s23020937 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Camara, Carmen
Peris-Lopez, Pedro
Safkhani, Masoumeh
Bagheri, Nasour
ECG Identification Based on the Gramian Angular Field and Tested with Individuals in Resting and Activity States
title ECG Identification Based on the Gramian Angular Field and Tested with Individuals in Resting and Activity States
title_full ECG Identification Based on the Gramian Angular Field and Tested with Individuals in Resting and Activity States
title_fullStr ECG Identification Based on the Gramian Angular Field and Tested with Individuals in Resting and Activity States
title_full_unstemmed ECG Identification Based on the Gramian Angular Field and Tested with Individuals in Resting and Activity States
title_short ECG Identification Based on the Gramian Angular Field and Tested with Individuals in Resting and Activity States
title_sort ecg identification based on the gramian angular field and tested with individuals in resting and activity states
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9862128/
https://www.ncbi.nlm.nih.gov/pubmed/36679733
http://dx.doi.org/10.3390/s23020937
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