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A Model to Predict Heartbeat Rate Using Deep Learning Algorithms
ECG provides critical information in a waveform about the heart’s condition. This information is crucial to physicians as it is the first thing to be performed by cardiologists. When COVID-19 spread globally and became a pandemic, the government of Saudi Arabia placed various restrictions and guidel...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9914604/ https://www.ncbi.nlm.nih.gov/pubmed/36766905 http://dx.doi.org/10.3390/healthcare11030330 |
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author | Alsheikhy, Ahmed Said, Yahia F. Shawly, Tawfeeq Lahza, Husam |
author_facet | Alsheikhy, Ahmed Said, Yahia F. Shawly, Tawfeeq Lahza, Husam |
author_sort | Alsheikhy, Ahmed |
collection | PubMed |
description | ECG provides critical information in a waveform about the heart’s condition. This information is crucial to physicians as it is the first thing to be performed by cardiologists. When COVID-19 spread globally and became a pandemic, the government of Saudi Arabia placed various restrictions and guidelines to protect and save citizens and residents. One of these restrictions was preventing individuals from touching any surface in public and private places. In addition, the authorities placed a mandatory rule in all public facilities and the private sector to evaluate the temperature of individuals before entering. Thus, the idea of this study stems from the need to have a touchless technique to determine heartbeat rate. This article proposes a viable and dependable method to estimate an average heartbeat rate based on the reflected light on the skin. This model uses various deep learning tools, including AlexNet, Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and ResNet50V2. Three scenarios have been conducted to evaluate and validate the presented model. In addition, the proposed approach takes its inputs from video streams and converts these streams into frames and images. Numerous trials have been conducted on volunteers to validate the method and assess its outputs in terms of accuracy, mean absolute error (MAE), and mean squared error (MSE). The proposed model achieves an average 99.78% accuracy, MAE is 0.142 when combing LSTMs and ResNet50V2, while MSE is 1.82. Moreover, a comparative measurement between the presented algorithm and some studies from the literature based on utilized methods, MAE, and MSE are performed. The achieved outcomes reveal that the developed technique surpasses other methods. Moreover, the findings show that this algorithm can be applied in healthcare facilities and aid physicians. |
format | Online Article Text |
id | pubmed-9914604 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99146042023-02-11 A Model to Predict Heartbeat Rate Using Deep Learning Algorithms Alsheikhy, Ahmed Said, Yahia F. Shawly, Tawfeeq Lahza, Husam Healthcare (Basel) Article ECG provides critical information in a waveform about the heart’s condition. This information is crucial to physicians as it is the first thing to be performed by cardiologists. When COVID-19 spread globally and became a pandemic, the government of Saudi Arabia placed various restrictions and guidelines to protect and save citizens and residents. One of these restrictions was preventing individuals from touching any surface in public and private places. In addition, the authorities placed a mandatory rule in all public facilities and the private sector to evaluate the temperature of individuals before entering. Thus, the idea of this study stems from the need to have a touchless technique to determine heartbeat rate. This article proposes a viable and dependable method to estimate an average heartbeat rate based on the reflected light on the skin. This model uses various deep learning tools, including AlexNet, Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and ResNet50V2. Three scenarios have been conducted to evaluate and validate the presented model. In addition, the proposed approach takes its inputs from video streams and converts these streams into frames and images. Numerous trials have been conducted on volunteers to validate the method and assess its outputs in terms of accuracy, mean absolute error (MAE), and mean squared error (MSE). The proposed model achieves an average 99.78% accuracy, MAE is 0.142 when combing LSTMs and ResNet50V2, while MSE is 1.82. Moreover, a comparative measurement between the presented algorithm and some studies from the literature based on utilized methods, MAE, and MSE are performed. The achieved outcomes reveal that the developed technique surpasses other methods. Moreover, the findings show that this algorithm can be applied in healthcare facilities and aid physicians. MDPI 2023-01-22 /pmc/articles/PMC9914604/ /pubmed/36766905 http://dx.doi.org/10.3390/healthcare11030330 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 Alsheikhy, Ahmed Said, Yahia F. Shawly, Tawfeeq Lahza, Husam A Model to Predict Heartbeat Rate Using Deep Learning Algorithms |
title | A Model to Predict Heartbeat Rate Using Deep Learning Algorithms |
title_full | A Model to Predict Heartbeat Rate Using Deep Learning Algorithms |
title_fullStr | A Model to Predict Heartbeat Rate Using Deep Learning Algorithms |
title_full_unstemmed | A Model to Predict Heartbeat Rate Using Deep Learning Algorithms |
title_short | A Model to Predict Heartbeat Rate Using Deep Learning Algorithms |
title_sort | model to predict heartbeat rate using deep learning algorithms |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9914604/ https://www.ncbi.nlm.nih.gov/pubmed/36766905 http://dx.doi.org/10.3390/healthcare11030330 |
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