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Identification of Coronary Artery Diseases Using Photoplethysmography Signals and Practical Feature Selection Process

A low-cost, fast, dependable, repeatable, non-invasive, portable, and simple-to-use vascular screening tool for coronary artery diseases (CADs) is preferred. Photoplethysmography (PPG), a low-cost optical pulse wave technology, is one method with this potential. PPG signals come from changes in the...

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Autores principales: Al Fahoum, Amjed S., Abu Al-Haija, Ansam Omar, Alshraideh, Hussam A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9952145/
https://www.ncbi.nlm.nih.gov/pubmed/36829743
http://dx.doi.org/10.3390/bioengineering10020249
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author Al Fahoum, Amjed S.
Abu Al-Haija, Ansam Omar
Alshraideh, Hussam A.
author_facet Al Fahoum, Amjed S.
Abu Al-Haija, Ansam Omar
Alshraideh, Hussam A.
author_sort Al Fahoum, Amjed S.
collection PubMed
description A low-cost, fast, dependable, repeatable, non-invasive, portable, and simple-to-use vascular screening tool for coronary artery diseases (CADs) is preferred. Photoplethysmography (PPG), a low-cost optical pulse wave technology, is one method with this potential. PPG signals come from changes in the amount of blood in the microvascular bed of tissue. Therefore, these signals can be used to figure out anomalies within the cardiovascular system. This work shows how to use PPG signals and feature selection-based classifiers to identify cardiorespiratory disorders based on the extraction of time-domain features. Data were collected from 360 healthy and cardiovascular disease patients. For analysis and identification, five types of cardiovascular disorders were considered. The categories of cardiovascular diseases were identified using a two-stage classification process. The first stage was utilized to differentiate between healthy and unhealthy subjects. Subjects who were found to be abnormal were then entered into the second stage classifier, which was used to determine the type of the disease. Seven different classifiers were employed to classify the dataset. Based on the subset of features found by the classifier, the Naïve Bayes classifier obtained the best test accuracy, with 94.44% for the first stage and 89.37% for the second stage. The results of this study show how vital the PPG signal is. Many time-domain parts of the PPG signal can be easily extracted and analyzed to find out if there are problems with the heart. The results were accurate and precise enough that they did not need to be looked at or analyzed further. The PPG classifier built on a simple microcontroller will work better than more expensive ones and will not make the patient nervous.
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spelling pubmed-99521452023-02-25 Identification of Coronary Artery Diseases Using Photoplethysmography Signals and Practical Feature Selection Process Al Fahoum, Amjed S. Abu Al-Haija, Ansam Omar Alshraideh, Hussam A. Bioengineering (Basel) Article A low-cost, fast, dependable, repeatable, non-invasive, portable, and simple-to-use vascular screening tool for coronary artery diseases (CADs) is preferred. Photoplethysmography (PPG), a low-cost optical pulse wave technology, is one method with this potential. PPG signals come from changes in the amount of blood in the microvascular bed of tissue. Therefore, these signals can be used to figure out anomalies within the cardiovascular system. This work shows how to use PPG signals and feature selection-based classifiers to identify cardiorespiratory disorders based on the extraction of time-domain features. Data were collected from 360 healthy and cardiovascular disease patients. For analysis and identification, five types of cardiovascular disorders were considered. The categories of cardiovascular diseases were identified using a two-stage classification process. The first stage was utilized to differentiate between healthy and unhealthy subjects. Subjects who were found to be abnormal were then entered into the second stage classifier, which was used to determine the type of the disease. Seven different classifiers were employed to classify the dataset. Based on the subset of features found by the classifier, the Naïve Bayes classifier obtained the best test accuracy, with 94.44% for the first stage and 89.37% for the second stage. The results of this study show how vital the PPG signal is. Many time-domain parts of the PPG signal can be easily extracted and analyzed to find out if there are problems with the heart. The results were accurate and precise enough that they did not need to be looked at or analyzed further. The PPG classifier built on a simple microcontroller will work better than more expensive ones and will not make the patient nervous. MDPI 2023-02-13 /pmc/articles/PMC9952145/ /pubmed/36829743 http://dx.doi.org/10.3390/bioengineering10020249 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
Al Fahoum, Amjed S.
Abu Al-Haija, Ansam Omar
Alshraideh, Hussam A.
Identification of Coronary Artery Diseases Using Photoplethysmography Signals and Practical Feature Selection Process
title Identification of Coronary Artery Diseases Using Photoplethysmography Signals and Practical Feature Selection Process
title_full Identification of Coronary Artery Diseases Using Photoplethysmography Signals and Practical Feature Selection Process
title_fullStr Identification of Coronary Artery Diseases Using Photoplethysmography Signals and Practical Feature Selection Process
title_full_unstemmed Identification of Coronary Artery Diseases Using Photoplethysmography Signals and Practical Feature Selection Process
title_short Identification of Coronary Artery Diseases Using Photoplethysmography Signals and Practical Feature Selection Process
title_sort identification of coronary artery diseases using photoplethysmography signals and practical feature selection process
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9952145/
https://www.ncbi.nlm.nih.gov/pubmed/36829743
http://dx.doi.org/10.3390/bioengineering10020249
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