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Alternative method to pre-diagnosed coronary artery disease using photoplethysmography: a lesson from COVID-19 pandemic

Ischemic heart disease (IHD) is one of the underlying factors that contribute to mortality in COVID-19 infected patients. IHD or coronary artery disease (CAD) is commonly diagnosed using invasive coronary angiography (ICA) or computed tomography angiography (CTA). However, these imaging modalities a...

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Main Authors: Mohd Zubir Suboh,, Rosmina Jaafar,, Nazrul Anuar Nayan,, Noor Hasmiza Harun,, Mohd Shawal Faizal Mohamad,, Hamzaini Abdul Hamid,
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia 2024
Online Access:http://journalarticle.ukm.my/24434/1/HA%203.pdf
http://journalarticle.ukm.my/24434/
https://ejournals.ukm.my/jhadhari/issue/view/1723
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spelling my-ukm.journal.244342024-10-21T08:08:05Z http://journalarticle.ukm.my/24434/ Alternative method to pre-diagnosed coronary artery disease using photoplethysmography: a lesson from COVID-19 pandemic Mohd Zubir Suboh, Rosmina Jaafar, Nazrul Anuar Nayan, Noor Hasmiza Harun, Mohd Shawal Faizal Mohamad, Hamzaini Abdul Hamid, Ischemic heart disease (IHD) is one of the underlying factors that contribute to mortality in COVID-19 infected patients. IHD or coronary artery disease (CAD) is commonly diagnosed using invasive coronary angiography (ICA) or computed tomography angiography (CTA). However, these imaging modalities are costly, operationally complex and hardly accessible, especially during the pandemic. Thus, researchers have great interest in using non-invasive techniques of electrocardiography (ECG) and photoplethysmography (PPG) as alternatives to pre-diagnose the disease. This study focused on the detection of the severity of stenosis in the coronary artery using PPG among newly diagnosed IHD patients. A total of 88 patients of Hospital Canselor Tuanku Muhriz were involved. They were grouped as having severe stenosis if their stenosis percentage are at 70% or more, based on ICA or CTA evidence. A total of 73 time-domain features were analyzed in this study. Five machine learning methods were investigated to categorize the patients using up to 15 selected features. Results showed that the Discriminant Analysis method performed the best with accuracy, sensitivity and specificity of 88.46%, 100% and 70%, respectively. In conclusion, the severity of stenosis in coronary arteries has a high potential of being detected using simple non-invasive tools of PPG. Penerbit Universiti Kebangsaan Malaysia 2024 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/24434/1/HA%203.pdf Mohd Zubir Suboh, and Rosmina Jaafar, and Nazrul Anuar Nayan, and Noor Hasmiza Harun, and Mohd Shawal Faizal Mohamad, and Hamzaini Abdul Hamid, (2024) Alternative method to pre-diagnosed coronary artery disease using photoplethysmography: a lesson from COVID-19 pandemic. Jurnal Hadhari, 16 (1). pp. 39-50. ISSN 1985-6830 https://ejournals.ukm.my/jhadhari/issue/view/1723
institution Universiti Kebangsaan Malaysia
building Tun Sri Lanang Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Kebangsaan Malaysia
content_source UKM Journal Article Repository
url_provider http://journalarticle.ukm.my/
language English
description Ischemic heart disease (IHD) is one of the underlying factors that contribute to mortality in COVID-19 infected patients. IHD or coronary artery disease (CAD) is commonly diagnosed using invasive coronary angiography (ICA) or computed tomography angiography (CTA). However, these imaging modalities are costly, operationally complex and hardly accessible, especially during the pandemic. Thus, researchers have great interest in using non-invasive techniques of electrocardiography (ECG) and photoplethysmography (PPG) as alternatives to pre-diagnose the disease. This study focused on the detection of the severity of stenosis in the coronary artery using PPG among newly diagnosed IHD patients. A total of 88 patients of Hospital Canselor Tuanku Muhriz were involved. They were grouped as having severe stenosis if their stenosis percentage are at 70% or more, based on ICA or CTA evidence. A total of 73 time-domain features were analyzed in this study. Five machine learning methods were investigated to categorize the patients using up to 15 selected features. Results showed that the Discriminant Analysis method performed the best with accuracy, sensitivity and specificity of 88.46%, 100% and 70%, respectively. In conclusion, the severity of stenosis in coronary arteries has a high potential of being detected using simple non-invasive tools of PPG.
format Article
author Mohd Zubir Suboh,
Rosmina Jaafar,
Nazrul Anuar Nayan,
Noor Hasmiza Harun,
Mohd Shawal Faizal Mohamad,
Hamzaini Abdul Hamid,
spellingShingle Mohd Zubir Suboh,
Rosmina Jaafar,
Nazrul Anuar Nayan,
Noor Hasmiza Harun,
Mohd Shawal Faizal Mohamad,
Hamzaini Abdul Hamid,
Alternative method to pre-diagnosed coronary artery disease using photoplethysmography: a lesson from COVID-19 pandemic
author_facet Mohd Zubir Suboh,
Rosmina Jaafar,
Nazrul Anuar Nayan,
Noor Hasmiza Harun,
Mohd Shawal Faizal Mohamad,
Hamzaini Abdul Hamid,
author_sort Mohd Zubir Suboh,
title Alternative method to pre-diagnosed coronary artery disease using photoplethysmography: a lesson from COVID-19 pandemic
title_short Alternative method to pre-diagnosed coronary artery disease using photoplethysmography: a lesson from COVID-19 pandemic
title_full Alternative method to pre-diagnosed coronary artery disease using photoplethysmography: a lesson from COVID-19 pandemic
title_fullStr Alternative method to pre-diagnosed coronary artery disease using photoplethysmography: a lesson from COVID-19 pandemic
title_full_unstemmed Alternative method to pre-diagnosed coronary artery disease using photoplethysmography: a lesson from COVID-19 pandemic
title_sort alternative method to pre-diagnosed coronary artery disease using photoplethysmography: a lesson from covid-19 pandemic
publisher Penerbit Universiti Kebangsaan Malaysia
publishDate 2024
url http://journalarticle.ukm.my/24434/1/HA%203.pdf
http://journalarticle.ukm.my/24434/
https://ejournals.ukm.my/jhadhari/issue/view/1723
_version_ 1814049146075611136
score 13.211869

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