Detecting Coronary Artery Disease Using Rest Seismocardiography and Gyrocardiography

In this study, we present a non-invasive solution to identify patients with coronary artery disease (CAD) defined as ⩾50% stenosis in at least one coronary artery. The solution is based on the analysis of linear acceleration (seismocardiogram, SCG) and angular velocity (gyrocardiogram, GCG) of the h...

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Autores principales:	Parastoo Dehkordi, Erwin P. Bauer, Kouhyar Tavakolian, Zhen G. Xiao, Andrew P. Blaber, Farzad Khosrow-Khavar
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	seismocardiography gyrocardiography coronary artery disease (CAD) cardiac mechanical activity angiography Physiology QP1-981
Acceso en línea:	https://doaj.org/article/495f35068fd64be0beee717123307109
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id	oai:doaj.org-article:495f35068fd64be0beee717123307109
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spelling	oai:doaj.org-article:495f35068fd64be0beee7171233071092021-12-02T17:29:44ZDetecting Coronary Artery Disease Using Rest Seismocardiography and Gyrocardiography1664-042X10.3389/fphys.2021.758727https://doaj.org/article/495f35068fd64be0beee7171233071092021-12-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fphys.2021.758727/fullhttps://doaj.org/toc/1664-042XIn this study, we present a non-invasive solution to identify patients with coronary artery disease (CAD) defined as ⩾50% stenosis in at least one coronary artery. The solution is based on the analysis of linear acceleration (seismocardiogram, SCG) and angular velocity (gyrocardiogram, GCG) of the heart recorded in the x, y, and z directional axes from an accelerometer/gyroscope sensor mounted on the sternum. The database was collected from 310 individuals through a multicenter study. The time-frequency features extracted from each SCG and GCG data channel were fed to a one-dimensional Convolutional Neural Network (1D CNN) to train six separate classifiers. The results from different classifiers were later fused to estimate the CAD risk for each participant. The predicted CAD risk was validated against related results from angiography. The SCG z and SCG y classifiers showed better performance relative to the other models (p < 0.05) with the area under the curve (AUC) of 91%. The sensitivity range for CAD detection was 92–94% for the SCG models and 73–87% for the GCG models. Based on our findings, the SCG models achieved better performance in predicting the CAD risk compared to the GCG models; the model based on the combination of all SCG and GCG classifiers did not achieve higher performance relative to the other models. Moreover, these findings showed that the performance of the proposed 3-axial SCG/GCG solution based on recordings obtained during rest was comparable, or better than stress ECG. These data may indicate that 3-axial SCG/GCG could be used as a portable at-home CAD screening tool.Parastoo DehkordiErwin P. BauerKouhyar TavakolianKouhyar TavakolianZhen G. XiaoAndrew P. BlaberFarzad Khosrow-KhavarFrontiers Media S.A.articleseismocardiographygyrocardiographycoronary artery disease (CAD)cardiac mechanical activityangiographyPhysiologyQP1-981ENFrontiers in Physiology, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	seismocardiography gyrocardiography coronary artery disease (CAD) cardiac mechanical activity angiography Physiology QP1-981
spellingShingle	seismocardiography gyrocardiography coronary artery disease (CAD) cardiac mechanical activity angiography Physiology QP1-981 Parastoo Dehkordi Erwin P. Bauer Kouhyar Tavakolian Kouhyar Tavakolian Zhen G. Xiao Andrew P. Blaber Farzad Khosrow-Khavar Detecting Coronary Artery Disease Using Rest Seismocardiography and Gyrocardiography
description	In this study, we present a non-invasive solution to identify patients with coronary artery disease (CAD) defined as ⩾50% stenosis in at least one coronary artery. The solution is based on the analysis of linear acceleration (seismocardiogram, SCG) and angular velocity (gyrocardiogram, GCG) of the heart recorded in the x, y, and z directional axes from an accelerometer/gyroscope sensor mounted on the sternum. The database was collected from 310 individuals through a multicenter study. The time-frequency features extracted from each SCG and GCG data channel were fed to a one-dimensional Convolutional Neural Network (1D CNN) to train six separate classifiers. The results from different classifiers were later fused to estimate the CAD risk for each participant. The predicted CAD risk was validated against related results from angiography. The SCG z and SCG y classifiers showed better performance relative to the other models (p < 0.05) with the area under the curve (AUC) of 91%. The sensitivity range for CAD detection was 92–94% for the SCG models and 73–87% for the GCG models. Based on our findings, the SCG models achieved better performance in predicting the CAD risk compared to the GCG models; the model based on the combination of all SCG and GCG classifiers did not achieve higher performance relative to the other models. Moreover, these findings showed that the performance of the proposed 3-axial SCG/GCG solution based on recordings obtained during rest was comparable, or better than stress ECG. These data may indicate that 3-axial SCG/GCG could be used as a portable at-home CAD screening tool.
format	article
author	Parastoo Dehkordi Erwin P. Bauer Kouhyar Tavakolian Kouhyar Tavakolian Zhen G. Xiao Andrew P. Blaber Farzad Khosrow-Khavar
author_facet	Parastoo Dehkordi Erwin P. Bauer Kouhyar Tavakolian Kouhyar Tavakolian Zhen G. Xiao Andrew P. Blaber Farzad Khosrow-Khavar
author_sort	Parastoo Dehkordi
title	Detecting Coronary Artery Disease Using Rest Seismocardiography and Gyrocardiography
title_short	Detecting Coronary Artery Disease Using Rest Seismocardiography and Gyrocardiography
title_full	Detecting Coronary Artery Disease Using Rest Seismocardiography and Gyrocardiography
title_fullStr	Detecting Coronary Artery Disease Using Rest Seismocardiography and Gyrocardiography
title_full_unstemmed	Detecting Coronary Artery Disease Using Rest Seismocardiography and Gyrocardiography
title_sort	detecting coronary artery disease using rest seismocardiography and gyrocardiography
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/495f35068fd64be0beee717123307109
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Detecting Coronary Artery Disease Using Rest Seismocardiography and Gyrocardiography

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