Feasibility of the deep learning method for estimating the ventilatory threshold with electrocardiography data

Abstract Regular aerobic physical activity is of utmost importance in maintaining a good health status and preventing cardiovascular diseases (CVDs). Although cardiopulmonary exercise testing (CPX) is an essential examination for noninvasive estimation of ventilatory threshold (VT), defined as the c...

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Autores principales:	Kotaro Miura, Shinichi Goto, Yoshinori Katsumata, Hidehiko Ikura, Yasuyuki Shiraishi, Kazuki Sato, Keiichi Fukuda
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/4c47e5e0168a4323b78e621e727ada37
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spelling	oai:doaj.org-article:4c47e5e0168a4323b78e621e727ada372021-12-02T10:59:14ZFeasibility of the deep learning method for estimating the ventilatory threshold with electrocardiography data10.1038/s41746-020-00348-62398-6352https://doaj.org/article/4c47e5e0168a4323b78e621e727ada372020-10-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-00348-6https://doaj.org/toc/2398-6352Abstract Regular aerobic physical activity is of utmost importance in maintaining a good health status and preventing cardiovascular diseases (CVDs). Although cardiopulmonary exercise testing (CPX) is an essential examination for noninvasive estimation of ventilatory threshold (VT), defined as the clinically equivalent to aerobic exercise, its evaluation requires an expensive respiratory gas analyzer and expertize. To address these inconveniences, this study investigated the feasibility of a deep learning (DL) algorithm with single-lead electrocardiography (ECG) for estimating the aerobic exercise threshold. Two hundred sixty consecutive patients with CVDs who underwent CPX were analyzed. Single-lead ECG data were stored as time-series voltage data with a sampling rate of 1000 Hz. The data of preprocessed ECG and time point at VT calculated by respiratory gas analyzer were used to train a neural network. The trained model was applied on an independent test cohort, and the DL threshold (DLT; a time of VT estimated through the DL algorithm) was calculated. We compared the correlation between oxygen uptake of the VT (VT–VO2) and the DLT (DLT–VO2). Our DL model showed that the DLT–VO2 was confirmed to be significantly correlated with the VT–VO2 (r = 0.875; P < 0.001), and the mean difference was nonsignificant (−0.05 ml/kg/min, P > 0.05), which displayed strong agreements between the VT and the DLT. The DL algorithm using single-lead ECG data enabled accurate estimation of VT in patients with CVDs. The DL algorithm may be a novel way for estimating aerobic exercise threshold.Kotaro MiuraShinichi GotoYoshinori KatsumataHidehiko IkuraYasuyuki ShiraishiKazuki SatoKeiichi FukudaNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-7 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Kotaro Miura Shinichi Goto Yoshinori Katsumata Hidehiko Ikura Yasuyuki Shiraishi Kazuki Sato Keiichi Fukuda Feasibility of the deep learning method for estimating the ventilatory threshold with electrocardiography data
description	Abstract Regular aerobic physical activity is of utmost importance in maintaining a good health status and preventing cardiovascular diseases (CVDs). Although cardiopulmonary exercise testing (CPX) is an essential examination for noninvasive estimation of ventilatory threshold (VT), defined as the clinically equivalent to aerobic exercise, its evaluation requires an expensive respiratory gas analyzer and expertize. To address these inconveniences, this study investigated the feasibility of a deep learning (DL) algorithm with single-lead electrocardiography (ECG) for estimating the aerobic exercise threshold. Two hundred sixty consecutive patients with CVDs who underwent CPX were analyzed. Single-lead ECG data were stored as time-series voltage data with a sampling rate of 1000 Hz. The data of preprocessed ECG and time point at VT calculated by respiratory gas analyzer were used to train a neural network. The trained model was applied on an independent test cohort, and the DL threshold (DLT; a time of VT estimated through the DL algorithm) was calculated. We compared the correlation between oxygen uptake of the VT (VT–VO2) and the DLT (DLT–VO2). Our DL model showed that the DLT–VO2 was confirmed to be significantly correlated with the VT–VO2 (r = 0.875; P < 0.001), and the mean difference was nonsignificant (−0.05 ml/kg/min, P > 0.05), which displayed strong agreements between the VT and the DLT. The DL algorithm using single-lead ECG data enabled accurate estimation of VT in patients with CVDs. The DL algorithm may be a novel way for estimating aerobic exercise threshold.
format	article
author	Kotaro Miura Shinichi Goto Yoshinori Katsumata Hidehiko Ikura Yasuyuki Shiraishi Kazuki Sato Keiichi Fukuda
author_facet	Kotaro Miura Shinichi Goto Yoshinori Katsumata Hidehiko Ikura Yasuyuki Shiraishi Kazuki Sato Keiichi Fukuda
author_sort	Kotaro Miura
title	Feasibility of the deep learning method for estimating the ventilatory threshold with electrocardiography data
title_short	Feasibility of the deep learning method for estimating the ventilatory threshold with electrocardiography data
title_full	Feasibility of the deep learning method for estimating the ventilatory threshold with electrocardiography data
title_fullStr	Feasibility of the deep learning method for estimating the ventilatory threshold with electrocardiography data
title_full_unstemmed	Feasibility of the deep learning method for estimating the ventilatory threshold with electrocardiography data
title_sort	feasibility of the deep learning method for estimating the ventilatory threshold with electrocardiography data
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/4c47e5e0168a4323b78e621e727ada37
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Feasibility of the deep learning method for estimating the ventilatory threshold with electrocardiography data

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