Electrocardiogram lead selection for intelligent screening of patients with systolic heart failure

Abstract Electrocardiogram (ECG)-based intelligent screening for systolic heart failure (HF) is an emerging method that could become a low-cost and rapid screening tool for early diagnosis of the disease before the comprehensive echocardiographic procedure. We collected 12-lead ECG signals from 900...

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Autores principales:	Yu-An Chiou, Jhen-Yang Syu, Sz-Ying Wu, Lian-Yu Lin, Li Tzu Yi, Ting-Tse Lin, Shien-Fong Lin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/efd94eef9a62442ab96f063be61dd015
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spelling	oai:doaj.org-article:efd94eef9a62442ab96f063be61dd0152021-12-02T10:49:34ZElectrocardiogram lead selection for intelligent screening of patients with systolic heart failure10.1038/s41598-021-81374-62045-2322https://doaj.org/article/efd94eef9a62442ab96f063be61dd0152021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81374-6https://doaj.org/toc/2045-2322Abstract Electrocardiogram (ECG)-based intelligent screening for systolic heart failure (HF) is an emerging method that could become a low-cost and rapid screening tool for early diagnosis of the disease before the comprehensive echocardiographic procedure. We collected 12-lead ECG signals from 900 systolic HF patients (ejection fraction, EF < 50%) and 900 individuals with normal EF in the absence of HF symptoms. The 12-lead ECG signals were converted by continuous wavelet transform (CWT) to 2D spectra and classified using a 2D convolutional neural network (CNN). The 2D CWT spectra of 12-lead ECG signals were trained separately in 12 identical 2D-CNN models. The 12-lead classification results of the 2D-CNN model revealed that Lead V6 had the highest accuracy (0.93), sensitivity (0.97), specificity (0.89), and f1 scores (0.94) in the testing dataset. We designed four comprehensive scoring methods to integrate the 12-lead classification results into a key diagnostic index. The highest quality result among these four methods was obtained when Leads V5 and V6 of the 12-lead ECG signals were combined. Our new 12-lead ECG signal–based intelligent screening method using straightforward combination of ECG leads provides a fast and accurate approach for pre-screening for systolic HF.Yu-An ChiouJhen-Yang SyuSz-Ying WuLian-Yu LinLi Tzu YiTing-Tse LinShien-Fong LinNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yu-An Chiou Jhen-Yang Syu Sz-Ying Wu Lian-Yu Lin Li Tzu Yi Ting-Tse Lin Shien-Fong Lin Electrocardiogram lead selection for intelligent screening of patients with systolic heart failure
description	Abstract Electrocardiogram (ECG)-based intelligent screening for systolic heart failure (HF) is an emerging method that could become a low-cost and rapid screening tool for early diagnosis of the disease before the comprehensive echocardiographic procedure. We collected 12-lead ECG signals from 900 systolic HF patients (ejection fraction, EF < 50%) and 900 individuals with normal EF in the absence of HF symptoms. The 12-lead ECG signals were converted by continuous wavelet transform (CWT) to 2D spectra and classified using a 2D convolutional neural network (CNN). The 2D CWT spectra of 12-lead ECG signals were trained separately in 12 identical 2D-CNN models. The 12-lead classification results of the 2D-CNN model revealed that Lead V6 had the highest accuracy (0.93), sensitivity (0.97), specificity (0.89), and f1 scores (0.94) in the testing dataset. We designed four comprehensive scoring methods to integrate the 12-lead classification results into a key diagnostic index. The highest quality result among these four methods was obtained when Leads V5 and V6 of the 12-lead ECG signals were combined. Our new 12-lead ECG signal–based intelligent screening method using straightforward combination of ECG leads provides a fast and accurate approach for pre-screening for systolic HF.
format	article
author	Yu-An Chiou Jhen-Yang Syu Sz-Ying Wu Lian-Yu Lin Li Tzu Yi Ting-Tse Lin Shien-Fong Lin
author_facet	Yu-An Chiou Jhen-Yang Syu Sz-Ying Wu Lian-Yu Lin Li Tzu Yi Ting-Tse Lin Shien-Fong Lin
author_sort	Yu-An Chiou
title	Electrocardiogram lead selection for intelligent screening of patients with systolic heart failure
title_short	Electrocardiogram lead selection for intelligent screening of patients with systolic heart failure
title_full	Electrocardiogram lead selection for intelligent screening of patients with systolic heart failure
title_fullStr	Electrocardiogram lead selection for intelligent screening of patients with systolic heart failure
title_full_unstemmed	Electrocardiogram lead selection for intelligent screening of patients with systolic heart failure
title_sort	electrocardiogram lead selection for intelligent screening of patients with systolic heart failure
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/efd94eef9a62442ab96f063be61dd015
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Electrocardiogram lead selection for intelligent screening of patients with systolic heart failure

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