Quantification of Interpatient 12-lead ECG Variabilities within a Healthy Cohort

The morphology of the electrocardiogram (ECG) varies among different healthy subjects due to anatomical and structural reasons, such as for example the shape of the heart geometry or the position and size of surrounding organs in the torso. Knowledge about these ECG morphology changes could be used...

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Autores principales:	Nagel Claudia, Pilia Nicolas, Loewe Axel, Dössel Olaf
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2020
Materias:	ecg variabilities ecg features electrophysiological model parameterization Medicine R
Acceso en línea:	https://doaj.org/article/2c9841fddab2443fab5753f1cc40654d
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spelling	oai:doaj.org-article:2c9841fddab2443fab5753f1cc40654d2021-12-05T14:10:43ZQuantification of Interpatient 12-lead ECG Variabilities within a Healthy Cohort2364-550410.1515/cdbme-2020-3127https://doaj.org/article/2c9841fddab2443fab5753f1cc40654d2020-09-01T00:00:00Zhttps://doi.org/10.1515/cdbme-2020-3127https://doaj.org/toc/2364-5504The morphology of the electrocardiogram (ECG) varies among different healthy subjects due to anatomical and structural reasons, such as for example the shape of the heart geometry or the position and size of surrounding organs in the torso. Knowledge about these ECG morphology changes could be used to parameterize electrophysiological simulations of the human heart. In this work, we detected the boundaries of ECG waveforms, i.e. the P-wave, the QRS-complex and the T-wave, in 12- lead ECGs from 918 healthy subjects in the Physionet Computing in Cardiology Challenge 2020 Database with the IBT openECG toolbox. Subsequently, we obtained the onset, the peak and the offset of each P-wave, QRS-complex and T-wave in the signal. In this way, the duration of the P-wave, the QRScomplex and the T-wave, the PQ-, RR- and the QT-interval as well as the amplitudes of the P-wave, the Q-, R- and Speak and the T-wave in each lead were extracted from the 918 healthy ECGs. Their statistical distributions and correlation between each other were assessed. The highest variabilities among the 918 healthy subject were found for the RR interval and the amplitudes of the QRScomplex. The highest correlation was observed for feature pairs that represent the same feature in different leads. Especially the R-peak amplitudes showed a strong correlation across different leads. The calculated feature distributions can be used to optimize the parameters of populations of cardiac electrophysiological models. In this way, realistic in-silico generated surface ECGs can be simulated in large scale and could be used as input data for machine learning algorithms for a classification of cardiovascular diseases.Nagel ClaudiaPilia NicolasLoewe AxelDössel OlafDe Gruyterarticleecg variabilitiesecg featureselectrophysiological model parameterizationMedicineRENCurrent Directions in Biomedical Engineering, Vol 6, Iss 3, Pp 493-496 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	ecg variabilities ecg features electrophysiological model parameterization Medicine R
spellingShingle	ecg variabilities ecg features electrophysiological model parameterization Medicine R Nagel Claudia Pilia Nicolas Loewe Axel Dössel Olaf Quantification of Interpatient 12-lead ECG Variabilities within a Healthy Cohort
description	The morphology of the electrocardiogram (ECG) varies among different healthy subjects due to anatomical and structural reasons, such as for example the shape of the heart geometry or the position and size of surrounding organs in the torso. Knowledge about these ECG morphology changes could be used to parameterize electrophysiological simulations of the human heart. In this work, we detected the boundaries of ECG waveforms, i.e. the P-wave, the QRS-complex and the T-wave, in 12- lead ECGs from 918 healthy subjects in the Physionet Computing in Cardiology Challenge 2020 Database with the IBT openECG toolbox. Subsequently, we obtained the onset, the peak and the offset of each P-wave, QRS-complex and T-wave in the signal. In this way, the duration of the P-wave, the QRScomplex and the T-wave, the PQ-, RR- and the QT-interval as well as the amplitudes of the P-wave, the Q-, R- and Speak and the T-wave in each lead were extracted from the 918 healthy ECGs. Their statistical distributions and correlation between each other were assessed. The highest variabilities among the 918 healthy subject were found for the RR interval and the amplitudes of the QRScomplex. The highest correlation was observed for feature pairs that represent the same feature in different leads. Especially the R-peak amplitudes showed a strong correlation across different leads. The calculated feature distributions can be used to optimize the parameters of populations of cardiac electrophysiological models. In this way, realistic in-silico generated surface ECGs can be simulated in large scale and could be used as input data for machine learning algorithms for a classification of cardiovascular diseases.
format	article
author	Nagel Claudia Pilia Nicolas Loewe Axel Dössel Olaf
author_facet	Nagel Claudia Pilia Nicolas Loewe Axel Dössel Olaf
author_sort	Nagel Claudia
title	Quantification of Interpatient 12-lead ECG Variabilities within a Healthy Cohort
title_short	Quantification of Interpatient 12-lead ECG Variabilities within a Healthy Cohort
title_full	Quantification of Interpatient 12-lead ECG Variabilities within a Healthy Cohort
title_fullStr	Quantification of Interpatient 12-lead ECG Variabilities within a Healthy Cohort
title_full_unstemmed	Quantification of Interpatient 12-lead ECG Variabilities within a Healthy Cohort
title_sort	quantification of interpatient 12-lead ecg variabilities within a healthy cohort
publisher	De Gruyter
publishDate	2020
url	https://doaj.org/article/2c9841fddab2443fab5753f1cc40654d
work_keys_str_mv	AT nagelclaudia quantificationofinterpatient12leadecgvariabilitieswithinahealthycohort AT pilianicolas quantificationofinterpatient12leadecgvariabilitieswithinahealthycohort AT loeweaxel quantificationofinterpatient12leadecgvariabilitieswithinahealthycohort AT dosselolaf quantificationofinterpatient12leadecgvariabilitieswithinahealthycohort
_version_	1718371781346066432

Quantification of Interpatient 12-lead ECG Variabilities within a Healthy Cohort

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