Heart rhythm characterization through induced physiological variables

Abstract Atrial fibrillation remains a major cause of morbi-mortality, making mass screening desirable and leading industry to actively develop devices devoted to automatic AF detection. Because there is a tendency toward mobile devices, there is a need for an accurate, rapid method for studying sho...

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Autores principales:	Jean-François Pons, Zouhair Haddi, Jean-Claude Deharo, Ahmed Charaï, Rachid Bouchakour, Mustapha Ouladsine, Stéphane Delliaux
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5936510a7b034d3a8145ff7d0f4df1f8
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spelling	oai:doaj.org-article:5936510a7b034d3a8145ff7d0f4df1f82021-12-02T16:06:19ZHeart rhythm characterization through induced physiological variables10.1038/s41598-017-04998-72045-2322https://doaj.org/article/5936510a7b034d3a8145ff7d0f4df1f82017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-04998-7https://doaj.org/toc/2045-2322Abstract Atrial fibrillation remains a major cause of morbi-mortality, making mass screening desirable and leading industry to actively develop devices devoted to automatic AF detection. Because there is a tendency toward mobile devices, there is a need for an accurate, rapid method for studying short inter-beat interval time series for real-time automatic medical monitoring. We report a new methodology to efficiently select highly discriminative variables between physiological states, here a normal sinus rhythm or atrial fibrillation. We generate induced variables using the first ten time derivatives of an RR interval time series and formally express a new multivariate metric quantifying their discriminative power to drive state variable selection. When combined with a simple classifier, this new methodology results in 99.9% classification accuracy for 1-min RR interval time series (n = 7,400), with heart rate accelerations and jerks being the most discriminant variables. We show that the RR interval time series can be drastically reduced from 60 s to 3 s, with a classification accuracy of 95.0%. We show that heart rhythm characterization is facilitated by induced variables using time derivatives, which is a generic methodology that is particularly suitable to real-time medical monitoring.Jean-François PonsZouhair HaddiJean-Claude DeharoAhmed CharaïRachid BouchakourMustapha OuladsineStéphane DelliauxNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-13 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jean-François Pons Zouhair Haddi Jean-Claude Deharo Ahmed Charaï Rachid Bouchakour Mustapha Ouladsine Stéphane Delliaux Heart rhythm characterization through induced physiological variables
description	Abstract Atrial fibrillation remains a major cause of morbi-mortality, making mass screening desirable and leading industry to actively develop devices devoted to automatic AF detection. Because there is a tendency toward mobile devices, there is a need for an accurate, rapid method for studying short inter-beat interval time series for real-time automatic medical monitoring. We report a new methodology to efficiently select highly discriminative variables between physiological states, here a normal sinus rhythm or atrial fibrillation. We generate induced variables using the first ten time derivatives of an RR interval time series and formally express a new multivariate metric quantifying their discriminative power to drive state variable selection. When combined with a simple classifier, this new methodology results in 99.9% classification accuracy for 1-min RR interval time series (n = 7,400), with heart rate accelerations and jerks being the most discriminant variables. We show that the RR interval time series can be drastically reduced from 60 s to 3 s, with a classification accuracy of 95.0%. We show that heart rhythm characterization is facilitated by induced variables using time derivatives, which is a generic methodology that is particularly suitable to real-time medical monitoring.
format	article
author	Jean-François Pons Zouhair Haddi Jean-Claude Deharo Ahmed Charaï Rachid Bouchakour Mustapha Ouladsine Stéphane Delliaux
author_facet	Jean-François Pons Zouhair Haddi Jean-Claude Deharo Ahmed Charaï Rachid Bouchakour Mustapha Ouladsine Stéphane Delliaux
author_sort	Jean-François Pons
title	Heart rhythm characterization through induced physiological variables
title_short	Heart rhythm characterization through induced physiological variables
title_full	Heart rhythm characterization through induced physiological variables
title_fullStr	Heart rhythm characterization through induced physiological variables
title_full_unstemmed	Heart rhythm characterization through induced physiological variables
title_sort	heart rhythm characterization through induced physiological variables
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/5936510a7b034d3a8145ff7d0f4df1f8
work_keys_str_mv	AT jeanfrancoispons heartrhythmcharacterizationthroughinducedphysiologicalvariables AT zouhairhaddi heartrhythmcharacterizationthroughinducedphysiologicalvariables AT jeanclaudedeharo heartrhythmcharacterizationthroughinducedphysiologicalvariables AT ahmedcharai heartrhythmcharacterizationthroughinducedphysiologicalvariables AT rachidbouchakour heartrhythmcharacterizationthroughinducedphysiologicalvariables AT mustaphaouladsine heartrhythmcharacterizationthroughinducedphysiologicalvariables AT stephanedelliaux heartrhythmcharacterizationthroughinducedphysiologicalvariables
_version_	1718385023095144448

Heart rhythm characterization through induced physiological variables

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