Heart rhythm characterization through induced physiological variables View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-12

AUTHORS

Jean-François Pons, Zouhair Haddi, Jean-Claude Deharo, Ahmed Charaï, Rachid Bouchakour, Mustapha Ouladsine, Stéphane Delliaux

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. More... »

PAGES

5059

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-017-04998-7

DOI

http://dx.doi.org/10.1038/s41598-017-04998-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1090535816

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/28698645


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