Heart rhythm characterization through induced physiological variables

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-...

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Bibliographic Details
Published in:Scientific reports 2017-07, Vol.7 (1), p.5059-13, Article 5059
Main Authors: Pons, Jean-François, Haddi, Zouhair, Deharo, Jean-Claude, Charaï, Ahmed, Bouchakour, Rachid, Ouladsine, Mustapha, Delliaux, Stéphane
Format: Article
Language:English
Subjects:
631/443/592/75/29/1309
639/705/1041
Algorithms
Cardiac arrhythmia
Classification
Electrocardiography
Fibrillation
Heart rate
Heart Rate - physiology
Humanities and Social Sciences
Humans
Life Sciences
multidisciplinary
Multivariate Analysis
Physiology
Science
Science (multidisciplinary)
Sinus
Time Factors
Time series
Variables
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Summary: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.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-017-04998-7