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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 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
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https://hal.archives-ouvertes.fr/hal-01760784
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Submitted on : Tuesday, April 17, 2018 - 1:45:14 PM
Last modification on : Wednesday, November 3, 2021 - 7:30:32 AM

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Jean-François Pons, Zouhair Haddi, Jean-Claude Deharo, Ahmed Charai, Rachid Bouchakour, et al.. Heart rhythm characterization through induced physiological variables. Scientific Reports, Nature Publishing Group, 2017, 7 (1), ⟨10.1038/s41598-017-04998-7⟩. ⟨hal-01760784⟩

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