A methodology using time derivatives of RR interval time series combined with a simple classifier achieved 99.9% classification accuracy for 1-minute recordings in distinguishing normal sinus rhythm from atrial fibrillation.
Does variable selection using γ-analysis applied to time derivatives of RR intervals improve the classification accuracy of atrial fibrillation versus normal sinus rhythm?
The use of time derivatives of RR interval time series combined with γ-analysis enables highly accurate and rapid automatic detection of atrial fibrillation, suitable for real-time medical monitoring.
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.
Pons et al. (Wed,) conducted a other in Atrial Fibrillation (n=7,400). Time derivatives of RR interval time series (induced variables) with logistic regression classifier was evaluated on Classification accuracy for distinguishing normal sinus rhythm from atrial fibrillation. A methodology using time derivatives of RR interval time series combined with a simple classifier achieved 99.9% classification accuracy for 1-minute recordings in distinguishing normal sinus rhythm from atrial fibrillation.
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