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.
Highlights
Despite constant improvements in the management of atrial fibrillation (AF), this arrhythmia remains a major cause of mortality and severe morbidity
We developed a new approach for characterizing physiological states to solve medical diagnostic challenges and tested it on 7,600 1-min heart rate time series
We developed a methodology based on the proposed γ-metric to ensure that the selected induced variables offer high discriminative power to distinguish normal sinus rhythm (NSR) from AF
Summary
Despite constant improvements in the management of atrial fibrillation (AF), this arrhythmia remains a major cause of mortality and severe morbidity. Most AF detection methods are based on inter-beat interval time series analysis. The findings are acceptable in terms of sensitivity and specificity, univariate analysis often refers to a unique feature to make decisions. The random forests analysis applied to five features (mean of RR interval segment, standard deviation, coefficient of sample entropy, local dynamics score and detrended fluctuation analysis) had positive predictive values of 97, 98 and 90% for AF, NSR, and SR with premature beats, respectively. This classifier assumed atrial flutter to be the same as AF. The classifier achieved 83.33% correct classification of the studied diseases
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