Key result
Wavelet-approximated HRV metrics increase prior to paroxysmal AF onset in ~100% of patients.
Why the study?
Does continuous time series analysis of RR interval standard deviation and 0.99 quantile spectral entropy predict the onset of paroxysmal atrial fibrillation episodes in patients with paroxysmal atrial fibrillation?
Observational (n=22)
Does continuous time series analysis of RR interval standard deviation and 0.99 quantile spectral entropy predict the onset of paroxysmal atrial fibrillation episodes in patients with paroxysmal atrial fibrillation?
Combining RR interval standard deviation and 0.99 quantile spectral entropy with wavelet smoothing shows potential as a real-time mobile alert method to warn patients of an impending paroxysmal atrial fibrillation episode.
Answers are grounded in this paper and cite their sources.
May enable preemptive pAF alerts via RR metrics; leaves open prospective validation in larger cohorts.
BACKGROUND: Knowledge of the onset of atrial fibrillation (AF) episodes in patients with paroxysmal atrial fibrillation (PAF) will enable them to better manage this condition. Current advances in mobile technology allow RR interval data to be obtained in real time. An analysis technique using RR interval data is presented with a view to alert a subject before a PAF episode. METHOD: The method is based on a time series of standard deviation and 0.99 quantile values of the spectral entropy, constructed from RR data. The RR data are taken from three time periods. The first time period has no occurrences of AF for 45 min to either side of the time period. The second time period just precedes an AF attack. Both of these are of thirty minutes duration. The third time period of approximately 5 min follows the second, and is when AF occurs. RESULTS: Twenty-two PAF subjects were studied and in all cases there was a steady increase in the values of these indices as the onset of the AF attack approached. CONCLUSION: This method of analysis of RR interval data shows potential use to alert a PAF subject before the onset of an AF episode.
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R. A. Thuraisingham (2015) conducted an observational in Paroxysmal atrial fibrillation (n=22). Time series analysis of RR interval standard deviation (SD) and 0.99 quantile of spectral entropy (SE99) with wavelet approximation vs. Baseline 30-minute sinus rhythm period distant from any paroxysmal atrial fibrillation episode (Region A) was evaluated on Increase in mean values of wavelet-approximated standard deviation (SD) and 0.99 quantile of spectral entropy (SE99) from distant baseline (Region A) to pre-onset (Region A+B) and onset (Region A+B+C) (p=3.2e-5). Wavelet-approximated RR interval standard deviation and 0.99 quantile spectral entropy increased before paroxysmal atrial fibrillation onset in 100% of 22 subjects.
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