Statistical and Diagnostic Properties of pRRx Parameters in Atrial Fibrillation Detection

Buś, Szymon; Jędrzejewski, Konrad; Guzik, Przemysław

doi:10.3390/jcm11195702

Cited by 6 publications

(17 citation statements)

References 48 publications

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“…Figure 7 shows histograms of accuracy, sensitivity, specificity, and DOR for comparing pRR31 and pRR3.25%. Although using x = 31 ms (pRR31) helps improve all metrics compared to x = 50 ms (pRR50) [ 3 ], the x = 3.25% (pRR3.25%) even further improves the distinction between 1 and min ECGs with AF and SR. The histograms demonstrate that the better performance of pRR3.25% is not random because there is no overlap between distributions of classification metrics for pRR31 and pRR3.25%.…”

Section: Resultsmentioning

confidence: 99%

“…We used the database’s R-wave location information and rhythm onset information and included only uninterrupted 1-min ECG segments with AF or SR, excluding segments with other rhythms. We applied the same preprocessing approach as in our previous study, dividing the original and continuous RR interval series into 1-min, non-overlapping segments containing only SR or AF [ 3 ].…”

Section: Materials and Methodsmentioning

confidence: 99%

“…To estimate the 95% confidence interval (CI) for each classification metric, we used nonparametric bootstrap with 5000 samples [ 14 ]. Additionally, we compared the diagnostic properties of the pRR31 parameter (the best among pRRx [ 3 ]) against the best pRRx% from the perspective of AF detection.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…Many various heart rate variability (HRV) parameters are used alone or in combinations to distinguish atrial fibrillation (AF) from sinus rhythm (SR) [ 1 , 2 , 3 ]. We and some other authors have shown that the percentage of successive RR interval differences equal to or greater than 50 ms (pRR50) outperforms other HRV parameters for this purpose [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…In over 60,000 electrocardiograms (ECGs) of 1-min duration with either SR or AF, pRR31, i.e., pRRx for x = 31 ms, was the most effective for AF diagnosis. We reported that the diagnostic odds ratio (DOR) in AF detection was 194 for pRR31, which significantly outperformed pRR50 (DOR of 173) [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

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A New Approach to Detecting Atrial Fibrillation Using Count Statistics of Relative Changes between Consecutive RR Intervals

Buś

Jędrzejewski

Guzik

2023

JCM

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Background: The ratio of the difference between neighboring RR intervals to the length of the preceding RR interval (x%) represents the relative change in the duration between two cardiac cycles. We investigated the diagnostic properties of the percentage of relative RR interval differences equal to or greater than x% (pRRx%) with x% in a range between 0.25% and 25% for the distinction of atrial fibrillation (AF) from sinus rhythm (SR). Methods: We used 1-min ECG segments with RR intervals with either AF (32,141 segments) or SR (32,769 segments) from the publicly available Physionet Long-Term Atrial Fibrillation Database (LTAFDB). The properties of pRRx% for different x% were analyzed using the statistical procedures and metrics commonly used to characterize diagnostic methods. Results: The distributions of pRRx% for AF and SR differ significantly over the whole studied range of x% from 0.25% to 25%, with particularly outstanding diagnostic properties for the x% range of 1.5% to 6%. However, pRR3.25% outperformed other pRRx%. Firstly, it had one of the highest and closest to perfect areas under the curve (0.971). For pRR3.25%, the optimal threshold for distinction AF from SR was set at 75.32%. Then, the accuracy was 95.44%, sensitivity was 97.16%, specificity was 93.76%, the positive predictive value was 93.85%, the negative predictive value was 97.11%, and the diagnostic odds ratio was 514. The excellent diagnostic properties of pRR3.25% were confirmed in the publicly available MIT–BIH Atrial Fibrillation Database. In a direct comparison, pRR3.25% outperformed the diagnostic properties of pRR31 (the percentage of successive RR intervals differing by at least 31 ms), i.e., so far, the best single parameter differentiating AF from SR. Conclusions: A family of pRRx% parameters has excellent diagnostic properties for AF detection in a range of x% between 1.5% and 6%. However, pRR3.25% outperforms other pRRx% parameters and pRR31 (until now, probably the most robust single heart rate variability parameter for AF diagnosis). The exquisite pRRx% diagnostic properties for AF and its simple computation make it well-suited for AF detection in modern ECG technologies (mobile/wearable devices, biopatches) in long-term monitoring. The diagnostic properties of pRRx% deserve further exploration in other databases with AF.

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Section: Resultsmentioning

confidence: 99%

Section: Materials and Methodsmentioning

confidence: 99%

Section: Materials and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A New Approach to Detecting Atrial Fibrillation Using Count Statistics of Relative Changes between Consecutive RR Intervals

Buś

Jędrzejewski

Guzik

2023

JCM

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Healthy Young POLes – HYPOL database with synchronised beat-to-beat heart rate and blood pressure signals

Guzik,

Krauze,

Wykrętowicz

et al. 2023

JMS

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Data sharing in medical research entails making research data available to other researchers for review, re-use, and collaboration purposes. This paper seeks to describe the HYPOL (Healthy Young POLes) database, which has been prepared for sharing. This database houses the clinical characteristics and beat-to-beat cardiovascular time series of 278 individuals of Polish descent, all aged between 19 and 30 years. The data were collected from healthy volunteers who participated in multiple projects carried out at the Department of Cardiology-Intensive Therapy research laboratory, Poznan University of Medical Sciences, Poznan, Poland. The cardiovascular time series data was obtained from non-invasive continuous finger blood pressure and ECG recordings, with sessions lasting up to 45 minutes. The HYPOL database includes an xls file detailing the main clinical characteristics and text files that capture ECG-derived RR intervals, finger systolic, diastolic, and mean blood pressure values, as well as the duration of interbeat intervals. There are data from 149 women (53.6% of the total) and 129 men. The median age of all participants studied is 24 years, their BMI was <24 kg/m2, pulse rate and blood pressure were normal. The median duration of the recordings was almost 30 minutes. In addition, we summarise selected parameters of heart rate variability (HRV) and heart rate asymmetry (HRA). The HYPOL database is available at hypol.ump.edu.pl. The download of data is free of charge after simple registration. Researchers and engineers can use the database for their purposes, except for selling it. The data can be used for testing various mathematical algorithms for HRV, HRA, blood pressure variability and asymmetry, and baroflex function.

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Statistical and Diagnostic Properties of pRR30, pRR3.25% and Asymmetrical Entropy Descriptors in Atrial Fibrillation Detection

Biczuk,

Buś,

Żurek

et al. 2024

Preprint

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Background: Early detection of atrial fibrillation (AF) is essential to prevent stroke and other cardiac and embolic complications. We compared the diagnostic properties for AF detection of the percentage of successive RR interval differences greater than or equal to 30 ms or 3.25% of the previous RR interval (pRR30 and pRR3.25%, respectively), and asymmetric entropy descriptors of RR intervals. Previously, both pRR30 and pRR3.25% outperformed many other heart rate variability (HRV) parameters in distinguishing AF from sinus rhythm (SR) in 60-second electrocardiograms (ECGs). Methods: The 60-s segments with RR intervals were extracted from the publicly available Physionet Long-Term Atrial Fibrillation Database (84 recording, 24-hour Holter ECG). There were 31753 60-s segments of AF and 32073 60-s segments of SR. The diagnostic properties of all parameters were analysed with the receiver operator curve analysis, confusion matrix and logistic regression. The best model with pRR30, pRR3.25% and total entropic features (H) had the largest area under the curve (AUC) – 0.98 compared to 0.959 for pRR30 - and 0.972 for pRR3.25%. However, the differences in AUC between pRR30, pRR3.25% alone and the combined model were negligible from a practical point of view. Moreover, combining pRR30, pRR3.25% with H significantly increased the number of false-negative cases by more than threefold. Conclusions: Asymmetric entropy has some potential in differentiating AF from SR in the 60-s RR interval time series, but the addition of these parameters does not seem to make a relevant difference compared to pRR30 and especially pRR3.25%.

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Statistical and Diagnostic Properties of pRRx Parameters in Atrial Fibrillation Detection

Cited by 6 publications

References 48 publications

A New Approach to Detecting Atrial Fibrillation Using Count Statistics of Relative Changes between Consecutive RR Intervals

A New Approach to Detecting Atrial Fibrillation Using Count Statistics of Relative Changes between Consecutive RR Intervals

Healthy Young POLes – HYPOL database with synchronised beat-to-beat heart rate and blood pressure signals

Statistical and Diagnostic Properties of pRR30, pRR3.25% and Asymmetrical Entropy Descriptors in Atrial Fibrillation Detection

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