Deriving electrophysiological phenotypes of paroxysmal atrial fibrillation based on the characteristics of heart rate variability
- Authors: Markov NS1,2, Ushenin KS1,2,3, Bozhko YG1, Arkhipov MV1, Solovyova OE2,3
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Affiliations:
- Ural State Medical University
- Ural Federal University
- Institute of Immunology and Physiology Ural Branch of the Russian Academy of Sciences
- Issue: Vol 102, No 5 (2021)
- Pages: 778-787
- Section: Clinical experiences
- Submitted: 05.10.2021
- Accepted: 05.10.2021
- Published: 13.10.2021
- URL: https://kazanmedjournal.ru/kazanmedj/article/view/82473
- DOI: https://doi.org/10.17816/KMJ2021-778
- ID: 82473
Cite item
Abstract
Aim. To analyze heart rate variability of patients with paroxysmal atrial fibrillation and identify electrophysiological phenotypes of the disease by using methods of exploratory analysis of twenty-four-hour electrocardiographic (Holter) recordings.
Methods. 64 electrocardiogram recordings of patients with paroxysmal atrial fibrillation were selected from the open Long-Term Atrial Fibrillation Database (repository — PhysioNet). 52 indices of heart rhythm variability were calculated for each recording, including new heart rate fragmentation and asymmetry indices proposed in the last 5 years. Data analysis was carried out with machine learning methods: dimensionality reduction with principal component analysis, hierarchical clustering and outlier detection. Feature correlation was checked by the Pearson criterion, the selected patient’s subgroups were confirmed by using Mann–Whitney and Student's tests.
Results. For the vast majority of patients with paroxysmal atrial fibrillation, heart rate variability can be described by five parameters. Each of these parameters captures a distinct approach in heart rate variability classification: dispersion characteristics of interbeat intervals, frequency characteristics of interbeat intervals, measurements of heart rate fragmentation, indices based on heart rate asymmetry, mean and median of interbeat intervals. Two large phenotypes of the disease were derived based on these parameters: the first phenotype is a vagotonic profile with a significant increase of linear parasympathetic indices and paroxysmal atrial fibrillation lasting longer than 4.5 hours; the second phenotype — with increased sympathetic indices, low parasympathetic indices and paroxysms lasting up to 4.5 hours.
Conclusion. Our findings indicate the potential of nonlinear analysis in the study of heart rate variability and demonstrate the feasibility of further integration of nonlinear indices for arrhythmia phenotyping.
Full Text
About the authors
N S Markov
Ural State Medical University; Ural Federal University
Email: yakov-bozhko@yandex.ru
Russian Federation, Yekaterinburg, Russia; Yekaterinburg, Russia
K S Ushenin
Ural State Medical University; Ural Federal University; Institute of Immunology and Physiology Ural Branch of the Russian Academy of Sciences
Email: yakov-bozhko@yandex.ru
Russian Federation, Yekaterinburg, Russia; Yekaterinburg, Russia; Yekaterinburg, Russia
Y G Bozhko
Ural State Medical University
Author for correspondence.
Email: yakov-bozhko@yandex.ru
Russian Federation, Yekaterinburg, Russia
M V Arkhipov
Ural State Medical University
Email: yakov-bozhko@yandex.ru
Russian Federation, Yekaterinburg, Russia
O E Solovyova
Ural Federal University; Institute of Immunology and Physiology Ural Branch of the Russian Academy of Sciences
Email: yakov-bozhko@yandex.ru
Russian Federation, Yekaterinburg, Russia; Yekaterinburg, Russia
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