Deriving electrophysiological phenotypes of paroxysmal atrial fibrillation based on the characteristics of heart rate variability

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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.

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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

References

  1. Inohara T., Shrader P., Pieper K., Blanco R.G., Tho­mas L., Singer D.E., Freeman J.V., Allen L.A., Fona­row G.C., Gersh B., Ezekowitz M.D. Association of atrial fibrillation clinical phenotypes with treatment patterns and outcomes: a multicenter registry study. JAMA Cardiol. 2018; 3 (1): 54–63. doi: 10.1001/jamacardio.2017.4665.
  2. Goyal P., Almarzooq Z.I., Cheung J., Kamel H., Krishnan U., Feldman D.N., Horn E.M., Kim L.K. Atrial fibrillation and heart failure with preserved ejection fraction: insights on a unique clinical phenotype from a natio­nally-representative United States cohort. Intern. J. Cardiol. 2018; 266: 112–118. doi: 10.1016/j.ijcard.2018.02.007.
  3. Husser D., Büttner P., Ueberham L., Dinov B., Sommer P., Arya A., Hindricks G., Bollmann A. Association of atrial fibrillation susceptibility genes, atrial fibrillation phenotypes and response to catheter ablation: a gene-based analysis of GWAS data. J. Translational Med. 2017; 15: 71. doi: 10.1186/s12967-017-1170-3.
  4. Banerjee A., Allan V., Denaxas S., Shah A., Kotecha D., Lambiase P.D., Joseph J., Lund L.H., Hemingway H. Subtypes of atrial fibrillation with concomitant valvular heart disease derived from electronic health records: phenotypes, population prevalence, trends and prognosis. EP Europace. 2019; 21 (12): 1776–1784. doi: 10.1093/europace/euz220.
  5. Dimmer C., Szili-Torok T., Tavernier R., Verstraten T., Jordaens L.J. Initiating mechanisms of paro­xysmal atrial fibrillation. Europace. 2003; 5 (1): 1–9. doi: 10.1053/eupc.2002.0273.
  6. Kusayama T., Wan J., Doytchinova A., Wong J., Kabir R.A., Mitscher G., Straka S., Shen C., Everett IV T.H., Chen P.S. Skin sympathetic nerve activity and the temporal clustering of cardiac arrhythmias. JCI Insight. 2019; 4 (4): e125853. doi: 10.1172/jci.insight.125853.
  7. Shaffer F., Ginsberg J.P. An overview of heart rate variability metrics and norms. Front. Public Health. 2017; 5: 258. doi: 10.3389/fpubh.2017.00258.
  8. Piskorski J., Guzik P. Asymmetric properties of long-term and total heart rate variability. Med. Biol. Engineering & Computing. 2011; 49 (11): 1289–1297. doi: 10.1007/s11517-011-0834-z.
  9. Yan C., Li P., Ji L., Yao L., Karmakar C., Liu C. Area asymmetry of heart rate variability signal. Biomed. Engineering Online. 2017; 16 (1): 112. doi: 10.1186/s12938-017-0402-3.
  10. Costa M.D., Davis R.B., Goldberger A.L. Heart rate fragmentation: a new approach to the analysis of cardiac interbeat interval dynamics. Front. Physiol. 2017; 8: 255. doi: 10.3389/fphys.2017.00255.
  11. Petrutiu S., Sahakian A.V., Swiryn S. Abrupt chan­ges in fibrillatory wave characteristics at the termination of paroxysmal atrial fibrillation in humans. Europace. 2007; 9 (7): 466–470. doi: 10.1093/europace/eum096.
  12. Gan G., Ma C., Wu J. Data clustering: theory, algorithms, and applications. Philadelphia: SIAM. 2020. 406 p. doi: 10.1137/1.9781611976335.bm.
  13. Majeed F., Asim M., Ali Abbas S., Jaleel A., Majid A., Awais Hassan M., Ahmad F., Shafiq M. Detection of atrial fibrillation and normal sinus rhythm using multiple machine learning classifiers. J. Med. Imag. Health Inform. 2021; 11 (5): 1453–1462. doi: 10.1166/jmihi.2021.3447.
  14. Piccirillo G., Ogawa M., Song J., Chong V.J., Joung B., Han S., Magrì D., Chen L.S., Lin S.F., Chen P.S. Power spectral analysis of heart rate variability and autonomic nervous system activity measured directly in healthy dogs and dogs with tachycardia-induced heart fai­lure. Heart Rhythm. 2009; 6 (4): 546–552. doi: 10.1016/j.hrthm.2009.01.006.
  15. Chen P.S., Chen L.S., Fishbein M.C., Lin S.F., Nattel S. Role of the autonomic nervous system in atrial fibrillation: pathophysiology and therapy. Circulation Res. 2014; 114 (9): 1500–1515. doi: 10.1161/CIRCRESAHA.114.303772.

Supplementary files

Supplementary Files
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1. Рис. 1. Диаграмма корреляции между индексами вариабельности сердечного ритма у пациентов с пароксизмальной формой фибрилляции предсердий. При помощи цветовой шкалы2 указаны абсолютные значения коэффициентов корреляции между признаками. Близость индексов друг к другу продемонстрирована с помощью дендрограмм на осях

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2. Рис. 2. Пространство характеристик вариабельности сердечного ритма после применения метода главных компонент. Пятимерное пространство главных компонент представлено на рисунке в виде трёхмерных и двухмерных проекций соответственно. Для наглядности распределения графики снизу имеют единую ось Y (компонент 1). Выбросы отмечены оранжевыми3 точками

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3. Рис. 3. Дендрограмма, отражающая близость параметров вариабельности сердечного ритма у пациентов. В подписях по оси Y написано количество пациентов в группе, по оси X отложено расстояние между группами пациентов. ­Хорошо прослеживаются два кластера данных, предположительно связанных с различными фенотипами фибрилляции предсердий, которые выделяются у самого основания дендрограммы

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4. Рис. 4. Визуализация данных после удаления выбросов в двух проекциях. Изображения на верхних панелях построены на основе 0-й и 1-й главных компонент, внизу — на основе 3-й и 4-й компонент. Данные, относящиеся к разным фенотипам, обозначены прямоугольниками и треугольниками соответственно, пунктирная линия на верхней панели показывает явное разделение кластеров в пространстве. Стрелки демонстрируют направления градиентов, по которым наблюдается увеличение признаков. Верхние и нижние графики имеют попарно общие оси Y

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