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

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.

Keywords

paroxysmal atrial fibrillation / arrhythmia phenotyping / exploratory data analysis / heart rate variability / HRV / Holter monitor

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.

RIGHTS & PERMISSIONS

Eco-Vector