Physiopathological Insights into Atrial Fibrillation Onset through Heart Rate Variability Correlations

Abstract

Atrial fibrillation (AF) is the most common cardiac arrhythmia and is associated with increased morbidity and mortality. Early prediction of AF episodes remains a clinical challenge. This study aimed to generate physiopathological hypotheses for AF onset by analyzing correlations among heart rate variability (HRV) parameters in patients monitored via long-term Holter ECG. We utilized the IRIDIA-AF database, comprising 1319 paroxysmal AF episodes from 872 patients. An XGBoost machine learning model was developed to predict AF onset within 24 h using short- and long-term HRV features, fragmentation indices, and non-linear metrics extracted during sinus rhythm. Model interpretation was performed using SHapley Additive exPlanations (SHAP) values, and dimensionality reduction techniques were applied for data visualization. The model achieved an area under the receiver operating characteristic curve of 0.919 and an area under the precision-recall curve of 0.919, with high accuracy, sensitivity, and specificity. Key predictive features included short-term vagal activity, HRV fragmentation indices, and non-linear parameters, highlighting the role of the autonomic nervous system in AF initiation. Our findings suggest that distinct physiological profiles, detectable via HRV, may underlie AF susceptibility and could inform personalized monitoring and prevention strategies.

Keywords

Atrial fibrillation / Machine learning / Onset prediction / Physiopathology / Heart rate variability / Heart rate fragmentation / Non-linearities

Supplementary Materials

The following supporting information can be found at: https://www.sciepublish.com/article/pii/657, Table S1. Heart rate variability (HRV) features used in the analysis.

Acknowledgments

We would like to thank Laurent Groben, Thomas Nguyen, Bernard Deruyter, and Pascal Godart for providing us with some of the Holter recordings.

Author Contributions

Conceptualization, J.-M.G.; Methodology, J.-M.G., C.G., F.M.; Software C.G., F.M.; Validation, H.B., S.C. Formal Analysis, J.-M.G. investigation, J.-M.G., C.G., F.M.; Writing—Original Draft Preparation, J.-M.G.; Writing—Review & Editing, J.-M.G., C.G., F.M.; Supervision, S.C., H.B.; Project Administration, J.-M.G.; Funding Acquisition, C.G., F.M.

Ethics Statement

The study was conducted according to the guidelines of the Declaration of Helsinki. Ethical approval for the use of these data was obtained from the relevant committees: protocol P2017/431 (27 October 2017) and CNER protocol 202101/01 (1 January 2021).

Informed Consent Statement

Patient consent was waived because this is a retrospective study and it was impossible to locate the patients who had benefited from Holter monitoring; furthermore, the data anonymization process means that it is not possible to locate the patients to ask for their consent.

Data Availability Statement

The first part of the database (IRIDIA v1) can be downloaded from Zenodo: Cédric Gilon, Jean-Marie Grégoire, Marianne Mathieu, Stéphane Carlier, & Hugues Bersini (2023). IRIDIA-AF, a large paroxysmal atrial fibrillation long-term electrocardiogram monitoring database (1.0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8405941.

Funding

This work was supported by the French Community of Belgium [FRIA funding: FC 038733] and from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska Curie grant agreement No 101034383.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

[1] Van Gelder IC, Rienstra M, Bunting KV, Casado-Arroyo R, Caso V, Crijns HJ, et al. 2024 ESC Guidelines for the management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): Developed by the task force for the management of atrial fibrillation of the European Society of Cardiology (ESC), with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC. Endorsed by the European Stroke Organisation (ESO). Eur. Heart J. 2024, 45, 3314-3414. doi:10.1093/EURHEARTJ/EHAE176.

[2] Kalarus Z, Mairesse GH, Sokal A, Boriani G, Średniawa B, Casado-Arroyo R, et al. Searching for atrial fibrillation: looking harder, looking longer, and in increasingly sophisticated ways. An EHRA position paper. EP Eur. 2023, 25, 185-198. doi:10.1093/europace/euac144.

[3] Lane DA, Skjøth F, Lip GY, Larsen TB, Kotecha D. Temporal trends in incidence, prevalence, and mortality of atrial fibrillation in primary care. J. Am. Heart Assoc. 2017, 6, e005155. doi:10.1161/JAHA.116.005155.

[4] Ma Q, Zhu J, Zheng P, Zhang J, Xia X, Zhao Y, et al. Global burden of atrial fibrillation/flutter: Trends from 1990 to 2019 and projections until 2044. Heliyon 2024, 10, e24052. doi:10.1016/J.HELIYON.2024.E24052.

[5] Attia ZI, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Deshmukh AJ, Gersh BJ, et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet 2019, 394, 861-867.

[6] Gregoire J, Gilon C, Carlier S, Bersini H. Role of the autonomic nervous system and premature atrial contractions in short-term paroxysmal atrial fibrillation forecasting: Insights from machine learning models. Arch. Cardiovasc. Dis. 2022, 115, 377-387. doi:10.1016/J.ACVD.2022.04.006.

[7] Malik M, Bigger JT, Camm AJ, Kleiger RE, Malliani A, Moss AJ, et al.Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Eur. Heart J. 1996, 17, 354-381. doi:10.1093/oxfordjournals.eurheartj.a014868.

[8] Ariza-Garzón MJ, Arroyo J, Caparrini A, Segovia-Vargas MJ. Explainability of a Machine Learning Granting Scoring Model in Peer-to-Peer Lending. IEEE Access 2020, 8, 64873-64890. doi:10.1109/ACCESS.2020.2984412.

[9] Greenacre M, Groenen PJ, Hastie T, d’Enza AI, Markos A, Tuzhilina E.Principal component analysis. Nat. Rev. Methods Primers 2022, 2, 100. doi:10.1038/S43586-022-00184-W.

[10] McInnes L, Healy J, Melville J. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. February 2018. Available online: https://arxiv.org/pdf/1802.03426 (accessed on 23 June 2025).

[11] Maaten LVD, Hinton G.Visualizing Data using t-SNE. J. Mach. Learn. Res. 2008, 9, 2579-2605.

[12] Choi A, Shin H. Quantitative analysis of the effect of an ectopic beat on the heart rate variability in the resting condition. Front. Physiol. 2018, 9, 922. doi:10.3389/FPHYS.2018.00922/BIBTEX.

[13] Shaffer F, Ginsberg JP. An Overview of Heart Rate Variability Metrics and Norms. Front. Public Health 2017, 5, 258. doi:10.3389/fpubh.2017.00258.

[14] Costa MD, Davis RB, Goldberger AL. Heart rate fragmentation: a new approach to the analysis of cardiac interbeat interval dynamics. Front. Phys. 2017, 8, 255.

[15] Costa MD, Redline S, Soliman EZ, Goldberger AL, Heckbert SR, Rey HA. Fragmented sinoatrial dynamics in the prediction of atrial fibrillation: The Multi-Ethnic Study of Atherosclerosis. Am. J. Physiol. -Heart Circ. Physiol. 2021, 320, H256-H271. doi:10.1152/ajpheart.00421.2020.

[16] Babloyantz A, Destexhe A. Is the normal heart a periodic oscillator? Biol. Cybern. 1988, 58, 203-211. doi:10.1007/BF00364139.

[17] Grégoire JM, Gilon C, Carlier S, Bersini H. Autonomic nervous system assessment using heart rate variability. Acta Cardiol. 2023, 78, 648-662. doi:10.1080/00015385.2023.2177371.

[18] Brennan M, Palaniswami M, Kamen P. Do existing measures of Poincaré plot geometry reflect non-linear features of heart rate variability? IEEE Trans. Biomed. Eng. 2001, 48, 1342-1347. doi:10.1109/10.959330.

[19] Deka B, Deka D. Non-linear analysis of heart rate variability signals in meditative state: A review and perspective. Biomed. Eng. Online 2023, 22, 1-31. doi:10.1186/S12938-023-01100-3/FIGURES/20.

[20] Raghunath S, Pfeifer JM, Ulloa-Cerna AE, Nemani A, Carbonati T, Jing L, et al. Deep Neural Networks Can Predict New-Onset Atrial Fibrillation from the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation-Related Stroke. Circulation 2021, 143, 1287-1298. doi:10.1161/CIRCULATIONAHA.120.047829.

[21] Baek YS, Lee SC, Choi W, Kim DH. A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm. Sci. Rep. 2021, 11, 12818. doi:10.1038/S41598-021-92172-5.

[22] Yuan N, Duffy G, Dhruva SS, Oesterle A, Pellegrini CN, Theurer J, et al. Deep Learning of Electrocardiograms in Sinus Rhythm From US Veterans to Predict Atrial Fibrillation. JAMA Cardiol. 2023, 8, 1131-1139. doi:10.1001/JAMACARDIO.2023.3701.

[23] Singh JP, Fontanarava J, de Massé G, Carbonati T, Li J, Henry C, et al. Short term prediction of Atrial Fibrillation from ambulatory monitoring ECG using a deep neural network. Eur. Heart J. -Digit. Health 2022, 3, 208-217. doi:10.1093/EHJDH/ZTAC014.