Role of the autonomic nervous system and premature atrial contractions in short-term paroxysmal atrial fibrillation forecasting: Insights from machine learning models.
[en] [en] BACKGROUND: Machine learning and deep learning techniques are now used extensively for atrial fibrillation (AF) screening, but their use for AF crisis forecasting has yet to be assessed in a clinical context.
AIMS: To assess the value of two machine learning algorithms for the short-term prediction of paroxysmal AF episodes.
METHODS: We conducted a retrospective study from an outpatient clinic. We developed a deep neural network model that was trained for a supervised binary classification, differentiating between RR interval variations that precede AF onset and RR interval variations far from any AF. We also developed a random forest model to obtain forecast results using heart rate variability variables, with and without premature atrial complexes.
RESULTS: In total, 10,484 Holter electrocardiogram recordings were screened, and 250 analysable AF onsets were labelled. The deep neural network model was able to distinguish if a given RR interval window would lead to AF onset in the next 30 beats with a sensitivity of 80.1% (95% confidence interval 78.7-81.6) at the price of a low specificity of 52.8% (95% confidence interval 51.0-54.6). The random forest model indicated that the main factor that precedes the start of a paroxysmal AF episode is autonomic nervous system activity, and that premature complexes add limited additional information. In addition, the onset of AF episodes is preceded by cyclical fluctuations in the low frequency/high frequency ratio of heart rate variability. Each peak is itself followed by an increase in atrial extrasystoles.
CONCLUSIONS: The use of two machine learning algorithms for the short-term prediction of AF episodes allowed us to confirm that the main cause of AF crises lies in an imbalance in the autonomic nervous system, and not premature atrial contractions, which are, however, required as a final firing trigger.
Disciplines :
Cardiovascular & respiratory systems
Author, co-author :
Grégoire, Jean-Marie; IRIDIA, Université Libre de Bruxelles, 1050 Bruxelles, Belgium, Department of Cardiology, UMONS (Université de Mons), 7000 Mons, Belgium. Electronic address: jean-marie.gregoire@ulb.be
Gilon, Cédric; IRIDIA, Université Libre de Bruxelles, 1050 Bruxelles, Belgium
Carlier, Stéphane ; Université de Mons - UMONS > Faculté de Médecine et de Pharmacie > Service de Cardiologie
Bersini, Hugues; IRIDIA, Université Libre de Bruxelles, 1050 Bruxelles, Belgium
Language :
English
Title :
Role of the autonomic nervous system and premature atrial contractions in short-term paroxysmal atrial fibrillation forecasting: Insights from machine learning models.
Chugh, S.S., Havmoeller, R., Narayanan, K., et al. Worldwide epidemiology of atrial fibrillation: a Global Burden of Disease 2010 Study. Circulation 129 (2014), 837–847.
Lane, D.A., Skjoth, F., Lip, G.Y.H., Larsen, T.B., Kotecha, D., Temporal trends in incidence, prevalence, and mortality of atrial fibrillation in primary care. J Am Heart Assoc, 6, 2017, e005155.
Hindricks, G., Potpara, T., Dagres, N., et al. 2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): The Task Force for the diagnosis and management of atrial fibrillation of the European Society of Cardiology (ESC). Developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC. Eur Heart J 42 (2021), 373–498.
Mairesse, G.H., Moran, P., Van Gelder, I.C., et al. Screening for atrial fibrillation: a European Heart Rhythm Association (EHRA) consensus document endorsed by the Heart Rhythm Society (HRS), Asia Pacific Heart Rhythm Society (APHRS), and Sociedad Latinoamericana de Estimulacion Cardiaca y Electrofisiologia (SOLAECE). Europace 19 (2017), 1589–1623.
Andrade, J.G., Wells, G.A., Deyell, M.W., et al. Cryoablation or drug therapy for initial treatment of atrial fibrillation. N Engl J Med 384 (2021), 305–315.
Wazni, O.M., Dandamudi, G., Sood, N., et al. Cryoballoon ablation as initial therapy for atrial fibrillation. N Engl J Med 384 (2021), 316–324.
Christophersen, I.E., Yin, X., Larson, M.G., et al. A comparison of the CHARGE-AF and the CHA2DS2-VASc risk scores for prediction of atrial fibrillation in the Framingham Heart Study. Am Heart J 178 (2016), 45–54.
Vogenberg, F.R., Isaacson Barash, C., Pursel, M., Personalized medicine: part 1: evolution and development into theranostics. Pharmacol Ther 35 (2010), 560–576.
Attia, Z.I., Noseworthy, P.A., Lopez-Jimenez, F., 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 394 (2019), 861–867.
Gorenek, B.C., Bax, J., Boriani, G., et al. Device-detected subclinical atrial tachyarrhythmias: definition, implications and management-an European Heart Rhythm Association (EHRA) consensus document, endorsed by Heart Rhythm Society (HRS), Asia Pacific Heart Rhythm Society (APHRS) and Sociedad Latinoamericana de Estimulacion Cardiaca y Electrofisiologia (SOLEACE). Europace 19 (2017), 1556–1578.
Healey, J.S., Connolly, S.J., Gold, M.R., et al. Subclinical atrial fibrillation and the risk of stroke. N Engl J Med 366 (2012), 120–129.
Abadi, M., Agarwal, A., Barham, P., et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. 2015 Available at: http://www.download.tensorflow.org/paper/whitepaper2015.pdf.
Gilon, C., Grégoire, J.-M., Bersini, H., Forecast of paroxysmal atrial fibrillation using a deep neural network. Proc Int Jt Conf Neural Networks, 2020, 1–7, 10.1109/IJCNN48605.2020.9207227.
Qin, M., Zeng, C., Liu, X., The cardiac autonomic nervous system: a target for modulation of atrial fibrillation. Clin Cardiol 42 (2019), 644–652.
Linz, D., Elliott, A.D., Hohl, M., et al. Role of autonomic nervous system in atrial fibrillation. Int J Cardiol 287 (2019), 181–188.
Linz, D., Ukena, C., Mahfoud, F., Neuberger, H.R., Bohm, M., Atrial autonomic innervation: a target for interventional antiarrhythmic therapy?. J Am Coll Cardiol 63 (2014), 215–224.
Malliani, A., Pagani, M., Lombardi, F., Cerutti, S., Cardiovascular neural regulation explored in the frequency domain. Circulation 84 (1991), 482–492.
Heart rate variability, Standards of measurement, physiological interpretation, and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Eur Heart J 17 (1996), 354–381.
Billman, G.E., The LF/HF ratio does not accurately measure cardiac sympatho-vagal balance. Front Physiol, 4, 2013, 26.
Bettoni, M., Zimmermann, M., Autonomic tone variations before the onset of paroxysmal atrial fibrillation. Circulation 105 (2002), 2753–2759.
Brembilla-Perrot, B., Houriez, P., Claudon, O., Beurrier, D., Preiss, J.P., Different action of beta-blockers on daytime and nighttime heart rate variability. Ann Noninvasive Electrocardiol 5 (2000), 158–165.
Niemela, M.J., Airaksinen, K.E., Huikuri, H.V., Effect of beta-blockade on heart rate variability in patients with coronary artery disease. J Am Coll Cardiol 23 (1994), 1370–1377.
Akselrod, S., Gordon, D., Ubel, F.A., Shannon, D.C., Berger, A.C., Cohen, R.J., Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. Science 213 (1981), 220–222.
Fioranelli, M., Piccoli, M., Mileto, G.M., et al. Analysis of heart rate variability five minutes before the onset of paroxysmal atrial fibrillation. Pacing Clin Electrophysiol 22 (1999), 743–749.
Kolb, C., Nurnberger, S., Ndrepepa, G., Zrenner, B., Schomig, A., Schmitt, C., Modes of initiation of paroxysmal atrial fibrillation from analysis of spontaneously occurring episodes using a 12-lead Holter monitoring system. Am J Cardiol 88 (2001), 853–857.
Lombardi, F., Tarricone, D., Tundo, F., Colombo, F., Belletti, S., Fiorentini, C., Autonomic nervous system and paroxysmal atrial fibrillation: a study based on the analysis of RR interval changes before, during and after paroxysmal atrial fibrillation. Eur Heart J 25 (2004), 1242–1248.
Shin, D.G., Yoo, C.S., Yi, S.H., et al. Prediction of paroxysmal atrial fibrillation using nonlinear analysis of the R-R interval dynamics before the spontaneous onset of atrial fibrillation. Circ J 70 (2006), 94–99.
Vikman, S., Makikallio, T.H., Yli-Mayry, S., et al. Altered complexity and correlation properties of R-R interval dynamics before the spontaneous onset of paroxysmal atrial fibrillation. Circulation 100 (1999), 2079–2084.
Hnatkova, K., Waktare, J.E., Murgatroyd, F.D., et al. Analysis of the cardiac rhythm preceding episodes of paroxysmal atrial fibrillation. Am Heart J 135 (1998), 1010–1019.
Goldberger, A.L., Amaral, L.A., Glass, L., et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101 (2000), E215–E220.
Thong, T., McNames, J., Aboy, M., Goldstein, B., Prediction of paroxysmal atrial fibrillation by analysis of atrial premature complexes. IEEE Trans Biomed Eng 51 (2004), 561–569.
Zong, W., Mukkamala, R., Mark, R.G., A methodology for predicting paroxysmal atrial fibrillation based on ECG arrhythmia feature analysis. Comput Cardiol 28 (2001), 125–128.
Boon, K.H., Khalil-Hani, M., Malarvili, M.B., Paroxysmal atrial fibrillation prediction based on HRV analysis and non-dominated sorting genetic algorithm III. Comput Methods Programs Biomed 153 (2018), 171–184.
Ebrahimzadeh, E., Kalantari, M., Joulani, M., Shahraki, R.S., Fayaz, F., Ahmadi, F., Prediction of paroxysmal Atrial Fibrillation: A machine learning based approach using combined feature vector and mixture of expert classification on HRV signal. Comput Methods Programs Biomed 165 (2018), 53–67.
Mohebbi, M., Ghassemian, H., Prediction of paroxysmal atrial fibrillation based on non-linear analysis and spectrum and bispectrum features of the heart rate variability signal. Comput Methods Programs Biomed 105 (2012), 40–49.
Narin, A., Isler, Y., Ozer, M., Perc, M., Early prediction of paroxysmal atrial fibrillation based on short-term heart rate variability. Phys A Stat Mech Its Appl 509 (2018), 56–65.
Healey, J.S., Amit, G., Field, T.S., Atrial fibrillation and stroke: how much atrial fibrillation is enough to cause a stroke?. Curr Opin Neurol 33 (2020), 17–23.
Boriani, G., Vitolo, M., Imberti, J.F., Potpara, T.S., Lip, G.Y.H., What do we do about atrial high rate episodes?. Eur Heart J Suppl 22:Suppl O (2020), O42–O52.
Padeletti, L., Purerfellner, H., Mont, L., et al. New-generation atrial antitachycardia pacing (Reactive ATP) is associated with reduced risk of persistent or permanent atrial fibrillation in patients with bradycardia: Results from the MINERVA randomized multicenter international trial. Heart Rhythm 12 (2015), 1717–1725.