Scattering Transform of Heart Rate Variability for the Prediction of Ischemic Stroke in Patients with Atrial FibrillationThis work was supported by CNRS grant PICS 7260.
20 July 2017
accepted: 20 December 2017
02 May 2018 (online)
Background: Atrial fibrillation (AF) is an identified risk factor for ischemic strokes (IS). AF causes a loss in atrial contractile function that favors the formation of thrombi, and thus increases the risk of stroke. Also, AF produces highly irregular and complex temporal dynamics in ventricular response RR intervals. Thus, it is hypothesized that the analysis of RR dynamics could provide predictors for IS. However, these complex and nonlinear dynamics call for the use of advanced multiscale nonlinear signal processing tools.
Objectives: The global aim is to investigate the performance of a recently-proposed multiscale and nonlinear signal processing tool, the scattering transform, in predicting IS for patients suffering from AF.
Methods: The heart rate of a cohort of 173 patients from Fujita Health University Hospital in Japan was analyzed with the scattering transform. First, p-values of Wilcoxon rank sum tests were used to identify scattering coefficients achieving significant (univariate) discrimination between patients with and without IS. Second, a multivariate procedure for feature selection and classification, the Sparse Support Vector Machine (S-SVM), was applied to predict IS.
Results: Groups of scattering coefficients, located at several time-scales, were identified as significantly higher (p-value < 0.05) in patients who developed IS than in those who did not. Though the overall predictive power of these indices remained moderate (around 60 %), it was found to be much higher when analysis was restricted to patients not taking antithrombotic treatment (around 80 %). Further, S-SVM showed that multivariate classification improves IS prediction, and also indicated that coefficients involved in classification differ for patients with and without antithrombotic treatment.
Conclusions: Scattering coefficients were found to play a significant role in predicting IS, notably for patients not receiving antithrombotic treatment. S-SVM improves IS detection performance and also provides insight on which features are important. Notably, it shows that AF patients not taking antithrombotic treatment are characterized by a slow modulation of RR dynamics in the ULF range and a faster modulation in the HF range. These modulations are significantly decreased in patients with IS, and hence have a good discriminant ability.
- 1 Kirchhof P, Benussi S, Kotecha D, Ahlsson A, Atar D, Casadei B. Guidelines for the management of atrial fibrillation developed in collaboration with eacts. Eur Heart J 2016; 273 (37) 2893-2962.
- 2 Hayano J, Yamasaki F, Sakata S, Okada A, Mukai S, Fujinami T. Spectral characteristics of ventricular response to atrial fibrillation. Am J Physiol Heart Circ Physiol 1997; 273 (06) H2811-H2816.
- 3 Kirsh JA, Sahakian AV, Baerman JM, Swiryn S. Ventricular response to atrial fibrillation: Role of atrioventricular conduction pathways. J Am Coll Cardiol 1988; 12 (05) 1265-1272.
- 4 Fuster V, Rydén LE, Cannom DS, Crijns HJ, Curtis AB, Ellenbogen KA. et al. ACC/AHA/ESC 2006 Guidelines for the Management of Patients With Atrial Fibrillation. Circulation 2006; 114 (07) e257-e354.
- 5 Lip GY, Frison L, Halperin JL, Lane DA. Identifying Patients at High Risk for Stroke Despite Anticoagulation. Stroke 2010; 41 (12) 2731-2738.
- 6 Yamada A, Hayano J, Sakata S, Okada A, Mukai S, Ohte N. et al. Reduced ventricular response irregularity is associated with increased mortality in patients with chronic atrial fibrillation. Circulation 2000; 102 (03) 300-306.
- 7 Watanabe E, Kiyono K, Hayano J, Yamamoto Y, Inamasu J, Yamamoto M. et al. Multiscale Entropy of the Heart Rate Variability for the Prediction of an Ischemic Stroke in Patients with Permanent Atrial Fibrillation. PLOS ONE 2015; 10 (09) e0137144.
- 8 Mallat S. Group Invariant Scattering. Comm Pure Appl Math 2012; 65 (10) 1331-1398.
- 9 Andén J, Mallat S. Deep Scattering Spectrum. IEEE Trans Sig Proc 2014; 62 (16) 4114-4128.
- 10 Chudáček V, Andén J, Mallat S, Abry P, Doret M. Scattering Transform for Intrapartum Fetal Heart Rate Variability Fractal Analysis: A Case-Control Study. IEEE Trans Biomed Eng 2014; 61 (04) 1100-1108.
- 11 Bruna J, Mallat S, Bacry E, Muzy J. Intermittent process analysis with scattering moments. Ann Statist 2015; 43 (01) 323-351.
- 12 Blondel M, Seki K, Uehara K. Block coordinate descent algorithm for large-scale sparse multiclass classification. J Mach Learn 2013; 93 (01) 31-52.
- 13 Spilka J, Frecon J, Leonarduzzi R, Pustelnik N, Abry P, Doret M. Sparse support vector machine for intrapartum fetal heart rate classification. IEEE Journal of Biomedical and Health Informatics 2017; 21: 664-671.