Summary
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.
Keywords
Atrial fibrillation - ischemic stroke - heart rate variability - nonlinear multiscale
analysis - scattering transform - wavelet transform
