Functional brain-heart interplay extends to the multifractal domain.

The study of functional brain-heart interplay has provided meaningful insights in cardiology and neuroscience. Regarding biosignal processing, this interplay involves predominantly neural and heartbeat linear dynamics expressed via time and frequency domain-related features. However, the dynamics of central and autonomous nervous systems show nonlinear and multifractal behaviours, and the extent to which this behaviour influences brain-heart interactions is currently unknown. Here, we report a novel signal processing framework aimed at quantifying nonlinear functional brain-heart interplay in the non-Gaussian and multifractal domains that combines electroencephalography (EEG) and heart rate variability series. This framework relies on a maximal information coefficient analysis between nonlinear multiscale features derived from EEG spectra and from an inhomogeneous point-process model for heartbeat dynamics. Experimental results were gathered from 24 healthy volunteers during a resting state and a cold pressor test, revealing that synchronous changes between brain and heartbeat multifractal spectra occur at higher EEG frequency bands and through nonlinear/complex cardiovascular control. We conclude that significant bodily, sympathovagal changes such as those elicited by cold-pressure stimuli affect the functional brain-heart interplay beyond second-order statistics, thus extending it to multifractal dynamics. These results provide a platform to define novel nervous-system-targeted biomarkers. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.

Neural Integration of Stimulus History Underlies Prediction for Naturalistically Evolving Sequences.

Forming valid predictions about the environment is crucial to survival. However, whether humans are able to form valid predictions about natural stimuli based on their temporal statistical regularities remains unknown. Here, we presented subjects with tone sequences with pitch fluctuations that, over time, capture long-range temporal dependence structures prevalent in natural stimuli. We found that subjects were able to exploit such naturalistic statistical regularities to make valid predictions about upcoming items in a sequence. Magnetoencephalography (MEG) recordings revealed that slow, arrhythmic cortical dynamics tracked the evolving pitch sequence over time such that neural activity at a given moment was influenced by the pitch of up to seven previous tones. Importantly, such history integration contained in neural activity predicted the expected pitch of the upcoming tone, providing a concrete computational mechanism for prediction. These results establish humans' ability to make valid predictions based on temporal regularities inherent in naturalistic stimuli and further reveal the neural mechanisms underlying such predictive computation.SIGNIFICANCE STATEMENT A fundamental question in neuroscience is how the brain predicts upcoming events in the environment. To date, this question has primarily been addressed in experiments using relatively simple stimulus sequences. Here, we studied predictive processing in the human brain using auditory tone sequences that exhibit temporal statistical regularities similar to those found in natural stimuli. We observed that humans are able to form valid predictions based on such complex temporal statistical regularities. We further show that neural response to a given tone in the sequence reflects integration over the preceding tone sequence and that this history dependence forms the foundation for prediction. These findings deepen our understanding of how humans form predictions in an ecologically valid environment.

Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK).

The second Signal Processing and Monitoring in Labor workshop gathered researchers who utilize promising new research strategies and initiatives to tackle the challenges of intrapartum fetal monitoring. The workshop included a series of lectures and discussions focusing on: new algorithms and techniques for cardiotocogoraphy (CTG) and electrocardiogram acquisition and analyses; the results of a CTG evaluation challenge comparing state-of-the-art computerized methods and visual interpretation for the detection of arterial cord pH <7.05 at birth; the lack of consensus about the role of intrapartum acidemia in the etiology of fetal brain injury; the differences between methods for CTG analysis "mimicking" expert clinicians and those derived from "data-driven" analyses; a critical review of the results from two randomized controlled trials testing the former in clinical practice; and relevant insights from modern physiology-based studies. We concluded that the automated algorithms performed comparably to each other and to clinical assessment of the CTG. However, the sensitivity and specificity urgently need to be improved (both computerized and visual assessment). Data-driven CTG evaluation requires further work with large multicenter datasets based on well-defined labor outcomes. And before first tests in the clinic, there are important lessons to be learnt from clinical trials that tested automated algorithms mimicking expert CTG interpretation. In addition, transabdominal fetal electrocardiogram monitoring provides reliable CTG traces and variability estimates; and fetal electrocardiogram waveform analysis is subject to promising new research. There is a clear need for close collaboration between computing and clinical experts. We believe that progress will be possible with multidisciplinary collaborative research.

Interplay between functional connectivity and scale-free dynamics in intrinsic fMRI networks.

Studies employing functional connectivity-type analyses have established that spontaneous fluctuations in functional magnetic resonance imaging (fMRI) signals are organized within large-scale brain networks. Meanwhile, fMRI signals have been shown to exhibit 1/f-type power spectra - a hallmark of scale-free dynamics. We studied the interplay between functional connectivity and scale-free dynamics in fMRI signals, utilizing the fractal connectivity framework - a multivariate extension of the univariate fractional Gaussian noise model, which relies on a wavelet formulation for robust parameter estimation. We applied this framework to fMRI data acquired from healthy young adults at rest and while performing a visual detection task. First, we found that scale-invariance existed beyond univariate dynamics, being present also in bivariate cross-temporal dynamics. Second, we observed that frequencies within the scale-free range do not contribute evenly to inter-regional connectivity, with a systematically stronger contribution of the lowest frequencies, both at rest and during task. Third, in addition to a decrease of the Hurst exponent and inter-regional correlations, task performance modified cross-temporal dynamics, inducing a larger contribution of the highest frequencies within the scale-free range to global correlation. Lastly, we found that across individuals, a weaker task modulation of the frequency contribution to inter-regional connectivity was associated with better task performance manifesting as shorter and less variable reaction times. These findings bring together two related fields that have hitherto been studied separately - resting-state networks and scale-free dynamics, and show that scale-free dynamics of human brain activity manifest in cross-regional interactions as well.

Drowsiness detection from polysomnographic data using multivariate selfsimilarity and eigen-wavelet analysis.

Because drowsiness is a major cause in vehicle accidents, its automated detection is critical. Scale-free temporal dynamics is known to be typical of physiological and body rhythms. The present work quantifies the benefits of applying a recent and original multivariate selfsimilarity analysis to several modalities of polysomnographic measurements (heart rate, blood pressure, electroencephalogram and respiration), from the MIT-BIH Polysomnographic Database, to better classify drowsiness-related sleep stages. Clinical relevance- This study shows that probing jointly temporal dynamics amongst polysomnographic measurements, with a proposed original multivariate multiscale approach, yields a gain of above 5% in the Area-under-Curve quanti-fying drowsiness-related sleep stage classification performance compared to univariate analysis.

Temporal evolution of the Covid19 pandemic reproduction number: Estimations from proximal optimization to Monte Carlo sampling.

Monitoring the evolution of the Covid19 pandemic constitutes a critical step in sanitary policy design. Yet, the assessment of the pandemic intensity within the pandemic period remains a challenging task because of the limited quality of data made available by public health authorities (missing data, outliers and pseudoseasonalities, notably), that calls for cumbersome and ad-hoc preprocessing (denoising) prior to estimation. Recently, the estimation of the reproduction number, a measure of the pandemic intensity, was formulated as an inverse problem, combining data-model fidelity and space-time regularity constraints, solved by nonsmooth convex proximal minimizations. Though promising, that formulation lacks robustness against the limited quality of the Covid19 data and confidence assessment. The present work aims to address both limitations: First, it discusses solutions to produce a robust assessment of the pandemic intensity by accounting for the low quality of the data directly within the inverse problem formulation. Second, exploiting a Bayesian interpretation of the inverse problem formulation, it devises a Monte Carlo sampling strategy, tailored to a nonsmooth log-concave a posteriori distribution, to produce relevant credibility interval-based estimates for the Covid19 reproduction number. Clinical relevance Applied to daily counts of new infections made publicly available by the Health Authorities for around 200 countries, the proposed procedures permit robust assessments of the time evolution of the Covid19 pandemic intensity, updated automatically and on a daily basis.

Application of cardiac autonomous indices in the study of neurogenic erectile dysfunction.

A research area of increasing interest consists of studying the benefits of using spectral analysis to screen neurogenic erectile dysfunctions. Our hypothesis is that spectral analysis consists of a non-invasive and simple procedure to investigate such patients. Subjects were allocated into two groups: control, no erectile dysfunction (n = 17), and patients with erectile dysfunction (n = 15). RR intervals (RRI), systolic blood pressure, diastolic blood pressure and baroreflex sensitivity recordings were performed continuously in the supine position, followed by the seated position, and finally standing position. In the supine position, the control group had a higher RRI and a lower diastolic blood pressure. For frequency domain analyses of RRI in the supine position, the erectile dysfunction group had a higher normalized low-frequency (LF) index and low-frequency/high-frequency (LF/HF) ratio while showing a lower normalized HF index. In the seated position, the erectile dysfunction group presented a higher LF/HF ratio. Regarding systolic blood pressure, the erectile dysfunction group showed lower normalized LF and higher normalized HF indices only in the supine position. The α index in HF was lower in the erectile dysfunction group in the three positions. Spectral analyses of cardiac sympathovagal drive constitute a fruitful non-invasive approach to evaluate alterations in cardiovascular autonomic modulation in neurogenic erectile dysfunction patients.

Multifractal analysis of fetal heart rate variability in fetuses with and without severe acidosis during labor.

We performed multifractal analysis of fetal heart rate (FHR) variability in fetuses with and without acidosis during labor. Multifractal analysis was performed on fetal electrocardiograms in 10-minute sliding windows within the last 2 hours before delivery in 45 term fetuses divided in three groups according to umbilical arterial pH and FHR pattern: group A had pH ≥7.30 and normal FHR, group B had pH ≥7.30 and intermediate or abnormal FHR, and group C had acidosis (pH ≤7.05) and intermediate or abnormal FHR. Six multifractal parameters were compared using Wilcoxon rank sum test. Multifractal parameters were significantly different between the three groups in the last 10 minutes before delivery (P <0.05). Two parameters (H(min), zeta(2)) exhibited a significant difference 70 minutes before delivery, and one parameter (C(2)) was different 10 minutes before birth (P <0.05). Multifractal parameters were significantly different in acidotic and nonacidotic fetuses, independently from FHR pattern.

Distance to Healthy Metabolic and Cardiovascular Dynamics From Fetal Heart Rate Scale-Dependent Features in Pregnant Sheep Model of Human Labor Predicts the Evolution of Acidemia and Cardiovascular Decompensation.

The overarching goal of the present work is to contribute to the understanding of the relations between fetal heart rate (FHR) temporal dynamics and the well-being of the fetus, notably in terms of predicting the evolution of lactate, pH and cardiovascular decompensation (CVD). It makes uses of an established animal model of human labor, where 14 near-term ovine fetuses subjected to umbilical cord occlusions (UCO) were instrumented to permit regular intermittent measurements of metabolites lactate and base excess, pH, and continuous recording of electrocardiogram (ECG) and systemic arterial blood pressure (to identify CVD) during UCO. ECG-derived FHR was digitized at the sampling rate of 1,000 Hz and resampled to 4 Hz, as used in clinical routine. We focused on four FHR variability features which are tunable to temporal scales of FHR dynamics, robustly computable from FHR sampled at 4 Hz and within short-time sliding windows, hence permitting a time-dependent, or local, analysis of FHR which helps dealing with signal noise. Results show the sensitivity of the proposed features for early detection of CVD, correlation to metabolites and pH, useful for early acidosis detection and the importance of coarse time scales (2.5-8 s) which are not disturbed by the low FHR sampling rate. Further, we introduce the performance of an individualized self-referencing metric of the distance to healthy state, based on a combination of the four features. We demonstrate that this novel metric, applied to clinically available FHR temporal dynamics alone, accurately predicts the time occurrence of CVD which heralds a clinically significant degradation of the fetal health reserve to tolerate the trial of labor.

Neural integration underlying naturalistic prediction flexibly adapts to varying sensory input rate.

Prediction of future sensory input based on past sensory information is essential for organisms to effectively adapt their behavior in dynamic environments. Humans successfully predict future stimuli in various natural settings. Yet, it remains elusive how the brain achieves effective prediction despite enormous variations in sensory input rate, which directly affect how fast sensory information can accumulate. We presented participants with acoustic sequences capturing temporal statistical regularities prevalent in nature and investigated neural mechanisms underlying predictive computation using MEG. By parametrically manipulating sequence presentation speed, we tested two hypotheses: neural prediction relies on integrating past sensory information over fixed time periods or fixed amounts of information. We demonstrate that across halved and doubled presentation speeds, predictive information in neural activity stems from integration over fixed amounts of information. Our findings reveal the neural mechanisms enabling humans to robustly predict dynamic stimuli in natural environments despite large sensory input rate variations.

Quantifying Functional Links between Brain and Heartbeat Dynamics in the Multifractal Domain: a Preliminary Analysis.

Quantification of brain-heart interplay (BHI) has mainly been performed in the time and frequency domains. However, such functional interactions are likely to involve nonlinear dynamics associated with the two systems. To this extent, in this preliminary study we investigate the functional coupling between multifractal properties of Electroencephalography (EEG) and Heart Rate Variability (HRV) series using a channel- and time scale-wise maximal information coefficient analysis. Experimental results were gathered from 24 healthy volunteers undergoing a resting state and a cold-pressure test, and suggest that significant changes between the two experimental conditions might be associated with nonlinear quantifiers of the multifractal spectrum. Particularly, major brain-heart functional coupling was associated with the secondorder cumulant of the multifractal spectrum. We conclude that a functional nonlinear relationship between brain- and heartbeat-related multifractal sprectra exist, with higher values associated with the resting state.

Revisiting Functional Connectivity for Infraslow Scale-Free Brain Dynamics Using Complex Wavelets.

The analysis of human brain functional networks is achieved by computing functional connectivity indices reflecting phase coupling and interactions between remote brain regions. In magneto- and electroencephalography, the most frequently used functional connectivity indices are constructed based on Fourier-based cross-spectral estimation applied to specific fast and band-limited oscillatory regimes. Recently, infraslow arrhythmic fluctuations (below the 1 Hz) were recognized as playing a leading role in spontaneous brain activity. The present work aims to propose to assess functional connectivity from fractal dynamics, thus extending the assessment of functional connectivity to the infraslow arrhythmic or scale-free temporal dynamics of M/EEG-quantified brain activity. Instead of being based on Fourier analysis, new Imaginary Coherence and weighted Phase Lag indices are constructed from complex-wavelet representations. Their performances are first assessed on synthetic data by means of Monte-Carlo simulations, and they are then compared favorably against the classical Fourier-based indices. These new assessments of functional connectivity indices are also applied to MEG data collected on 36 individuals both at rest and during the learning of a visual motion discrimination task. They demonstrate a higher statistical sensitivity, compared to their Fourier counterparts, in capturing significant and relevant functional interactions in the infraslow regime and modulations from rest to task. Notably, the consistent overall increase in functional connectivity assessed from fractal dynamics from rest to task correlated with a change in temporal dynamics as well as with improved performance in task completion, which suggests that the complex-wavelet weighted Phase Lag index is the sole index is able to capture brain plasticity in the infraslow scale-free regime.

Who benefits most from Lyon's bike sharing system?

Bike sharing systems (BSS) have been growing fast all over the world, along with the number of articles analyzing such systems. However the lack of databases at the individual level and covering several years has limited the analysis of BSS users' behavior in the long term. This article gives a first detailed description of the temporal evolution of individual customers. Using a 5-year dataset covering 120,827 distinct year-long subscribers, we show the heterogeneous individual trajectories masked by the overall system stability. Users follow two main trajectories: about half remain in the system for at most one year, showing a low median activity (47 trips); the remaining half corresponds to more active users (median activity of 91 trips in their first year) that remain continuously active for several years (mean time = 2.9 years). We show that users from urban cores, middle-aged and male are over represented among these long-term users, which profit most from the BSS. This provides further support for the view that BSS mostly benefit the already privileged.

Spatial and temporal regularization to estimate COVID-19 reproduction number R(t): Promoting piecewise smoothness via convex optimization.

Among the different indicators that quantify the spread of an epidemic such as the on-going COVID-19, stands first the reproduction number which measures how many people can be contaminated by an infected person. In order to permit the monitoring of the evolution of this number, a new estimation procedure is proposed here, assuming a well-accepted model for current incidence data, based on past observations. The novelty of the proposed approach is twofold: 1) the estimation of the reproduction number is achieved by convex optimization within a proximal-based inverse problem formulation, with constraints aimed at promoting piecewise smoothness; 2) the approach is developed in a multivariate setting, allowing for the simultaneous handling of multiple time series attached to different geographical regions, together with a spatial (graph-based) regularization of their evolutions in time. The effectiveness of the approach is first supported by simulations, and two main applications to real COVID-19 data are then discussed. The first one refers to the comparative evolution of the reproduction number for a number of countries, while the second one focuses on French departments and their joint analysis, leading to dynamic maps revealing the temporal co-evolution of their reproduction numbers.

Multifractal formalisms for multivariate analysis.

Multifractal analysis, that quantifies the fluctuations of regularities in time series or textures, has become a standard signal/image processing tool. It has been successfully used in a large variety of applicative contexts. Yet, successes are confined to the analysis of one signal or image at a time (univariate analysis). This is because multivariate (or joint) multifractal analysis remains so far rarely used in practice and has barely been studied theoretically. In view of the myriad of modern real-world applications that rely on the joint (multivariate) analysis of collections of signals or images, univariate analysis constitutes a major limitation. The goal of the present work is to theoretically ground multivariate multifractal analysis by studying the properties and limitations of the most natural extension of the univariate formalism to a multivariate formulation. It is notably shown that while performing well for a class of model processes, this natural extension is not valid in general. Based on the theoretical study of the mechanisms leading to failure, we propose alternative formulations and examine their mathematical properties.

Wavelet p-Leader Non Gaussian Multiscale Expansions for Heart Rate Variability Analysis in Congestive Heart Failure Patients.

OBJECTIVE: Numerous indices were devised for the statistical characterization of temporal dynamics of heart rate variability (HRV) with the aim to discriminate between healthy subjects and nonhealthy patients. Elaborating on the concepts of (multi)fractal and nonlinear analyses, the present contribution defines and studies formally novel non Gaussian multiscale representations. METHODS: A methodological framework for non Gaussian multiscale representations constructed on wavelet p-leaders is developed, relying a priori neither on exact scale-free dynamics nor on predefined forms of departure from Gaussianity. Its versatility in quantifying the strength and nature of departure from Gaussian is analyzed theoretically and numerically. The ability of the representations to discriminate between healthy subjects and congestive heart failure (CHF) patients, and between survivors and nonsurvivor CHF patients, is assessed on a large cohort of 198 subjects. RESULTS: The analysis leads to conclude that i) scale-free and multifractal dynamics are observed, both for healthy subjects and CHF patients, for time scales shorter than [Formula: see text]; ii) a circadian evolution of multifractal and non Gaussian properties of HRV is evidenced for healthy subjects, but not for CHF patients; iii) non Gaussian multiscale indices possess high discriminative abilities between survivor and nonsurvivor CHF patients, at specific time scales ([Formula: see text] and [Formula: see text]). CONCLUSIONS: The non Gaussian multiscale representations provide evidence for the existence of short-term cascade-type multifractal mechanisms underlying HRV for both healthy and CHF subjects. A circadian evolution of this mechanism is only evidenced for the healthy group, suggesting an alteration of the sympathetic-parasympathetic balance for CHF patients. SIGNIFICANCE: Results obtained for a large cohort of subjects suggest that the novel non Gaussian indices might robustly quantify crucial information for clinical risk stratification in CHF patients.

Nonlinear denoising for characterization of solid friction under low confinement pressure.

The present work investigates paper-paper friction dynamics by pulling a slider over a substrate. It focuses on the transition between stick-slip and inertial regimes. Although the device is classical, probing solid friction with the fewest contact damage requires that the applied load should be small. This induces noise, mostly impulsive in nature, on the recorded slider motion and force signals. To address the challenging issue of describing the physics of such systems, we promote here the use of nonlinear filtering techniques relying on recent nonsmooth optimization schemes. In contrast to linear filtering, nonlinear filtering captures the slider velocity asymmetry and, thus, the creep motion before sliding. Precise estimates of the stick and slip phase durations can thus be obtained. The transition between the stick-slip and inertial regimes is continuous. Here we propose a criterion based on the probability of the system to be in the stick-slip regime to quantify this transition. A phase diagram is obtained that characterizes the dynamics of this frictional system under low confinement pressure.

Self-similarity and multifractality in human brain activity: A wavelet-based analysis of scale-free brain dynamics.

BACKGROUND: The temporal structure of macroscopic brain activity displays both oscillatory and scale-free dynamics. While the functional relevance of neural oscillations has been largely investigated, both the nature and the role of scale-free dynamics in brain processing have been disputed. NEW METHOD: Here, we offer a novel method to rigorously enrich the characterization of scale-free brain activity using a robust wavelet-based assessment of self-similarity and multifractality. For this, we analyzed human brain activity recorded with magnetoencephalography (MEG) while participants were at rest or performing a visual motion discrimination task. RESULTS: First, we report consistent infraslow (from 0.1 to 1.5 Hz) scale-free dynamics (i.e., self-similarity and multifractality) in resting-state and task data. Second, we observed a fronto-occipital gradient of self-similarity reminiscent of the known hierarchy of temporal scales from sensory to higher-order cortices; the anatomical gradient was more pronounced in task than in rest. Third, we observed a significant increase of multifractality during task as compared to rest. Additionally, the decrease in self-similarity and the increase in multifractality from rest to task were negatively correlated in regions involved in the task, suggesting a shift from structured global temporal dynamics in resting-state to locally bursty and non Gaussian scale-free structures during task. COMPARISON WITH EXISTING METHOD(S): We showed that the wavelet leader based multifractal approach extends power spectrum estimation methods in the way of characterizing finely scale-free brain dynamics. CONCLUSIONS: Altogether, our approach provides novel fine-grained characterizations of scale-free dynamics in human brain activity.

Mortality Prediction in Severe Congestive Heart Failure Patients with Multifractal Point-Process Modeling of Heartbeat Dynamics.

Multifractal analysis of human heartbeat dynamics has been demonstrated to provide promising markers of Congestive Heart Failure (CHF). Yet, it crucially builds on the interpolation of RR intervals series, which has been generically performed with limited links to CHF pathophysiology. We devise a novel methodology estimating multifractal autonomic dynamics from heartbeat-derived series defined in the continuous time. We hypothesize that markers estimated from our novel framework are also effective for mortality prediction in severe CHF. We merge multifractal analysis within a methodological framework based on inhomogeneous point process models of heartbeat dynamics. Specifically, wavelet coefficients and wavelet leaders are computed over measures extracted from instantaneous statistics of probability density functions characterizing and predicting the time until the next heartbeat event occurs. The proposed approach is tested on data from 94 CHF patients, aiming at predicting survivor and non-survivor individuals as determined after a 4 years follow up. Instantaneous markers of vagal and sympatho-vagal dynamics display power-law scaling for a large range of scales, from s to s. Using standard SVM algorithms, the proposed inhomogeneous point-process representation based multifractal analysis achieved the best CHF mortality prediction accuracy of 79.11 % (sensitivity 90.48%, specificity 67.74%). Our results suggest that heartbeat scaling and multifractal properties in CHF patients are not generated at the sinus-node level, but rather by the intrinsic action of vagal short-term control and of sympatho-vagal fluctuations associated with circadian cardiovascular control, especially within the VLF band. These markers might provide critical information in devising a clinical tool for individualized prediction of survivor and non-survivor CHF patients.

Scattering Transform of Heart Rate Variability for the Prediction of Ischemic Stroke in Patients with Atrial Fibrillation.

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.