Group SCI STI JMV

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Novel spatiotemporal processing tools for body-surface potential map signals for the prediction of catheter ablation outcome in persistent atrial fibrillation

Adrian Luca, Etienne Pruvot, Jean-Marc Vesin

Background: Signal processing tools are required to efficiently analyze data collected in body-surface-potential map (BSPM) recordings. A limited number of such tools exist for studying persistent atrial fibrillation (persAF). We propose two novel, spatiotemporal indices for processing BSPM data and test their clinical applicability through a comparison with the recently proposed non-dipolar component index (NDI) for prediction of single-procedure catheter ablation (CA) success rate in persAF patients. Methods: BSPM recordings were obtained with a 252-lead vest in 13 persAF patients (8 men, 63 +/- 8 years, 11 +/- 13 months sustained AF duration) before undergoing CA. Each recording was divided into seven 1-min segments of high signal quality. Spatiotemporal ventricular activity (VA) cancellation was applied to each segment to isolate atrial activity (AA). The two novel indices, called error-ratio, normalized root-mean-square error (ERNRMSE) and error-ratio, mean-absolute error (ERABSE), were calculated. These indices quantify the capacity of a subset of BSPM vest electrodes to accurately represent the AA, and AA dominant frequency (DF), respectively, on all BSPM electrodes over time, compared to the optimal principal component analysis (PCA) representation. The NDI, quantifying the fraction of energy retained after removal of the three largest PCs, was also calculated. The two novel indices and the NDI were statistically compared between patient groups based on single-procedure clinical CA outcome. Finally, their predictive power for univariate CA outcome classification was assessed using receiver operating characteristic (ROC) analysis with cross-validation for a logistic regression classifier. Results: Patient clinical outcomes were recorded 6 months following procedures, and those who had an arrhythmia recurrence at least 2 months post-CA were defined as having a negative outcome. Clinical outcome information was available for 11 patients, 6 with arrhythmia recurrence. Therefore, a total of 77 1-min AA-BSPM segments were available for analysis. Significant differences were found in the values of the novel indices and NDI between patients with arrhythmia recurrence post-ablation and those without. ROC analysis showed the best CA outcome predictive performance for ERNRMSE (AUC = 0.77 +/- 0.08, sensitivity = 76.2%, specificity = 84.8%). Conclusion: Significant association was found between the novel indices and CA success or failure. The novel index ERNRMSE additionally shows good predictive power for single-procedure CA outcome.

FRONTIERS MEDIA SA2022

Correlation between conduction velocity and frequency analysis in patients with atrial fibrillation using high-density charge mapping

Lam Dang, Jean-Marc Vesin

Spectral analysis of atrial signals has been used to identify regions of interest in atrial fibrillation (AF). However, the relationship to the atrial substrate is unclear. In this study, we compare regions with dominant frequency (DF), simultaneously determined in the left atrium (LA) by a novel noncontact mapping system using unipolar charge density signals, to the zones of slow conduction (SZ) during AF. In 19 AF patients the conduction during AF was assessed by a validated algorithm and SZ compared to the DF and the DF ratio between the DF peak and the area under the total spectrum (DFR). The results were compared in five different regions of the LA. The reproducibility of SZ location at different time measurements was higher than for DF or DFR. The SZs are mainly confined at the anterior and posterior wall of the LA. There was no statistically significant correlation between SZ and DF or DFR across the atrium.

SPRINGER HEIDELBERG2022

Evaluation and optimization of novel extraction algorithms for the automatic detection of atrial activations recorded within the pulmonary veins during atrial fibrillation

Adrian Luca, Yann Prudat, Etienne Pruvot, Jean-Marc Vesin, Sasan Yazdani

Background and objective The automated detection of atrial activations (AAs) recorded from intracardiac electrograms (IEGMs) during atrial fibrillation (AF) is challenging considering their various amplitudes, morphologies and cycle length. Activation time estimation is further complicated by the constant changes in the IEGM active zones in complex and/or fractionated signals. We propose a new method which provides reliable automatic extraction of intracardiac AAs recorded within the pulmonary veins during AF and an accurate estimation of their local activation times. Methods First, two recently developed algorithms were evaluated and optimized on 118 recordings of pulmonary vein IEGM taken from 35 patients undergoing ablation of persistent AF. The adaptive mathematical morphology algorithm (AMM) uses an adaptive structuring element to extract AAs based on their morphological features. The relative-energy algorithm (Rel-En) uses short- and long-term energies to enhance and detect the AAs in the IEGM signals. Second, following the AA extraction, the signal amplitude was weighted using statistics of the AA sequences in order to reduce over- and undersensing of the algorithms. The detection capacity of our algorithms was compared with manually annotated activations and with two previously developed algorithms based on the Teager-Kaiser energy operator and the AF cycle length iteration, respectively. Finally, a method based on the barycenter was developed to reduce artificial variations in the activation annotations of complex IEGM signals. Results The best detection was achieved using Rel-En, yielding a false negative rate of 0.76% and a false positive rate of only 0.12% (total error rate 0.88%) against expert annotation. The post-processing further reduced the total error rate of the Rel-En algorithm by 70% (yielding to a final total error rate of 0.28%). Conclusion The proposed method shows reliable detection and robust temporal annotation of AAs recorded within pulmonary veins in AF. The method has low computational cost and high robustness for automatic detection of AAs, which makes it a suitable approach for online use in a procedural context.

BMC2022

RMSSD Is More Sensitive to Artifacts Than Frequency-Domain Parameters: Implication in Athletes? Monitoring

Grégoire Millet, Jean-Marc Vesin, Sasan Yazdani

Easy-to-use and accurate heart rate variability (HRV) assessments are essential in athletes??? follow-up, but artifacts may lead to erroneous analysis. Artifact detection and correction are the purpose of extensive literature and implemented in dedicated analysis programs. However, the effects of number and/or magnitude of artifacts on various time-or frequency-domain parameters remain unclear. The purpose of this study was to assess the effects of artifacts on HRV parameters. Root mean square of the successive differences (RMSSD), standard deviation of the normal to normal inter beat intervals (SDNN), power in the low-(LF) and high-frequency band (HF) were computed from two 4-min RR recordings in 178 participants in both supine and standing positions, respectively. RRs were modified by (1) randomly adding or subtracting 10, 30, 50 or 100 ms to the successive RRs; (2) a single artifact was manually inserted; (3) artifacts were automatically corrected from signal naturally containing artifacts. Finally, RR recordings were analyzed before and after automatic detection-correction of artifacts. Modifying each RR by 10, 30, 50 and 100 ms randomly did not significantly change HRV parameters (range-6%, +6%, supine). In contrast, by adding a single artifact, RMSSD increased by 413% and 269%, SDNN by 54% and 47% in supine and standing positions, respectively. LF and HF changed only between-3% and +8% (supine and standing) in the artifact condition. When more than 0.9% of the signal contained artifacts, RMSSD was significantly biased, whilst when more than 1.4% of the signal contained artifacts LF and HF were significantly biased. RMSSD and SDNN were more sensitive to a single artifact than LF and HF. This indicates that, when using RMSSD only, a single artifact may induce erroneous interpretation of HRV. Therefore, we recommend using both time-and frequency-domain parameters to minimize the errors in the diagnoses of health status or fatigue in athletes.

JOURNAL SPORTS SCIENCE & MEDICINE2022

Graph signal processing tailored for subgraph focus and community structure

Miljan Petrovic

Community structure in graph-modeled data appears in a range of disciplines that comprise network science. Its importance relies on the influence it bears on other properties of graphs such as resilience, or prediction of missing connections. Nevertheless, research to date seems to overlook its effect on the properties of signals defined in the domain of graphs' vertices. Indeed, the framework of graph signal processing mainly focuses on local connectivity patterns reflected by the graph Laplacian, and the Laplacian's effect on signals as the graph equivalent of a differential operator. This dissertation investigates the aforementioned interplay between graph signals and the underlying community structure. We make an effort to answer questions like -- do signal's values align and in what way with the communities?; does localization of signal's energy favors a community as the vertex support? Answers to these questions should provide a clearer perspective on the relation between graph signals and networks at the level of subgraphs.This dissertation consists of two main parts. First, we investigate a particular approach -- based on modularity matrix -- of informing the graph Fourier transform about the communities without explicitly detecting them. Thereof, we show that the derived community-aware operators on signals, such as filtering or subsampling, provide a complementary view on the framework to the conventional one based on the Laplacian. Indeed, reduced signal variability within a community seems to be a valuable metric of important signal behavior, aside from its smoothness. Secondly, we explore the intricacies of a broader definition of a community -- a subgraph of any special interest, possibly identified through metadata on vertices instead of the underlying edge connectivity. Within this context, the goal is to understand the benefits of processing signals in a subgraph-restricted way. We design a new, more computationally stable type of Slepians -- bandlimited signals with energy concentrated on a subgraph. Consequently, we show that such bandlimited vectors can be successfully employed to identify a signal's oscillatory pattern localized in a subgraph of interest, by means of a modified filtering procedure. Findings from both lines of research confirm the need and benefits of a better understanding of the interaction between communities and graph signals.

EPFL2022

Dynamics of Intraprocedural Dominant Frequency Identifies Ablation Outcome in Persistent Atrial Fibrillation

Andréa Buttu, Adrian Luca, Alain Pithon, Etienne Pruvot, Jean-Marc Vesin

Background: The role of dominant frequency (DF) in tracking the efficiency of a stepwise catheter ablation (step-CA) in persistent atrial fibrillation (peAF) remains poorly studied. We hypothesized that the DF time-course during step-CA displays divergent patterns between patients in whom a step-CA successfully restores long-term sinus rhythm (SR) and those with recurrence. Methods: This study involved 40 consecutive patients who underwent a step-CA for peAF (sustained duration 19 +/- 11 months). Dominant frequency was computed on electrograms recorded from the right and left atrial appendages (RAA; LAA) and the coronary sinus before and during the step-CA synchronously to the 12-lead ECG. Dominant frequency was defined as the highest peak within the power spectrum. Results: Persistent atrial fibrillation was terminated by a step-CA in 28 patients [left-terminated (LT)], whereas 12 patients remaining in AF after ablation [not left-terminated (NLT)] were cardioverted. Over a mean follow-up of 34 +/- 14 months, all NLT patients had a recurrence. Among the 28 LT patients, 20 had a recurrence, while 8 remained in SR throughout follow-up. The RAA and V-1 DF had the best predictive values of the procedural failure to terminate AF (area under the curve; AUC 0.84, p < 0.05). A decision tree model including a decrease in LAA DF >= 6.61% during the first 20 min following pulmonary vein isolation (PVI) and a baseline RAA DF

2021

Effects of eight neuropsychiatric copy number variants on human brain structure

Bogdan Draganski, Kuldeep Kumar, Lester Melie Garcia, Claudia Modenato, Borja Rodriguez Herreros

Many copy number variants (CNVs) confer risk for the same range of neurodevelopmental symptoms and psychiatric conditions including autism and schizophrenia. Yet, to date neuroimaging studies have typically been carried out one mutation at a time, showing that CNVs have large effects on brain anatomy. Here, we aimed to characterize and quantify the distinct brain morphometry effects and latent dimensions across 8 neuropsychiatric CNVs. We analyzed T1-weighted MRI data from clinically and non-clinically ascertained CNV carriers (deletion/duplication) at the 1q21.1 (n = 39/28), 16p11.2 (n = 87/78), 22q11.2 (n = 75/30), and 15q11.2 (n = 72/76) loci as well as 1296 non-carriers (controls). Case-control contrasts of all examined genomic loci demonstrated effects on brain anatomy, with deletions and duplications showing mirror effects at the global and regional levels. Although CNVs mainly showed distinct brain patterns, principal component analysis (PCA) loaded subsets of CNVs on two latent brain dimensions, which explained 32 and 29% of the variance of the 8 Cohen's d maps. The cingulate gyrus, insula, supplementary motor cortex, and cerebellum were identified by PCA and multi-view pattern learning as top regions contributing to latent dimension shared across subsets of CNVs. The large proportion of distinct CNV effects on brain morphology may explain the small neuroimaging effect sizes reported in polygenic psychiatric conditions. Nevertheless, latent gene brain morphology dimensions will help subgroup the rapidly expanding landscape of neuropsychiatric variants and dissect the heterogeneity of idiopathic conditions.

SPRINGERNATURE2021

Group SCI STI JMV

Novel spatiotemporal processing tools for body-surface potential map signals for the prediction of catheter ablation outcome in persistent atrial fibrillation

Correlation between conduction velocity and frequency analysis in patients with atrial fibrillation using high-density charge mapping

Evaluation and optimization of novel extraction algorithms for the automatic detection of atrial activations recorded within the pulmonary veins during atrial fibrillation

An automatic single beat algorithm to discriminate farfield from nearfield bipolar voltage electrograms in the pulmonary veins

RMSSD Is More Sensitive to Artifacts Than Frequency-Domain Parameters: Implication in Athletes? Monitoring

Graph signal processing tailored for subgraph focus and community structure

Dynamics of Intraprocedural Dominant Frequency Identifies Ablation Outcome in Persistent Atrial Fibrillation

Effects of eight neuropsychiatric copy number variants on human brain structure

Correlation between conduction velocity and frequency analysis in patients with atrial fibrillation using high-density charge mapping

Evaluation and optimization of novel extraction algorithms for the automatic detection of atrial activations recorded within the pulmonary veins during atrial fibrillation

An automatic single beat algorithm to discriminate farfield from nearfield bipolar voltage electrograms in the pulmonary veins

RMSSD Is More Sensitive to Artifacts Than Frequency-Domain Parameters: Implication in Athletes? Monitoring

Effects of eight neuropsychiatric copy number variants on human brain structure

Novel spatiotemporal processing tools for body-surface potential map signals for the prediction of catheter ablation outcome in persistent atrial fibrillation

Graph signal processing tailored for subgraph focus and community structure

Dynamics of Intraprocedural Dominant Frequency Identifies Ablation Outcome in Persistent Atrial Fibrillation