Atrial signal extraction in atrial fibrillation ECGs exploiting spatial constraints

International audienceThe accuracy in the extraction of the atrial activity (AA) from electrocardiogram (ECG) signals recorded during atrial fibrillation (AF) episodes plays an important role in the analysis and characterization of atrial arrhhythmias. The present contribution puts forward a new method for AA signal automatic extraction based on a blind source separation (BSS) formulation that exploits spatial information about the AA during the T-Q segments. This prior knowledge is used to optimize the spectral content of the AA signal estimated by BSS on the full ECG recording. The comparative performance of the method is evaluated on real data recorded from AF sufferers. The AA extraction quality of the proposed technique is comparable to that of previous algorithms, but is achieved at a reduced cost and without manual selection of parameters

Machine learning approach and waves synchronization improvement for the localization of Atrial Flutter circuit based on the 12-leads ECG

International audienceThe localization of the Atrial flutter (AFL) is of great interest for ablation planification. Regardless the direction of rotation of the corresponding reentry loop, its left or right atrium origin needs to be known beforehand. This lo-calization is usually performed by using visual inspection of the 12-leads standard ECG that could be computerized. The aim of the study is to automatically classify the corresponding averaged F-waves by using one to five simple features. The averaged F-wave is computed by introducing a new multi-lead extension of a SVD based method for the wave resynchronization. A dataset of ECG recorded from 56 subjects and comprising 25 left AFL and 31 right AFL will train the clas-sifier. It is shown that the single lead SVD based wave synchronization is efficiently extended to 12 leads by computing the SVD of each group of waves for each lead and optimally combining the corresponding first singular values. From the subsequent averaged 12 leads F-wave, 3 groups (Gi) of features were extracted: G1-(min, max), G2-(integral of the negative, of the positive part), G3-(integral of the wave, integral of the absolute value of the wave). For each group 24 features are then computed to feed the learning algorithm. A wrapper approach using an exhaustive search for feature selection is applied to maximize the mean classification accuracy computed over one to five features for each group (Gi) applied to the 12 leads. The logistic regression (LR) model is used for the supervised classifications. The mean accuracy ranges for the three groups, without validations, are G1:[0.69-0.83], G2:[0.68-0.81], G3:[0.68-0.80] for one feature up to five. The maximum accuracy comes from G1 with five features and is equal to 93%. The corresponding selected features are [max(I), max(III), max(V3), min(aVL), min(V5)]. In order to check for the risk of model overfitting, a leave one out cross-validation (LOOCV) is performed with these five features and gives 86% for the accuracy. When using all the 24 features simultaneously, the corresponding accuracy without validation is 93% and 67% for the LOOCV

Analysis of Heart Rate Variability Using Time-Varying Filtering of Heart Transplanted Patients

International audienceIn this paper, we analyze the heart rate variability (HRV), obtained by using the time-varying integral pulse frequency modulation (TVIPFM) which is well adapted to the exercise stress testing. We consider that the mean heart period is varying function of time, during exercise. This technique allows the estimation of the autonomic nervous system modulation (ANS) from the beat occurrences. The estimated respiratory sinus arrhythmia is then filtered in the time-frequency domain around the respiration using a time-varying filter. It is proven that the Spectrogram is a convenient time-frequency representation that allows the implementation of such filter. The recorded data comes from exercise test performed by ten heart transplant patients. The magnitude of the filtered modulation of the heart rate due to respiration is compared to the date of transplantation taking into account the volume of respiration. It reveals that the normalized magnitude of the filtered variability, is significantly increased as the age of transplantation is higher with a high correlation coefficient (R=0.74, p=0.01). This correlation raised to 0.82 when considering dynamic behavior of the parameters. Applied to our dataset, standard parameter fails to exhibit such correlation

Non-Invasive Localization of Atrial Flutter Circuit using Recurrence Quantification Analysis and Machine Learning

International audienceAtrial flutter presents quasi-periodic atrial activity due to circular depolarization. Given the different structure of right and left atria, spatiotemporal variability should be different. This was analyzed using recurrence quan-tification analysis. Autocorrelation signals were estimated from the unthresholded recurrence plot, calculated with a properly processed ECG to remove variability related to external sources (noise, respiratory motion, T wave overlap). Simple features were considered from the autocorre-lation that attempts to describe the atrial activity in terms of range of recurrence and periodicity. Linear classification using support vector machines and logistic regression both allowed good classification performance (max accuracy 0.8 for both). Feature selection showed that right and left AFL have significantly different cycle lengths (right vs. left: 230.63 ms vs. 206.50 ms, p < 0.01). 1. Introduction The quasi-periodic atrial activity (AA) observed on the electrocardiogram (ECG) during atrial flutter (AFL) is caused by a rotating circular depolarization of the atrium. It has been shown that beat-to-beat variability of the flutter or F waves, quantified using vectorcardiographic parameters , allowed localization of right or left atrial circuit [1]. Different variability was observed for right and left local-ization, inducing a hypothesis of varying circuit stability. With a beat-to-beat approach, instantaneous spatiotem-poral information is not preserved, which may contain information about AA. In addition, both atria are known to be remarkably different in structure. The right atrium contains many large and well-defined cardiac fibers and is relatively thin, whereas the left atrium is thick and multi-layered [2]. It is expected that spatiotemporal variability would be different. The use of recurrence quantification analysis (RQA) has been highlighted for spatiotemporal analysis and characterization of atrial fibrillation (AF) activation propagation [3, 4]. Of particular interest, atrial fibrillation recurrence behavior was characterized, and was shown to be different for recurring and non-recurring persistent AF. In this paper, RQA is employed in order to study the spatiotemporal variability related to the circular propagation of AFL activation in a non-invasive fashion. Several features are extracted from the computed recurrence signal and serves as features for classification of circuit localiza-tion. Machine learning techniques are considered in order to obtain practical classifiers as well as to understand the reason why right and left AFL are different by employing feature selection

Quantifying the PR interval pattern during dynamic exercise and recovery.

International audienceWe present a novel analysis tool for time delay estimation in electrocardiographic signal processing. This tool enhances PR interval estimation (index of the atrioventricular conduction time) by limiting the distortion effect of the T wave overlapping the P wave at high heart rates. Our approach consists of modeling the T wave, cancelling its influence, and finally estimating the PR intervals during exercise and recovery with the proposed generalized Woody method. Different models of the T wave are presented and compared in a statistical summary that quantitatively justifies the improvements introduced by this study. Among the different models tested, we found that a piecewise linear function significantly reduces the T wave-induced bias in the estimation process. Combining this modeling with the proposed time delay estimation method leads to accurate PR interval estimation. Using this method on real ECGs recorded during exercise and its recovery, we found: 1) that the slopes of PR interval series in the early recovery phase are dependent on the subjects' training status (average of the slopes for sedentary men = 0.11 ms/s, and for athlete men = 0.28 ms/s), and 2) an hysteresis phenomenon exists in the relation PR/RR intervals when data from exercise and recovery are compared

Improving Flutter Localization Performance by Optimizing the Inverse Dower Transform

International audienceA previous study showed the possibility to localize right or left flutter circuit origin using variability contained in vectorcardiographic loop parameters. The Inverse Dower Transform, used to obtain the vectorcardiograms is based on a very simplistic torso conductor model, and hence not optimized. The present study aims to optimize the transform to maximize classifier accuracy. A parametric optimization model was proposed, as well as an optimization scheme. Model parameters were obtained by iteratively optimizing the linear SVM classifier accuracy until convergence. The goal can be shown to be multimodal and non-smooth. Therefore, a multi-instance and derivative-free method was considered. Previous dataset of 56 flutter recordings (31 right, 25 left) was used, considering only non-overlapped and respiratory motion-corrected F loops. For the SVM classifier, a 3.8% increase in accuracy was observed (max 0.95). When the logistic regression clas-sifier was used, an increase of 7.8% was observed (max 0.98). Comparison to a targeted transform previously developed showed an improvement by 17−19%. Observation of the model parameter values showed amplitude reduction applied to Lead X and rotation applied to Lead Z

Theme B: Biomedical signal and image processing

This paper presents an overview of the main actions and projects of the theme B ‘Biomedical Signal and Image Processing’ of the GdR Stic-Santé. Several scientific meetings have been organized during the 2011–2012 period. They are always devoted to advanced signal and image processing that could bring innovative solutions to relevant medical applications. The theme has strong relationships with other GdRs and also organizes meetings in close coordination with these GdRs. It also supports two working groups with well-identified research projects. Prospects includes reinforcing communication and cooperation with the other GdRs, involving labs from other countries, and attracting private companies that could also share their needs in terms of developments for which the theme participants could offer solutions. The whole motivation is to enhance the fit between academic research, needs from the medical community and impact for medical research and applications

Validation of the PR-RR Hysteresis Phenomenon

Abstract Previous studies on ECG recorded under exercise conditions on sedentary and athlete subjects lead to following results: 1) the subjects can be characterized according to their training level studying the PR slope in the early recovery phase, and 2) it exists a non-linear relationship between PR and RR intervals which exhibits a clockwise hysteresis shape when data from exercise and its recovery are compared. The main drawback of these studies is that they were performed on a small size data set. As the understanding of the PR-RR hysteresis phenomenon may lead to improvement of pacemaker&apos;s design, the aim of this study is to check the PR-RR hysteresis on a larger data set

High-density mapping of the average complex interval helps localizing atrial fibrillation drivers and predicts catheter ablation outcomes

BackgroundPersistent Atrial Fibrillation (PersAF) electrogram-based ablation is complex, and appropriate identification of atrial substrate is critical. Little is known regarding the value of the Average Complex Interval (ACI) feature for PersAF ablation.ObjectiveUsing the evolution of AF complexity by sequentially computing AF dominant frequency (DF) along the ablation procedure, we sought to evaluate the value of ACI for discriminating active drivers (AD) from bystander zones (BZ), for predicting AF termination during ablation, and for predicting AF recurrence during follow-up.MethodsWe included PersAF patients undergoing radiofrequency catheter ablation by pulmonary vein isolation and ablation of atrial substrate identified by Spatiotemporal Dispersion or Complex Fractionated Atrial Electrograms (&gt;70% of recording). Operators were blinded to ACI measurement which was sought for each documented atrial substrate area. AF DF was measured by Independent Component Analysis on 1-minute 12-lead ECGs at baseline and after ablation of each atrial zone. AD were differentiated from BZ either by a significant decrease in DF (&gt;10%), or by AF termination. Arrhythmia recurrence was monitored during follow-up.ResultsWe analyzed 159 atrial areas (129 treated by radiofrequency during AF) in 29 patients. ACI was shorter in AD than BZ (76.4 ± 13.6 vs. 86.6 ± 20.3 ms; p = 0.0055), and mean ACI of all substrate zones was shorter in patients for whom radiofrequency failed to terminate AF [71.3 (67.5–77.8) vs. 82.4 (74.4–98.5) ms; p = 0.0126]. ACI predicted AD [AUC 0.728 (0.629–0.826)]. An ACI &lt; 70 ms was specific for predicting AD (Sp 0.831, Se 0.526), whereas areas with an ACI &gt; 100 ms had almost no chances of being active in AF maintenance. AF recurrence was associated with more ACI zones with identical shortest value [3.5 (3–4) vs. 1 (0–1) zones; p = 0.021]. In multivariate analysis, ACI &lt; 70 ms predicted AD [OR = 4.02 (1.49–10.84), p = 0.006] and mean ACI &gt; 75 ms predicted AF termination [OR = 9.94 (1.14–86.7), p = 0.038].ConclusionACI helps in identifying AF drivers, and is correlated with AF termination and AF recurrence during follow-up. It can help in establishing an ablation plan, by prioritizing ablation from the shortest to the longest ACI zone