Explainable Electrocardiogram Analysis with Wave Decomposition: Application to Myocardial Infarction Detection

International audienceAutomatic analysis of electrocardiograms with adequate explainability is a challenging task. Many deep learning based methods have been proposed for automatic classification of electrocardiograms. However, very few of them provide detailed explainable classification evidence. In our study, we explore explainable ECG classification through explicit decomposition of single-beat (median-beat) ECG signal. In particular, every single-beat ECG sample is decomposed into five subwaves and each subwave is parameterised by a Frequency Modulated Moebius. Those parameters have explicit meanings for ECG interpretation. In stead of solving the optimisation problem iteratively which is timeconsuming, we make use of an Cascaded CNN network to estimate the parameters for each single-beat ECG signal. Our preliminary results show that with appropriate position regularisation strategy, our neural network is able to estimate the subwave for P, Q, R, S, T events and maintain a good reconstruction accuracy (with R2 score 0.94 on test dataset of PTB-XL) in a unsupervised manner. Using the estimated parameters, we achieve very good classification and generalisation performance on myocardial infarction detection on four different datasets. The features of high importance are in accordance with clinical interpretations

Volume overload impact on 3D right ventricular shape and strain: comparative analysis of tetralogy of Fallot and atrial septal defect patients

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Cardiac Motion Modeling with Parallel Transport and Shape Splines

International audienceIn cases of pressure or volume overload, probing cardiac function may be difficult because of the interactions between shape and deformations.In this work, we use the LDDMM framework and parallel transport to estimate and reorient deformations of the right ventricle. We then propose a normalization procedure for the amplitude of the deformation, and a second-order spline model to represent the full cardiac contraction. The method is applied to 3D meshes of the right ventricle extracted from echocardiographic sequences of 314 patients divided into three disease categories and a control group. We find significant differences between pathologies in the model parameters, revealing insights into the dynamics of each disease

Imaging in pulmonary hypertension: Focus on the role of echocardiography

SummaryPatients with pulmonary hypertension must be evaluated using a multimodality approach to ensure a correct diagnosis and basal evaluation as well as a prognostic assessment. Beyond the assessment of pulmonary pressures, the echocardiographical examination allows the evaluation of right ventricular adaptation to elevated afterload. Numbers of variables are commonly used in the assessment of the pulmonary hypertension patient in order to detect changes in right heart geometry, right-to-left interaction and right ventricular dysfunction. Whereas an isolated change in one echocardiographical variable is not meaningful, multiple echocardiographical variable modifications together provide accurate information. In this review, we will link pulmonary hypertension pathophysiological changes with echocardiographical indices and describe the clinical implications of echocardiographical findings

Cardiac manifestations of MIS-C: cardiac magnetic resonance and speckle-tracking data

BackgroundCardiac involvement is central in MIS-C and represents the main cause of morbidity. In this study, we aimed to assess myocardial damage in patients with MIS-C using cardiac magnetic resonance (CMR) during the acute phase, as well as left ventricular and atrial longitudinal strain on admission, at discharge, and after 3 months.MethodsWe performed a single-center prospective cohort study and case–control study. Between September 2020 and February 2022, we enrolled 39 patients hospitalized for MIS-C at our center. We performed left ventricular and atrial longitudinal 2D strain analysis on admission and during follow-up; echocardiographic data were compared to a matched control population. Patients above 4 years old with increased troponin underwent CMR.ResultsOf 24 patients (mean age: 8.2 ± 4.9 years) who underwent CMR, 14 (58%) presented myocardial edema and 6 (25%) late gadolinium enhancement (LGE). LGE was associated with older age (p < 0.01), increased BMI (p = 0.03), increased ferritin levels (p < 0.001), lower left ventricular (LV) ejection fraction (p < 0.001), LV longitudinal strain (p = 0.004), left atrial (LA) strain (p = 0.05), and prolonged hospital stay (p = 0.02). On admission, LV ejection fraction, LV longitudinal strain, and LA strain were impaired, but each improved gradually over time; LVEF was the fastest to recover, while global LV longitudinal strain was still impaired as compared to controls after 3 months (p = 0.01).ConclusionOur study demonstrates that myocardial injury is present in a quarter of MIS-C patients, and impaired LA and LV myocardial deformation persist for at least several weeks after the acute phase. CMR and LV/LA strain could help us to individualize follow-up of MIS-C patients

Right ventricular function in acute pulmonary embolism: a three-dimensional strain study

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Non-Invasive Pressure Estimation in Patients with Pulmonary Arterial Hypertension: Data-driven or Model-based?

International audienceRight heart catheterisation is considered as the gold standard for the assessment of patients with suspected pulmonary hyper-tension. It provides clinicians with meaningful data, such as pulmonary capillary wedge pressure and pulmonary vascular resistance, however its usage is limited due to its invasive nature. Non-invasive alternatives, like Doppler echocardiography could present insightful measurements of right heart but lack detailed information related to pulmonary vascu-lature. In order to explore non-invasive means, we studied a dataset of 95 pulmonary hypertension patients, which includes measurements from echocardiography and from right-heart catheterisation. We used data extracted from echocardiography to conduct cardiac circulation model per-sonalisation and tested its prediction power of catheter data. Standard machine learning methods were also investigated for pulmonary artery pressure prediction. Our preliminary results demonstrated the potential prediction power of both data-driven and model-based approaches

Three-dimensional right-ventricular regional deformation and survival in pulmonary hypertension

International audienceBackground: Survival in pulmonary hypertension (PH) relates to right ventricular (RV) function. However, the RV unique anatomy and structure limit 2D analysis and its regional 3D function has not been studied yet. The aim of this study was to assess the implications of global and regional 3D RV deformation on clinical condition and survival in adults with PH and healthy controls.Methods and Results: We collected a prospective longitudinal cohort of 104 consecutive PH patients and 34 healthy controls between September 2014 and December 2015. Acquired 3D transthoracic RV echocardiographic sequences were analysed by semi- automatic software (TomTec 4D RV-Function 2.0). Output meshes were post-processed to extract regional motion and deformation. Global and regional statistics provided deformation patterns for each subgroup of subjects.RV lateral and inferior regions showed the highest deformation. In PH patients, RV global and regional motion and deformation (both circumferential, longitudinal and area strain) were affected in all segments (p-18% was the most powerful RV function parameter, identifying patients with a 48%-increased risk of death (AUC 0.83 [0.74-0.90], p<0.001).Conclusions: RV strain patterns gradually worsen in PH patients and provide independent prognostic information in this population

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