Multivariate echocardiographic analysis integrating signal processing and machine learning

La thérapie de resynchronisation cardiaque (CRT) permet de soigner des cas spécifiques d’insuffisance cardiaque. Les patients ne présentent malheureusement aucune amélioration dans 30% des cas. Il est par conséquent nécessaire de bien sélectionner les candidats susceptibles de répondre à la CRT. La littérature propose de nouvelles caractéristiques pour la sélection des patients mais aucune n’a réussi à intégrer les directives. Notre équipe a cherché à montrer l’intérêt des caractéristiques issues des courbes de déformations du ventricule gauche. Ainsi, nous avons travaillé sur l’utilisation de l’apprentissage automatique pour sélectionner les caractéristiques les plus significatives pour déterminer la réponse à la CRT. Deux méthodes ont été utilisées pour la sélection des caractéristiques. La première utilise une méthode de regroupement non-supervisée pour associer les patients aux caractéristiques similaires afin de former des groupes et observer les caractéristiques les plus significatives à la séparation entre les répondeurs et non-répondeurs. La deuxième utilise la méthode OOB basée sur les forêts d’arbres pour ordonner les caractéristiques selon leur importance pour estimer la réponse à la CRT. L’objectif final a été la classification des patients pour prédire la réponse à la CRT. Pour cela, un processus d’apprentissage comprenant une phase de sélection des caractéristiques et une phase de classification à l’aide de la méthode de Monte-Carlo appliquée aux forêts d’arbres a permis une meilleure détection des candidats susceptibles de répondre à la CRT.Cardiac resynchronization therapy (CRT) is used to treat specific cases of heart failure. Unfortunately, patients do not show any improvement in 30% of cases. It is therefore necessary to carefully select the candidates who can respond to CRT. The literature suggests new characteristics for patient selection, but none have successfully incorporated the guidelines. Our team have tried to show the interest of the features obtained from strain curves of the left ventricle. Thus, we used machine learning methods to select the most significant features to estimate response to CRT. Two methods were used for the selection of features. The first uses an unsupervised clustering method to associate patients with similar features in order to create groups and observe the most significant features to separate responders and non-responders. The second uses the OOB method based on random forests to rank the features according to their importance to estimate the response to CRT. The final goal was to classify patients to predict response to CRT. For this, a learning process including a feature selection phase and a classification phase using the Monte-Carlo method applied to random forests allowed better detection of candidates who can respond to CRT

Classification automatique de signaux EMG pour reconnaître des pathologies.

International audienceThis paper proposes to assess the relevance of new automated tools for electromyography (EMG) analysis, in order to differentiate neuropathic from myo-pathic patterns. The challenge is to define the diagnosis with only one iEMG signal per patient. Our proposed method uses the decomposition of the EMG signal to characterize motor unit action potentials (MUAPs). The decomposition of each iEMG signal is carried out with EMGLAB. For each signal, the decomposition provides a code which is used by the automated classification algorithms. We use here the linear Support Vector Machine (SVM) and the Bagging Trees methods. For the learning process we use several EMG signals and in different parts of the muscle. Only one recorded electromyography EMG signal per subject is used for the diagnostic test. We evaluate the k − f old cross-validation and the confusion matrix for both models. The accuracy is 77.3% for the SVM and 68.2% for the Bagging Trees. These are the first developments of this tool to make it useful for clinical practice

Transforming spontaneous premature neonatal EEG to unpaired spontaneous fetal MEG using a CycleGan learning approach

A large body of electroencephalography (EEG) studies has characterized the spontaneous neural activity of premature neonates at different gestational ages. However, evaluation of normal and pathological fetal brain development is still a challenge due to the complexity of the extraction and analysis of fetal neural activity. Fetal magnetoencephalography (fMEG) is currently the only available technique to record fetal neural activity with a time resolution equivalent to that of EEG. However, the signatures and characteristics of fetal spontaneous neural activity are still largely unknown. Benefiting from progress in machine learning and artificial intelligence, we aimed to transfer premature EEG to fMEG, to characterize the manifestation of spontaneous activity using the knowledge obtained from premature EEG. In this study, 30 high-resolution EEG recordings from premature newborns and 44 fMEG recordings, both from 34 to 37 weeks of gestation (wGA) were used to develop a transfer function to predict the spontaneous neural activity of the fetus. After preprocessing, bursts of spontaneous activity were detected using the non-linear energy operator over both EEG and fMEG signals. Next, we proposed a CycleGAN-based model to transform the premature EEG to fMEG and vice versa and evaluated its performance with both time and frequency measurements on both forward and inverse conversions. In the time domain, the values were similar for the mean square error (< 5%) and correlation (0.91 ± 0.05 and 0.89 ± 0.08) for the EEG to fMEG and fMEG to EEG transformations between the original data and that generated by CycleGAN. However, considering the frequency content, the CycleGAN-based model modulated the frequency content of EEG to MEG transformed signals relative to the original signals by increasing the power, on average, in all frequency bands, except for the slow delta frequency band. Our developed model showed promising potential to generate a priori signatures of fMEG manifestations related to spontaneous neural activity. Collectively, this study represents the first steps toward identifying neurobiomarkers of fetal brain development

Echocardiographic view and feature selection for the estimation of the response to CRT

International audienceCardiac resynchronization therapy (CRT) is an implant-based therapy applied to patients with a specific heart failure (HF) profile. The identification of patients that may benefit from CRT is a challenging task and the application of current guidelines still induce a non-responder rate of about 30%. Several studies have shown that the assessment of left ventricular (LV) mechanics by speckle tracking echocardiography can provide useful information for CRT patient selection. A comprehensive evaluation of LV mechanics is normally performed using three different echocardioraphic views: 4, 3 or 2-chamber views. The aim of this study is to estimate the relative importance of strain-based features extracted from these three views, for the estimation of CRT response. Several features were extracted from the longitudinal strain curves of 130 patients and different methods of feature selection (out-of-bag random forest, wrapping and filtering) have been applied. Results show that more than 50% of the 20 most important features are calculated from the 4-chamber view. Although features from the 2- and 3-chamber views are less represented in the most important features, some of the former have been identified to provide complementary information. A thorough analysis and interpretation of the most informative features is also provided, as a first step towards the construction of a machine-learning chain for an improved selection of CRT candidates

Left ventricular strain for predicting the response to cardiac resynchronization therapy: two methods for one question

International audienceAIMS: Myocardial work (manually controlled software) and integral-derived longitudinal strain (automatic quantification of strain curves) are two promising tools to quantify dyssynchrony and potentially select the patients that are most likely to have a reverse remodelling due to cardiac resynchronization therapy (CRT). We sought to test and compare the value of these two methods in the prediction of CRT-response. MATERIALS AND RESULTS: Two hundred and forty-three patients undergoing CRT-implantation from three European referral centres were considered. The characteristics from the six-segment of the four-chamber view were computed to obtain regional myocardial work and the automatically generated integrals of strain. The characteristics were studied in mono-parametric and multiparametric evaluations to predict CRT-induced 6-month reverse remodelling. For each characteristic, the performance to estimate the CRT response was determined with the receiver operating characteristic (ROC) curve and the difference between the performances was statistically evaluated. The best area under the curve (AUC) when only one characteristic used was obtained for a myocardial work (AUC = 0.73) and the ROC curve was significantly better than the others. The best AUC for the integrals was 0.63, and the ROC curve was not significantly greater than the others. However, with the best combination of works and integrals, the ROC curves were not significantly different and the AUCs were 0.77 and 0.72. CONCLUSION: Myocardial work used in a mono-parametric estimation of the CRT-response has better performance compared to other methods. However, in a multiparametric application such as what could be done in a machine-learning approach, the two methods provide similar results

Prediction of response to cardiac resynchronization therapy using a multi-feature learning method

International audienceWe hypothesized that a multiparametric evaluation, based on the combination of electrocardiographic and echocardiographic parameters, could enhance the appraisal of the likelihood of reverse remodeling and prognosis of favorable clinical evolution to improve the response of cardiac resynchronization therapy (CRT). Three hundred and twenty-three heart failure patients were retrospectively included in this multicenter study. 221 patients (68%) were responders, defined by a decrease in left ventricle end-systolic volume ≥15% at the 6-month follow-up. In addition, strain data coming from echocardiography were analyzed with custom-made signal processing methods. Integrals of regional longitudinal strain signals from the beginning of the cardiac cycle to strain peak and to the instant of aortic valve closure were analyzed. QRS duration, septal flash and different other features manually extracted were also included in the analysis. The random forest (RF) method was applied to analyze the relative feature importance, to select the most significant features and to build an ensemble classifier with the objective of predicting response to CRT. The set of most significant features was composed of Septal Flash, E, E/A, E/EA, QRS, left ventricular end-diastolic volume and eight features extracted from strain curves. A Monte Carlo cross-validation method with 100 runs was applied, using, in each run, different random sets of 80% of patients for training and 20% for testing. Results show a mean area under the curve (AUC) of 0.809 with a standard deviation of 0.05. A multiparametric approach using a combination of echo-based parameters of left ventricular dyssynchrony and QRS duration helped to improve the prediction of the response to cardiac resynchronization therapy

Characterization of responder profiles for cardiac resynchronization therapy through unsupervised clustering of clinical and strain data

International audienceBackground - The mechanisms of improvement of left ventricular (LV) function with cardiac resynchronization therapy (CRT) are not yet elucidated. The aim of this study was to characterize CRT responder profiles through clustering analysis, on the basis of clinical and echocardiographic preimplantation data, integrating automatic quantification of longitudinal strain signals. Methods - This was a multicenter observational study of 250 patients with chronic heart failure evaluated before CRT device implantation and followed up to 4 years. Clinical, electrocardiographic, and echocardiographic data were collected. Regional longitudinal strain signals were also analyzed with custom-made algorithms in addition to existing approaches, including myocardial work indices. Response was defined as a decrease of ≥15% in LV end-systolic volume. Death and hospitalization for heart failure at 4 years were considered adverse events. Seventy features were analyzed using a clustering approach (k-means clustering). Results - Five clusters were identified, with response rates between 50% in cluster 1 and 92.7% in cluster 5. These five clusters differed mainly by the characteristics of LV mechanics, evaluated using strain integrals. There was a significant difference in event-free survival at 4 years between cluster 1 and the other clusters. The quantitative analysis of strain curves, especially in the lateral wall, was more discriminative than apical rocking, septal flash, or myocardial work in most phenogroups. Conclusions - Five clusters are described, defining groups of below-average to excellent responders to CRT. These clusters demonstrate the complexity of LV mechanics and prediction of response to CRT. Automatic quantitative analysis of longitudinal strain curves appears to be a promising tool to improve the understanding of LV mechanics, patient characterization, and selection for CRT

Left atrial strain is a predictor of left ventricular systolic and diastolic reverse remodelling in CRT candidates

International audienceAIMS: The left atrium (LA) has a pivotal role in cardiac performance and LA deformation is a well-known prognostic predictor in several clinical conditions including heart failure with reduced ejection fraction. The aim of this study is to investigate the effect of cardiac resynchronization therapy (CRT) on both LA morphology and function and to assess the impact of LA reservoir strain (LARS) on left ventricular (LV) systolic and diastolic remodelling after CRT. METHODS AND RESULTS: Two hundred and twenty-one CRT-candidates were prospectively included in the study in four tertiary centres and underwent echocardiography before CRT-implantation and at 6-month follow-up (FU). CRT-response was defined by a 15% reduction in LV end-systolic volume. LV systolic and diastolic remodelling were defined as the percent reduction in LV end-systolic and end-diastolic volume at FU. Indexed LA volume (LAVI) and LV-global longitudinal (GLS) strain were the main parameters correlated with LARS, with LV-GLS being the strongest determinant of LARS (r = -0.59, P &lt; 0.0001). CRT induced a significant improvement in LAVI and LARS in responders (both P &lt; 0.0001). LARS was an independent predictor of both LV systolic and diastolic remodelling at follow-up (r = -0.14, P = 0.049 and r = -0.17, P = 0.002, respectively). CONCLUSION: CRT induces a significant improvement in LAVI and LARS in responders. In CRT candidates, the evaluation of LARS before CRT delivery is an independent predictor of LV systolic and diastolic remodelling at FU