Pierre Roussel

Kele Xu, Yin Yang, Aurore Jaumard-Hakoun et al.

This article describes the development of a platform designed to visualize the 3D motion of the tongue using ultrasound image sequences. An overview of the system design is given and promising results are presented. Compared to the analysis of motion in 2D image sequences, such a system can provide additional visual information and a quantitative description of the tongue 3D motion. The platform can be useful in a variety of fields, such as speech production, articulation training, etc.

Kele Xu, Yin Yang, Clémence Leboullenger et al.

This article describes a contour-based 3D tongue deformation visualization framework using B-mode ultrasound image sequences. A robust, automatic tracking algorithm characterizes tongue motion via a contour, which is then used to drive a generic 3D Finite Element Model (FEM). A novel contour-based 3D dynamic modeling method is presented. Modal reduction and modal warping techniques are applied to model the deformation of the tongue physically and efficiently. This work can be helpful in a variety of fields, such as speech production, silent speech recognition, articulation training, speech disorder study, etc.

Charles-Henri Cappelaere, R. Dubois, P. Roussel et al.

The purpose of this study is to optimize the selection of prophylactic cardioverter defibrillator implantation candidates. Currently, the main criterion for implantation is a low Left Ventricular Ejection Fraction (LVEF) whose specificity is relatively poor. We designed two classifiers aimed to predict, from long term ECG recordings (Holter), whether a low-LVEF patient is likely or not to undergo ventricular arrhythmia in the next six months. One classifier is a single hidden layer neural network whose variables are the most relevant features extracted from Holter recordings, and the other classifier has a structure that capitalizes on the physiological decomposition of the arrhythmogenic factors into three disjoint groups: the myocardial substrate, the triggers and the autonomic nervous system (ANS). In this ad hoc network, the features were assigned to each group; one neural network classifier per group was designed and its complexity was optimized. The outputs of the classifiers were fed to a single neuron that provided the required probability estimate. The latter was thresholded for final discrimination A dataset composed of 186 pre-implantation 30-mn Holter recordings of patients equipped with an implantable cardioverter defibrillator (ICD) in primary prevention was used in order to design and test this classifier. 44 out of 186 patients underwent at least one treated ventricular arrhythmia during the six-month follow-up period. Performances of the designed classifier were evaluated using a cross-test strategy that consists in splitting the database into several combinations of a training set and a test set. The average arrhythmia prediction performances of the ad-hoc classifier are NPV = 77% $\pm$ 13% and PPV = 31% $\pm$ 19% (Negative Predictive Value $\pm$ std, Positive Predictive Value $\pm$ std). According to our study, improving prophylactic ICD-implantation candidate selection by automatic classification from ECG features may be possible, but the availability of a sizable dataset appears to be essential to decrease the number of False Negatives.