International audienceThe Extended Kalman Filter (EKF) is a very popular tool dealing with state estimation. Its continuous-discrete version (CD-EKF) estimates the state trajectory of continuous-time nonlinear models, whose internal state is described by a stochastic differential equation and which is observed through a noisy nonlinear form of the sampled state. The prediction step of the CD-EKF leads to solve a differential equation that cannot be generally solved in a closed form. This technical note presents an overview of the numerical methods, including recent works, usually implemented to approximate this filter. Comparisons of theses methods on two different nonlinear models are finally presented. The first one is the Van der Pol oscillator which is widely used as a benchmark. The second one is a neuronal population model. This more original model is used to simulate EEG activity of the cortex. Experiments showed better stability properties of implementations for which the positivity of the prediction matrix is guaranteed

Abstract-Previous studies have shown that cardiac microacceleration signals, recorded either cutaneously, or embedded into the tip of an endocardial pacing lead, provide meaningful information to characterize the cardiac mechanical function. This information may be useful to personalize and optimize the cardiac resynchronization therapy, delivered by a biventricular pacemaker, for patients suffering from chronic heart failure. The present paper focuses on the improvement of a previously proposed method for the estimation of the systole period from a signal acquired with a cardiac micro-accelerometer (SonR sensor, Sorin CRM SAS, France). We propose an optimal algorithm switching approach, to dynamically select the best configuration of the estimation method, as a function of different control variables, such as signal-to-noise ratio or heart rate. This method was evaluated on a database containing recordings from 31 patients suffering from chronic heart failure and implanted with a biventricular pacemaker, for which various cardiac pacing configurations were tested. Ultrasound measurements of the systole period were used as a reference and the improved method was compared with the original estimator. A reduction of 11% on the absolute estimation error was obtained for the systole period with the proposed algorithm switching approach.

This paper describes a macroscopic neurophysiologically relevant model of the entorhinal cortex (EC), a brain structure largely involved in human mesio-temporal lobe epilepsy. This model is intervalidated in the experimental framework of ictogenesis animal model (isolated guinea-pig brain perfused with bicuculline). Using sensitivity and stability analysis, an investigation of model parameters related to GABA neurotransmission (recognized to be involved in epileptic activity generation) was performed. Based on spectral and statistical features, simulated signals generated from the model for multiple GABAergic inhibition-related parameter values were classified into eight classes of activity. Simulated activities showed striking agreement (in terms of realism) with typical epileptic activities identified in field potential recordings performed in the experimental model. From this combined computational/experimental approach, hypotheses are suggested about the role of different types of GABAergic neurotransmission in the generation of epileptic activities in EC.

In the context of pre-surgical evaluation of epileptic patients, depth-EEG signals constitute a valuable source of information to characterize the spatiotemporal organization of paroxysmal interictal and ictal activities, prior to surgery. However, interpretation of these very complex data remains a formidable task. Indeed, interpretation is currently mostly qualitative and efforts are still to be produced in order to quantitatively assess pathophysiological information conveyed by signals. The proposed EEG model-based approach is a contribution to this effort. It introduces both a physiological parameter set which represents excitation and inhibition levels in recorded neuronal tissue and a methodology to estimate this set of parameters. It includes Sequential Monte Carlo nonlinear filtering to estimate hidden state trajectory from EEG and Particle Swarm Optimization to maximize a likelihood function deduced from Monte Carlo computations. Simulation results illustrate what it can be expected from this methodology.