An analytical approach to initiation of propagating fronts

We consider the problem of initiation of a propagating wave in a one-dimensional excitable fibre. In the Zeldovich-Frank-Kamenetsky equation, a.k.a. Nagumo equation, the key role is played by the "critical nucleus'' solution whose stable manifold is the threshold surface separating initial conditions leading to initiation of propagation and to decay. Approximation of this manifold by its tangent linear space yields an analytical criterion of initiation which is in a good agreement with direct numerical simulations.Comment: 4 pages, 2 figures, submitted to Phys Rev Letter

Nonlinear dynamics of two-dimensional cardiac action potential duration mapping model with memory

International audienceThe aim of this work is the analysis of the nonlinear dynamics of two-dimensional mapping model of cardiac action potential duration (2D-map APD) with memory derived from one dimensional map (1D-map). Action potential duration (APD) restitution, which relates APD to the preceding diastolic interval (DI), is a useful tool for predicting cardiac arrhythmias. For a constant rate of stimulation the short action potential during alternans is followed by a longer DI and inversely. It has been suggested that these differences in DI are responsible for the occurrence and maintenance of APD alternans. We focus our attention on the observed bifurcations produced by a change in the stimulation period and a fixed value of a particular parameter in the model. This parameter provides new information about the dynamics of the APD with memory, such as the occurrence of bistabilities not previously described in the literature, as well as the fact that synchronization rhythms occur in different ways and in a new fashion as the stimulation frequency increases. Moreover, we show that this model is flexible enough as to accurately reflect the chaotic dynamics properties of the APD: we have highlighted the fractal structure of the strange attractor of the 2D-map APD, and we have characterized chaos by tools such as the calculation of the Lyapunov exponents, the fractal dimension and the Kolmogorov entropy, with the next objective of refining the study of the nonlinear dynamics of the duration of the action potential and to apply methods of controlling chaos

Pattern image enhancement by automatic focus correction

International audienceOptical analysis techniques are key tools for the failure analysis and defect localization in integrated circuits. Using a confocal laser microscope, it is possible to extract different pieces of information such as spatial distribution of signals or voltage waveforms. Blur is getting a more and critical issue as technology pitch is getting smaller, very close to resolution limits. Find the correct focus, is a recurrent problem to solve in optical microscopy. The expert has to correct the blur disrupting the image, by manually searching the good focus. By consequences, this step may take a long time to identify regions of interest in the circuit. With this purpose in mind, the aim of this paper, is to propose an automatic process estimating the out-of-focus blur parameters characterized by specific attributes. Proposed technique takes advantage of extracted features in the Discrete Cosine Transform (DCT) of blurred images. The blur information is identified and allows to recover the blur's kernel. Finally, the accuracy and the robustness of the suggested process is demonstrated on real blurred images.Focus correctionFailure analysisDiscrete Cosine TransformThresholdingRadon transformImage processin

Detection of Complex Fractionated Atrial Electrograms (CFAE)using Recurrence Quantification Analysis.

International audienceAtrial fibrillation (AF) is the most common cardiac arrhythmia but its proarrhythmic substrate remains unclear. Reentrant electrical activity in the atria may be responsible for AF maintenance. Over the last decade, different catheter ablation strategies targeting the electrical substrate of the left atrium have been developed in order to treat AF. Complex Fractionated Atrial Electrograms (CFAE) recorded in the atria may represent not only reentry mechanisms, but also a large variety of bystander electrical wave fronts. In order to identify CFAE involved in AF maintenance as a potential target for AF ablation, we have developed an algorithm based on nonlinear data analysis using Recurrence Quantification Analysis (RQA). RQA features make it possible to quantify hidden structures in a signal and offer clear representations of different CFAE types. Five RQA features were used to qualify CFAE areas previously tagged by a trained electrophysiologist. Data from these analyzes were used by two classifiers to detect CFAE periods in a signal. While a single feature is not sufficient to properly detect CFAE periods, the set of five RQA features combined with a classifier were highly reliable for CFAE detection

Signal and image processing techniques for VLSI failure analysis

International audienc

Reconstruction from experimental data of a mathematical model of cardiac tissue: A feasibility study

The aim of this work is to study the feasability of reconstruction mathematical model of cardiac tissue from intracellular recordings. It is studied using simulated data and the presented method is applied to the Aliev-Panfilov model. A dissociated scheme is proposed and the estimation of some parameters is investigated in case of ideal or noisy data. The influence of the number and distribution of electrodes is then studied. 1

Unsupervised learning for signal mapping in dynamic photon emission

International audienceDynamic photon emission is an efficient tool for timing analysis of various areas. However, advances in transistors integration bring more complex test patterns and more objects to investigate. As a consequence, understanding the analyzed area and finding nodes of interest can be difficult. In this paper, a method for drawing synthesis of the various signals met inside an area is reported. It is based on unsupervised learning tool for dimension reduction and clustering. The process is applied to real data to show its efficiency and its quality is evaluated