Sabir Jacquir

Automatic detection of P, QRS and T patterns in 12 leads ECG signal based on CWT

Abstract In this paper, a new method based on the continuous wavelet transform is described in order to detect the QRS, P and T waves. QRS, P and T waves may be distinguished from noise, baseline drift or irregular heartbeats. The algorithm, described in this paper, has been evaluated using the Computers in Cardiology (CinC) Challenge 2011 database and also applied on the MIT-BIH Arrhythmia database (MITDB). The data from the CinC Challenge 2011 are standard 12 ECG leads recordings with full diagnostic bandwidth compared to the MITDB which only includes two leads for each ECG signal. Firstly, our algorithm is validated using fifty 12 leads ECG samples from the CinC collection. The samples have been chosen in the “acceptable records” list given by Physionet. The detection and the duration delineation of the QRS, P and T waves given by our method are compared to expert physician results. The algorithm shows a sensitivity equal to 0.9987 for the QRS complex, 0.9917 for the T wave and 0.9906 for the P wave. The accuracy and the Youden index values show that the method is reliable for the QRS, T and P waves detection and delineation. Secondly, our algorithm is applied to the MITDB in order to compare the detection of QRS wave to results of other some works in the literature.

Experimental study of electrical FitzHugh-Nagumo neurons with modified excitability

We present an electronical circuit modelling a FitzHugh-Nagumo neuron with a modified excitability. To characterize this basic cell, the bifurcation curves between stability with excitation threshold, bistability and oscillations are investigated. An electrical circuit is then proposed to realize a unidirectional coupling between two cells, mimicking an inter-neuron synaptic coupling. In such a master-slave configuration, we show experimentally how the coupling strength controls the dynamics of the slave neuron, leading to frequency locking, chaotic behavior and synchronization. These phenomena are then studied by phase map analysis. The architecture of a possible neural network is described introducing different kinds of coupling between neurons.

Spiking dynamics of interacting oscillatory neurons

Spiking sequences emerging from dynamical interaction in a pair of oscillatory neurons are investigated theoretically and experimentally. The model comprises two unidirectionally coupled FitzHugh-Nagumo units with modified excitability (MFHN). The first (master) unit exhibits a periodic spike sequence with a certain frequency. The second (slave) unit is in its excitable mode and responds on the input signal with a complex (chaotic) spike trains. We analyze the dynamic mechanisms underlying different response behavior depending on interaction strength. Spiking phase maps describing the response dynamics are obtained. Complex phase locking and chaotic sequences are investigated. We show how the response spike trains can be effectively controlled by the interaction parameter and discuss the problem of neuronal information encoding.

Detection of Complex Fractionated Atrial Electrograms Using Recurrence Quantification Analysis

Atrial 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 (CFAEs) 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.

Computation of the electrical potential inside the nerve induced by an electrical stimulus

The aim is to investigate the activation conditions of the different nerves which control the bladder. The selective stimulation of the nerve fibers depends on electrode configuration and intensity of applied current. The goal of this study is to compute the electrical potential inside the nerve due to an applied boundary currents. A symmetrically cylindrical model, representing the geometry and electrical conductivity of a nerve surrounded by a connective tissue and a cuff is used. In the quasistatic approximation, the problem can be modeled by a Poisson equation with Neumann boundary conditions. A symmetric boundary integral formulation is discretized using mixed finite elements. We can thus compute an electrical potential distribution depending on the electrode configuration and the applied current inside a nerve. Our results show that the distribution of the electrical potential inside a nerve or a fascicle depends on the geometry of the electrode and the shape of the applied current.

INVESTIGATION OF MICRO SPIRAL WAVES AT CELLULAR LEVEL USING A MICROELECTRODE ARRAYS TECHNOLOGY

During cardiac arrhythmia, functional reentries may take the form of spiral waves. The purpose of this study was to induce spiral waves by an electrical stimulation of cultured neonatal rat cardiomyocytes using a microelectrode arrays technology. In basal conditions, cardiac muscle cells in monolayer culture displayed a planar wavefront propagation. External electrical impulse trains induced severe arrhythmia and spiral waves appeared. This in vitro generation of spiral wave opens a new way to test the anti-arrhythmic drugs and for strategies at microscopically scale.

Frequency mapping in dynamic light emission with wavelet transform

Abstract Dynamic photon emission microscopy is an efficient tool to analyse today’s integrated circuit. Nevertheless, the reduction of transistor’s dimensions leads to more complex acquisitions where many spots can be seen. A frequency characterization of the whole acquired area can help to have a better understanding of it. With that purpose in mind, a new methodology to draw frequency mapping of dynamic light emission acquisition is reported. It is fully automated and based on wavelet transform and autocorrelation function. Regarding the possible use in an industrial context, the suggested method can help to localize abnormal emission activity and it gives some perspectives on automatic databases comparison.

Cardiac arrhythmias induced by an electrical stimulation at a cellular level

To provide insights into the impulse propagation between cardiac myocytes, we performed studies of excitation spread with cellular resolution in confluent monolayers of cultured cardiomyocytes (CM). Multisite field potentials have been recorded using microelectrode arrays (MEA) technology in a basal condition and in proarrhythmic conditions induced by a high frequency electrical stimulation. The in vitro observation of spiral waves opens a new way to test the anti-arrhythmic drugs or strategies at cellular level.

Improvement of signal to noise ratio in electro optical probing technique by wavelets filtering

Electro Optical Probing (EOP) technique is an efficient backside contactless technique to measure waveforms in modern VLSI circuits. The signal related intensity variation of the reflected beam is very weak therefore, to acquire a signal with enough Signal to Noise Ratio, averaging techniques are usually performed. Resulting acquisition time for one waveform is too long to implement point to point probing to image mode. To overcome this limitation, we have developed a new filtering by wavelets approach to keep a good SNR while significantly reducing this acquisition time. It opens the doors to new multipoint probing applications. In this paper, we describe the technique, its efficiency in terms of SNR, execution time and limits.

Unsupervised image processing scheme for transistor photon emission analysis in order to identify defect location

Abstract. The study of the light emitted by transistors in a highly scaled complementary metal oxide semiconductor (CMOS) integrated circuit (IC) has become a key method with which to analyze faulty devices, track the failure root cause, and have candidate locations for where to start the physical analysis. The localization of defective areas in IC corresponds to a reliability check and gives information to the designer to improve the IC design. The scaling of CMOS leads to an increase in the number of active nodes inside the acquisition area. There are also more differences between the spot’s intensities. In order to improve the identification of all of the photon emission spots, we introduce an unsupervised processing scheme. It is based on iterative thresholding decomposition (ITD) and mathematical morphology operations. It unveils all of the emission spots and removes most of the noise from the database thanks to a succession of image processing. The ITD approach based on five thresholding methods is tested on 15 photon emission databases (10 real cases and 5 simulated cases). The photon emission areas’ localization is compared to an expert identification and the estimation quality is quantified using the object consistency error.