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This article presents a method of QRS complex detection and more precisely the R wave in an electrocardiogram (ECG) based on the mathematics morphology which calls upon the four operators’ morphology, erosion, dilation, opening and closing. These operators are combined with a window relocated which is called the structuring element. Morphological filtering uses the structuring element to extract the shape information from ECG signal. The effectiveness of the proposed algorithm is tested by using recordings obtained from the MIT-BIH arrhythmia database. Experiment results show that the proposed algorithm outperforms the other algorithms.

International audience; no abstract

International audience; no abstract

Arrhythmia is one kind of diseases that gives rise to the death and possibly forms the immedicable danger. The most common cardiac arrhythmia is the ventricular premature beat. The main purpose of this study is to develop an efficient arrhythmia detection algorithm based on Empirical Mode Decomposition (EMD). This algorithm requires the following stages: band-pass Butterworth filters, Empirical Mode Decomposition, sum the first three Intrinsic Functions Mode (IMFs), and take its absolute value, retain the amplitudes, find the position of the maximum. The excellent performance of the algorithm is confirmed by a sensitivity of 99.82 % (204 false negatives) and a positive predictivity of 99.89% (114 false positives) against the MIT-BIH arrhythmia database. Global Journal of Computer Science and Technology, Volume 11 Issue 20 Version 1.0 December 2011.

ECG signals by the Wigner-Ville Distribution (WVD) in order to classify related pathologies. The major problem of the WVD is the interferences (cross-terms). The Smoothed Pseudo-Wigner-Ville Distribution (SPWVD) makes it possible to reduce these interferences by a suitable choice of two windows H and G and their corresponding sizes Lg and Lh. We propose here a classification method based on the SPWVD and the nearest representative decision rule, the latter requires the choice of a distance and the definition of a representative for each training class. Two beat classes were considered: normal beats (NOR) and premature ventricular beats (PVC). By using the MIT-BIH data-base, the method was tested on a set composed of more than 80.000 beats. The best classification rate that we obtain is 90,66%, this one was carried out by using only the first most discriminant coefficient. HuMaN 07 proceedings, Timimoun March,12-13-14 2007.

We present in this work the efficiency of the wavelet transform, in exploring non-stationary signals such as those containing transients and discontinuities, and time varying spectra signals. We examine the nonstationarity problem as well as the alternative solutions suggested to deal with like the time-frequency analysis. In this context, we have applied the continuous wavelet transform –CWT- to a set of classical types of signals showing each a particular feature and to a real signal, which is the electrocardiogram ECG signal to evaluate the CWT efficiency. The obtained results demonstrated the higher ability of the wavelet transform in localizing specified temporal and spectral features of a signal. Conférence Internationale sur les Systèmes de Télécommunication , d’Electronique Médicale et d’Automatique, CISTEMA’2003

L’électrocardiographie explore l’activité électrique du coeur par enregistrement des variations des ondes électriques de l’activité de polarisation et de dépolarisation ventriculaire et auriculaires du coeur. L’activité cardiaque constitue l’un des plus importants paramètres déterminant l’état d’un sujet. Elle se traduit par l’apparition de plusieurs ondes sur le tracé de l’électrocardiographe : c’est le signal cardiaque, l’électrocardiogramme : ECG. L'analyse du signal ECG et l'identification des ses paramètres constituent une étape primordiale pour le diagnostic. Cependant, des sources de bruits telles que les variations de la ligne de base, l'interférence électrique du réseau 50Hz, et d'autres artefacts, mettent en arène la mise en oeuvre des techniques d'analyse et d'identification utilisées. Toutefois, un ensemble de méthodes et d'algorithme sont développés compte tenu de l'importance de ce signal et son exploitation en routine clinique dans le diagnostic des cas pathologiques cardiaques. Cette étude s’inscrit dans ce problématique et propose un classificateur des arythmies cardiaques par application des réseaux de neurones. Les résultats ont été validés par des signaux ECG des différents patients de la base de données « MIT BIH Arrhythmia Database ». Conférence Internationale sur les Systèmes de Télécommunication , d’Electronique Médicale et d’Automatique, CISTEMA’2003

The atrial activity of the human heart is normally visible in the electrocardiogram ( ECG) signal as a P-wave. In patients with intermittent artery heart disease, a different P-wave morphology can sometimes be seen, indicating atrial conduction defects. The purpose of this study was to use a supervised neural network (NN)- based algorithm to discriminate between an atrial premature beats (APB) and normal ones. The performance of the method was measured using eight recordings of each type from MIT-BIH database, based on the morphology of the P-wave and the duration of the QS segment between the Q-wave of the APB beat and the previous S-wave. The method achieved a sensitivity of 92 % and a specificity of 89 % in discrimination of APB beats from normal ones. The results show that neural networks can be used in electrocardiogram (ECG) processing in cases where fast and reliable detection of artrial premature beat is desired as in the case of critical units (CCU’s). Conférence Internationale sur les Systèmes de Télécommunication , d’Electronique Médicale et d’Automatique, CISTEMA’2003