M. Lahlimi

Work place: STIC laboratory, Faculty of sciences, University Chouaib Doukkali, El Jadida, Morocco

E-mail: lahlimi.mounir@gmail.com


Research Interests: Engineering, Computational Engineering, Computational Science and Engineering


Mounir LAHLIMI holds an engineering degree in electromechanical from National School of Mines of Rabat (Morocco) and a master degree in Optics Image Vision from University Jean Monnet Saint-Etienne (France). A professional engineer, His career of more than 10 years in condition monitoring, is an expert engineering of Dynae, specialized Services Company In the monitoring and diagnosis of machines. His research interests are in the areas of signal processing and diagnostic of rotating machines.

Author Articles
Cyclic Analysis of Phonocardiogram Signals

By A.Choklati Khalid SABRI M. Lahlimi

DOI: https://doi.org/10.5815/ijigsp.2017.10.01, Pub. Date: 8 Oct. 2017

Acoustic vibrations of the heart in time domain correspond to phonocardiogram (PCG) signal. A PCG signal, in the healthy case, consists of two fundamental sounds s1 and s2 produced by the mechanical functioning of the heart. Abnormalities in the heart valves correspond to other cardiac sounds than s1 and s2. This makes PCG signal a valuable tool related to the track of heart diseases. Actually, the characterization and the analysis of PCG signals is being a fertile area of study and investigation. However, most of the topics which treated this area of research focused only on time-frequency analysis, without exploiting the periodic character of PCG signal due to the limitations of the PCG modeling. In this work, we propose a coherent mathematical model for PCG signals based on cyclostationarity and Gabor kernel. The motivation behind is to define a framework, utilizing cyclic statistic due to noise robustness, for a full description of PCG signals, which leads to an easy and efficient early identification of certain heart abnormalities. The validation of the proposed model and its capacity to reflect the heart functioning is tested over synthetic and real data sets.

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