Search results for: M.H. Bédoui

Authors: Ibtihel Nouira, Asma Ben Abdallah, Ibtissem Kouaja, Mohamed Hèdi Bedoui

Abstract:

The processing of the electrocardiogram (ECG) signal consists essentially in the detection of the characteristic points of signal which are an important tool in the diagnosis of heart diseases. The most suitable are the detection of R waves. In this paper, we present various mathematical tools used for filtering ECG using digital filtering and Discreet Wavelet Transform (DWT) filtering. In addition, this paper will include two main R peak detection methods by applying a windowing process: The first method is based on calculations derived, the second is a time-frequency method based on Dyadic Wavelet Transform DyWT.

Keywords: Derived calculation methods, Electrocardiogram, R peaks, Wavelet Transform.

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Authors: K. Ben Khalifa, M.H. Bédoui, M. Dogui, F. Alexandre

Abstract:

Several studies have been carried out, using various techniques, including neural networks, to discriminate vigilance states in humans from electroencephalographic (EEG) signals, but we are still far from results satisfactorily useable results. The work presented in this paper aims at improving this status with regards to 2 aspects. Firstly, we introduce an original procedure made of the association of two neural networks, a self organizing map (SOM) and a learning vector quantization (LVQ), that allows to automatically detect artefacted states and to separate the different levels of vigilance which is a major breakthrough in the field of vigilance. Lastly and more importantly, our study has been oriented toward real-worked situation and the resulting model can be easily implemented as a wearable device. It benefits from restricted computational and memory requirements and data access is very limited in time. Furthermore, some ongoing works demonstrate that this work should shortly results in the design and conception of a non invasive electronic wearable device.

Keywords: Electroencephalogram interpretation, artificialneural networks, vigilance states, hardware implementation

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Authors: I. Slim, H. Akkari, A. Ben Abdallah, I. Bhouri, M. Hedi Bedoui

Abstract:

Osteoporosis is a common disease characterized by low bone mass and deterioration of micro-architectural bone tissue, which provokes an increased risk of fracture. This work treats the texture characterization of trabecular bone radiographs. The aim was to analyze according to clinical research a group of 174 subjects: 87 osteoporotic patients (OP) with various bone fracture types and 87 control cases (CC). To characterize osteoporosis, Fractal and MultiFractal (MF) methods were applied to images for features (attributes) extraction. In order to improve the results, a new method of MF spectrum based on the q-stucture function calculation was proposed and a combination of Fractal and MF attributes was used. The Support Vector Machines (SVM) was applied as a classifier to distinguish between OP patients and CC subjects. The features fusion (fractal and MF) allowed a good discrimination between the two groups with an accuracy rate of 96.22%.

Keywords: Fractal, micro-architecture analysis, multifractal, SVM, osteoporosis.

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Authors: A. Ben Abdallah, H. Essabbah, M. H. Bedoui

Abstract:

The myocardial sintigraphy is an imaging modality which provides functional informations. Whereas, coronarography modality gives useful informations about coronary arteries anatomy. In case of coronary artery disease (CAD), the coronarography can not determine precisely which moderate lesions (artery reduction between 50% and 70%), known as the “gray zone", are haemodynamicaly significant. In this paper, we aim to define the relationship between the location and the degree of the stenosis in coronary arteries and the observed perfusion on the myocardial scintigraphy. This allows us to model the impact evolution of these stenoses in order to justify a coronarography or to avoid it for patients suspected being in the gray zone. Our approach is decomposed in two steps. The first step consists in modelling a coronary artery bed and stenoses of different location and degree. The second step consists in modelling the left ventricle at stress and at rest using the sphercical harmonics model and myocardial scintigraphic data. We use the spherical harmonics descriptors to analyse left ventricle model deformation between stress and rest which permits us to conclude if ever an ischemia exists and to quantify it.

Keywords: Spherical harmonics model, vascular bed, 3D reconstruction, left ventricle, myocardial scintigraphy.

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Authors: B. Mahmoud, M.H. Bedoui, R. Raychev, H. Essabbah

Abstract:

We present in this paper an acquisition and treatment system designed for semi-analog Gamma-camera. It consists of a nuclear medical Image Acquisition, Treatment and Display chain(IATD) ensuring the acquisition, the treatment of the signals(resulting from the Gamma-camera detection head) and the scintigraphic image construction in real time. This chain is composed by an analog treatment board and a digital treatment board. We describe the designed systems and the digital treatment algorithms in which we have improved the performance and the flexibility. The digital treatment algorithms are implemented in a specific reprogrammable circuit FPGA (Field Programmable Gate Array).interface for semi-analog cameras of Sopha Medical Vision(SMVi) by taking as example SOPHY DS7. The developed system consists of an Image Acquisition, Treatment and Display (IATD) ensuring the acquisition and the treatment of the signals resulting from the DH. The developed chain is formed by a treatment analog board and a digital treatment board designed around a DSP [2]. In this paper we have presented the architecture of a new version of our chain IATD in which the integration of the treatment algorithms is executed on an FPGA (Field Programmable Gate Array)

Keywords: Nuclear medical image, scintigraphic image, digitaltreatment, linearity, spectrometry, FPGA.

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