Sfax, Sfax | Tunisia
Main Specialties: Clinical Neurophysiology
Additional Specialties: Epilepsy
Actually I am an assistant professor at Sfax University, Tunisia, also a post doc at laboratory: Multimedia, Information Systems and Advanced Computing Laboratory (MIRACL), Sfax, Tunisia in a collaboration with laboratory Institute of Neuroscience and system (INS), Marseille, France. I did obtained my PHD in neuroscience from Aix Marseille University since 2012. My main Research interests are :Pre processing of electrophysiological signals EEG, MEG and IEEG, Separation between spikes and oscillations in MEG and IEEG in epilepsy, Despikifing IEEG signal to predict the buildup seizure, source localization of epileptic spikes and gamma oscillations in MEG and IEEG, Epileptic brain dynamic mapping, confrontation of the MEG recordings results versus Intracerebral EEG results, Embedding filtering methods using intelligent architecture, Evaluating centrality measures of brain dynamic.
Primary Affiliation: INSERM - Sfax, Sfax , Tunisia
PubMed Central Citations
12PubMed Central Citations
International Conference on Innovations in Bio-Inspired Computing and Applications
In spite of important technological developments in the med- ical eld and particularly in neuroscience one, epilepsy remained a serious pathology that could aect the human brain. In this work, we modeled a healthy and an epileptic cerebral activity in rest state. We used, the virtual brain TVB toolbox to simulate the two states based on FHN and epileptor model. We compared phase plane spaces, electrophysiological time series (electroencephalogram EEG, magnetoencephalogram MEG and intracerabral EEG), specter of eigenvalues transition matrix and to- pographic maps for healthy and epileptic rest state. There is a unique metastable state for healthy cerebral dynamics convergence which dis- appears in epileptic cerebral dynamics. Epileptic rest state time series depicts several transitory activities that vanish in the normal state. Nor- mal rest state topographic maps illustrate a limited dipolar activity; which is more extended in epileptic model. These prominent dierences
Biologically inspired cognitive architectures
To define the neural networks responsible of an epileptic seizure, it is useful to perform advanced signal processing techniques. In this context, electrophysiological signals present three types of waves: oscillations, spikes, and a mixture of both. Recent studies show that spikes and oscillations should be separated properly in order to define the accurate neural connectivity during the pre-ictal, seizure and inter-ictal states. Retrieving oscillatory activity is a sensitive task due to the frequency overlap between oscillations and transient activities. Advanced filtering techniques have been proposed to ensure a good separation between oscillations and spikes. It would be interesting to apply them in real time for instantaneous monitoring, seizure warning or neurofeedback systems. This requires improving execution time. This constraint can be overcome using embedded systems that combine hardware and software in an optimized architecture. We propose here to implement a stationary wavelet transform (SWT) as an adaptive filtering technique retaining only pre-ictal gamma oscillations, as validated in previous work, on a partial dynamic configuration. Then, the same architecture is used with further modifications to integrate spatio temporal mapping for an early recognition of seizure build-up. Data that contains transient, pre-ictal gamma oscillations and a seizure was simulated. the method on real intracerebral signals was also tested. The SWT was integrated on an embedded architecture. This architecture permits a spatio temporal mapping to detect the accurate time and localization of seizure buildup, while reducing computation time by a factor of around 40. Embedded systems are a promising venue for real-time applications in clinical systems for epilepsy.
Background: Electrophysiological signals (Electroencephalography : EEG, Magnetoencephalography : MEG and intracerebral EEG) have a major contribution in the diagnosis of epilepsy. Following this diagnosis, an operation can be proposed to the patient, which aims to surgically remove the zone responsible for the seizures. It is therefore crucial to clearly define this area. Within this framework, two types of markers can be used; transient activities (epileptic spikes) and oscillations. However, these activities are difficult to separate because there is a frequency overlap between them. Objective: Several strategies have already been proposed for separating these activities in an offline approach. However, it would be very interesting to have embedded systems, which could be used on real-time patient monitoring systems, either for early detection of seizures or for neurofeedback techniques. These filtering techniques can be expensive in computing time, which limits the current capabilities of embedded systems. The aim of our work is to propose a signal processing chain to properly separate the spikes and oscillations. Patients and Methods/Material and Methods: This chain uses the stationary wavelet transform (SWT) as a time-frequency filtering technique, followed by a thresholding step. We implemented this procedure on an embedded system, using an adaptive architecture based on dynamically partial reconfiguration. Results: We proved a better characterization of the networks involved in the oscillatory activity while strongly reducing the computation time. Conclusion: The embedded system are very useful in reducing the computation time for the definition of the neural networks involved in epileptic discharges.
Journal of Information Assurance & Security
The emergency of embedded systems puts new challenges for the design of different system in many fields. One of the embedded application's fields is the medical one. The major difficulty is to embed systems with reduced energy. Computational resources must be carefully used to execute complex application often in unpredictable environments. In this paper, the used application is the detection of evoked potential with variable latency and multiple trials using consensus matching pursuit algorithm. Fitting to the noisy Evoked Potential (EP) signal persistent in all response, we use the Consensus version of the matching pursuit algorithm (CMP). EP is a resulted wave from a stimulus. The EP can be explained with a good quality of energy ratio factor (QR). If we use a noisy EP, we cannot rebuild the original data because of the random atoms of CMP dictionary. We select the significant atoms to reconstruct EP signals. This application is embedded on a Xilinx ML 507. We used an adaptive architecture based on dynamically partial reconfiguration.
International Journal of Computer applications IJCA
International Journal of Computer Applications IJCA