Khaled Ben Khalifa

Learn More
Using artificial neural networks for Electroencephalogram (EEG) signal interpretation is a very challenging tasks for several reasons. The first class of reasons refers to the nature of data. Such signals are complex and difficult to process. The second class of reasons refers to the nature of underlying knowledge. Expertise is manifold and difficult to(More)
Several recent works have used neural networks to discriminate vigilance states in humans from electroencephalo-graphic (EEG) signals. Our study aims at being more exhaustive. It takes into account various connectionist models , and it precisely studies their features and their performances. Physicians have been associated to the project, especially when(More)
The development of hardware platforms for artificial neural networks (ANN) has been hindered by the high consumption of power and hardware resources. In this paper, we present a methodology for ANN-optimized implementation, of a learning vector quantization (LVQ) type on a field-programmable gate array (FPGA) device. The aim was to provide an intelligent(More)
The Multiple-Wordlength Operation Grouping (MWOG) is a recently used approach for an optimized implementation on a Field Programmable Gate Array (FPGA). By fixing the precision constraint, this approach allows minimizing the data wordlength. In this paper, the authors present the integration of the approach based on the MWOG in the Algorithm Architecture(More)
PURPOSE To evaluate the incidence of newly presenting seizures in children in the area of Sousse, Tunisia. METHODS From June 1, 1998, to May 31, 1999, all children aged 1 month to 15 years with first provoked and unprovoked seizures were included. Children with febrile seizures were excluded. All suspected cases were systematically referred to the(More)
  • 1