Static and Dynamic Modeling of Absence Epileptic Seizures Using Depth Recordings
This research temporally explores absence epileptic seizures using depth cortical data recorded from different layers of the somatosensory cortex of Genetic Absence Epilepsy Rats from Strasbourg (GAERS). We characterize the recorded absence seizures by a linear combination of a few static and dynamic sources. Retrieving these sources from the recorded absence seizures is the main target of this study which helps us uncover the temporal evolution of absence seizures. The method used in this study provides an interesting and original solution to the classical data denoising consisting in removing the background activity and cleaning the data. The obtained results show the presence of a static source and a few specific dynamic sources during the recorded absence seizures. It is also shown that the sources have similar origins in different GAERS.
Abstract To combat against persistent sex offenders, recent laws call for the use of the GPS technology to monitor their movements. In [1] , it has been proposed the adoption of a Spatio-Temporal DataBase (S-T DB) for archiving this type of complex data, besides traditional ones (i.e., data about their home, (pending) crimes, and sensible areas). In the present paper we discuss investigative strategies that can be easily implemented on top of such a kind of database by taking advantage of the history of the movements of sex offenders. As implementation platform, the SECONDO DB Management System (DBMS) is adopted since it supports a data type about moving objects as well as a reach set of spatio-temporal operators to query such complex data [2] .