Abstract
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.
The data used in this study were acquired at Grenoble institute of Neurosciences (GIN) in the team Synchornization and Modulation of Neural Networks in Epilepsy (SyMoNNE) supervised by Dr. A. Depaulis. Also, this work has been partly supported by the European project 2012-ERC-AdG-320684 CHESS.
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Akhavan, S., Phlypo, R., Soltanian-Zadeh, H., Kamarei, M., Jutten, C. (2018). Static and Dynamic Modeling of Absence Epileptic Seizures Using Depth Recordings. In: Deville, Y., Gannot, S., Mason, R., Plumbley, M., Ward, D. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2018. Lecture Notes in Computer Science(), vol 10891. Springer, Cham. https://doi.org/10.1007/978-3-319-93764-9_49
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DOI: https://doi.org/10.1007/978-3-319-93764-9_49
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