Name
Abstract | ||
|---|---|---|
The partial epileptic seizures are often considered to be caused by a wrong balance between inhibitory and excitatory interneuron connections within a focal brain area. These abnormal balances are likely to result in loss of functional connectivities between remote brain structures, while functional connectivities within the incriminated zone are enhanced. The identification of the epileptic networks underlying these hypersynchronies are expected to contribute to a better understanding of the brain mechanisms responsible for the development of the seizures. In this objective, threshold strategies are commonly applied, based on synchrony measurements computed from recordings of the electrophysiologic brain activity. However, such methods are reported to be prone to errors and false alarms. In this paper, we propose a hidden Markov chain modeling of the synchrony states with the aim to develop a reliable machine learning methods for epileptic network inference. The method is applied on a real Stereo-EEG recording, demonstrating consistent results with the clinical evaluations and with the current knowledge on temporal lobe epilepsy. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/EMBC.2013.6610510 | EMBC |
Keywords | Field | DocType |
synchrony measurement,seizure development,stereo-eeg,partial epileptic seizure,medical disorders,temporal lobe epilepsy,inhibitory interneuron connection,machine learning method,medical signal detection,neurophysiology,learning (artificial intelligence),electroencephalography,incriminated zone,bayesian approach,electrophysiologic brain activity recording,real stereo-eeg recording,synchrony state,functional connectivities,epileptic network inference,hidden markov chain modeling,bioelectric phenomena,brain mechanism,focal brain area,hidden markov chain,hypersynchrony,excitatory interneuron connection,network inference,remote brain structures,hidden markov models,threshold strategies,epileptic network identification,learning artificial intelligence,stereo eeg,hippocampus | Neuroscience,Neurophysiology,Computer science,Inference,Epilepsy,Brain activity and meditation,Hidden Markov model,Interneuron,Electroencephalography,Temporal lobe | Conference |
Volume | ISSN | Citations |
2013 | 1557-170X | 0 |
PageRank | References | Authors |
0.34 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Steven Le Cam | 1 | 4 | 3.50 |
| Valérie Louis-Dorr | 2 | 70 | 9.35 |
| Louis Maillard | 3 | 97 | 10.38 |