Classification of epileptic motor manifestations using inertial and magnetic sensors.
In order to characterize objectively the succession of movements observed during motor seizures, inertial and magnetic sensors were placed on epileptic patients. Video recordings synchronized with motion recordings were analyzed visually during seizures and divided, for each limb, into events corresponding to different classes of motor manifestations. For each classified event, features were extracted and a subset selection was automated using artificial neural networks. The best artificial neural network was simulated on whole recordings to generate a stereotypic evolution of motor manifestations that we called motorograms. It is shown that motorograms can point out seizure movements and emphasize epileptic patterns.
Grenoble Institut des Neurosciences, Inserm U 836-UJF-CEA-CHU, University Hospital Center of Grenoble, BP 217, 38043 Grenoble cedex 9, France. guillaume.becq@gipsa-lab.grenoble-inp.fr
Guillaume
G
Becq
Stéphane
S
Bonnet
Lorella
L
Minotti
Michel
M
Antonakios
Régis
R
Guillemaud
Philippe
P
Kahane
D000054:Acceleration; D003198:Computer Simulation; D004574:Electromagnetic Fields; D004827:Epilepsy / Q000503:physiopathology*; D006801:Humans; D008991:Monitoring, Physiologic / Q000379:methods*; D016571:Neural Networks, Computer; D012640:Seizures / Q000145:classification / Q000503:physiopathology*; D012815:Signal Processing, Computer-Assisted; D014741:Video Recording / Q000379:methods*