Segmentation of depth-EEG seizure signals: method based on a physiological parameter and comparative study.
The analysis of stereoelectroencephalographic (intracerebral recording) signals provides information on the electrical activity of brain structures implied in epileptic seizures. A simple nonparametric adaptive segmentation method, based on a physiologically relevant parameter, is presented and compared with three methods reported in the literature. The comparative frame allows us to objectively test methods for their performances on the same basis. Results show that the proposed method is robust with respect to the types of change studied and easier to conduct, even if it is less accurate about the estimation of instants of change than another method presented in this study. Signals are segmented throughout the duration of seizures without parameter readjustment and generate instants of change in accordance with those interactively delimited by the clinician.
Wendling
F
F
Laboratoire Traitement du Signal et de L'Image, INSERM CJF 93-04, Université de Rennes 1, France.
Carrault
G
G
Badier
J M
JM
D000465:Algorithms; D000679:Amygdala / Q000503:physiopathology; D004569:Electroencephalography; D004833:Epilepsy, Temporal Lobe / Q000175:diagnosis* / Q000503:physiopathology; D006624:Hippocampus / Q000503:physiopathology; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes