We computed network connectivity of each technique. Then, we applied four methods of inverse problem to define cortical areas and neural generators of excessive discharges. We validated our technique on MEG signal using detector precision on 5 patients. We evaluated our technique’s robustness in separation between transitory and ripples versus frequency range, transitory shapes, and signal to noise ratio on simulated data (depicting both epileptic biomarkers and respecting time series and spectral properties of realistic data). First, we applied an advanced technique based on Singular Value Decomposition (SVD) to recover only pure transitory activities (interictal epileptiform discharges). Here, we proposed to evaluate performances of distributed inverse problem in defining EZ. These techniques present different assumptions and particular epileptic network connectivity. Locating efficiently these abnormal sources among magnetoencephalography (MEG) biomarker is obtained by several inverse problem techniques. Characterizing epileptogenic zones EZ (sources responsible of excessive discharges) would assist a neurologist during epilepsy diagnosis.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |