Title :
Reference-Based Source Separation Method For Identification of Brain Regions Involved in a Reference State From Intracerebral EEG
Author :
Samadi, Sadegh ; Amini, L. ; Cosandier-Rimele, D. ; Soltanian-Zadeh, Hamid ; Jutten, Christian
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Tehran, Tehran, Iran
Abstract :
In this paper, we present a fast method to extract the sources related to interictal epileptiform state. The method is based on general eigenvalue decomposition using two correlation matrices during: 1) periods including interictal epileptiform discharges (IED) as a reference activation model and 2) periods excluding IEDs or abnormal physiological signals as background activity. After extracting the most similar sources to the reference or IED state, IED regions are estimated by using multiobjective optimization. The method is evaluated using both realistic simulated data and actual intracerebral electroencephalography recordings of patients suffering from focal epilepsy. These patients are seizure-free after the resective surgery. Quantitative comparisons of the proposed IED regions with the visually inspected ictal onset zones by the epileptologist and another method of identification of IED regions reveal good performance.
Keywords :
eigenvalues and eigenfunctions; electroencephalography; feature extraction; medical disorders; medical signal processing; neurophysiology; optimisation; source separation; surgery; IED region identification; IED state; abnormal physiological signal; background activity; brain region identification; correlation matrices; focal epilepsy; general eigenvalue decomposition; interictal epileptiform discharges; interictal epileptiform state; intracerebral EEG; intracerebral electroencephalography recording; multiobjective optimization; reference activation model; reference state; reference-based source separation method; resective surgery; source extraction; visually inspected ictal onset zones; Brain modeling; Correlation; Eigenvalues and eigenfunctions; Electrodes; Electroencephalography; Optimization; Source separation; Epilepsy; general eigenvalue decomposition (GEVD); intracerebral electroencephalography (iEEG); multiobjective optimization; source separation; Algorithms; Brain; Brain Mapping; Computer Simulation; Diagnosis, Computer-Assisted; Electroencephalography; Epilepsy; Humans; Models, Neurological; Nerve Net; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity;
Journal_Title :
Biomedical Engineering, IEEE Transactions on
DOI :
10.1109/TBME.2013.2247401