Abstract
Electroencephalography (EEG) occupies an important place for studying human brain activity in general, and epileptic processes in particular, with appropriate time resolution. Scalp EEG or intracerebral EEG signals recorded in patients with drug-resistant partial epilepsy convey important information about epileptogenic networks that must be localized and understood prior to subsequent therapeutic procedures. However, this information, often subtle, is ‘hidden’ in the signals. It is precisely the role of signal processing to extract this information and to put it into a ‘coherent and interpretable picture’ that can participate in the therapeutic strategy. Nowadays, the panel of available methods is very wide depending on the objectives such as, for instance, the detection of transient epileptiform events, the detection and/or prediction of seizures, the recognition and/or the classification of EEG patterns, the localization of epileptic neuronal sources, the characterization of neural synchrony, the determination of functional connectivity, among others. The intent of this paper is to focus on a specific category of methods providing relevant information about epileptogenic networks from the analysis of spatial properties of EEG signals in the time and frequency domain. These methods apply to either interictal or ictal recordings and share the common objective of localizing the subsets of brain structures involved in both types of paroxysmal activity. Most of these methods were developed by our group and are routinely used during pre-surgical evaluation. Examples are detailed. Results, as well as limitations of the methods, are also discussed.
Highlights
Epilepsy is a neurological disease that directly affects 50 million people worldwide (Prilipko et al, 2006)
During the pre-surgical evaluation of patients with drug-resistant partial epilepsy, a number of clinical investigations providing both anatomical and functional data are performed in order to determine the organization of the epileptogenic zone (EZ) which often corresponds to a network of structurally and functionally connected brain structures (Bartolomei et al, 2001; Bragin et al, 2000; Briellmann et al, 2004)
(iii) Results and discussion we report the main findings obtained from the application of the method in fifteen patients with intractable partial epilepsy of temporal origin
Summary
Epilepsy is a neurological disease that directly affects 50 million people worldwide (Prilipko et al, 2006). During the pre-surgical evaluation of patients with drug-resistant partial epilepsy, a number of clinical investigations providing both anatomical (presence of a lesion, for instance) and functional (presence of abnormalities in brain electrical or metabolic activity, for instance) data are performed in order to determine the organization of the EZ which often corresponds to a network of structurally and functionally connected brain structures (Bartolomei et al, 2001; Bragin et al, 2000; Briellmann et al, 2004) Among these investigations, electroencephalography (scalp EEG) and stereoelectroencephalography (intracerebral EEG) consist in the measurement of brain electrical activity using electrodes positioned either on the head or directly implanted into brain structures respectively, potentially involved into the generation of epileptic events, either during ictal periods (seizures) and during interictal periods (outside seizures). We did not report results about linear methods (based on multivariate autoregressive model) proposed to estimate causality (in Granger sense) between signals like the directed coherence method (DCOH) or the partial directed coherence (PDC), among others (see (Gourevitch et al, 2006) for review on potential applications in neurophysiology)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
