Epilepsy is a neurological disease causing seizures in its victims and affects approximately 50 million people worldwide. Successful treatment is dependent upon correct identification of the origin of the seizures within the brain. To achieve this, electroencephalograms (EEGs) are used to measure a patient’s brainwaves. This EEG data must be manually analyzed to identify interictal spikes that emanate from the afflicted region of the brain. This process can take a neurologist more than a week and a half per patient. This thesis presents a method to extract and process the interictal spikes in a patient, and use them to reduce the amount of data for a neurologist to manually analyze. The effectiveness of multiple neural network implementations is compared, and a data reduction of 3-4 orders of magnitude, or upwards of 99%, is achieved.

abstract: Interictal spikes, together with seizures, have been recognized as the two hallmarks of epilepsy, a brain disorder that 1% of the world's population suffers from. Even though the presence of spikes in brain's electromagnetic activity has diagnostic value, their dynamics are still elusive. It was an objective of this dissertation to formulate a mathematical framework within which the dynamics of interictal spikes could be thoroughly investigated. A new epileptic spike detection algorithm was developed by employing data adaptive morphological filters. The performance of the spike detection algorithm was favorably compared with others in the literature. A novel spike spatial synchronization measure was developed and tested on coupled spiking neuron models. Application of this measure to individual epileptic spikes in EEG from patients with temporal lobe epilepsy revealed long-term trends of increase in synchronization between pairs of brain sites before seizures and desynchronization after seizures, in the same patient as well as across patients, thus supporting the hypothesis that seizures may occur to break (reset) the abnormal spike synchronization in the brain network. Furthermore, based on these results, a separate spatial analysis of spike rates was conducted that shed light onto conflicting results in the literature about variability of spike rate before and after seizure. The ability to automatically classify seizures into clinical and subclinical was a result of the above findings. A novel method for epileptogenic focus localization from interictal periods based on spike occurrences was also devised, combining concepts from graph theory, like eigenvector centrality, and the developed spike synchronization measure, and tested very favorably against the utilized gold rule in clinical practice for focus localization from seizures onset. Finally, in another application of resetting of brain dynamics at seizures, it was shown that it is possible to differentiate with a high accuracy between patients with epileptic seizures (ES) and patients with psychogenic nonepileptic seizures (PNES). The above studies of spike dynamics have elucidated many unknown aspects of ictogenesis and it is expected to significantly contribute to further understanding of the basic mechanisms that lead to seizures, the diagnosis and treatment of epilepsy. / Dissertation/Thesis / Ph.D. Electrical Engineering 2012

Plusieurs études de simulation in vitro et in vivo ont été réalisées au cours des dernières décennies afin de clarifier les interrelations des sources corticales avec leurs corrélats électrophysiologiques enregistrés sur l’EEG invasif et l’EEG de scalp. L’amplitude des potentiels corticaux, l’étendue de l’aire corticale impliquée par la décharge, de même que la localisation et la géométrie de la source corticale sont des facteurs indépendants qui modulent l’observabilité et la contribution de ces sources sur l’EEG de surface. L’enregistrement simultané et multi-échelle de l’EEG de scalp et intra-crânien (avec des électrodes sous-durales ou profondes) durant l’exploration pré-chirurgicale des patients épileptiques offre une opportunité unique d’explorer cette question fondamentale. Alors que les études précédentes ont considéré essentiellement des sources néocorticales dans le contexte de l’épilepsie du lobe temporal, notre travail s’est intéressé à l’observabilité et la contribution de sources profondes temporales et frontales. Nous avons pu montrer : (1) que les sources épileptiques profondes enregistrées dans les régions temporales médianes et fronto-basales ne sont pas visibles par l’analyse visuelle de routine mais sont détectables après élimination du bruit de fond physiologique généré par les sources corticales de surface sus-jacentes ; (3) que l’amplitude des pointes enregistrées en surface est corrélée avec la surface d’activation corticale de la convexité et avec des ratios élevés d’amplitude pointes/activité de fond / Several in vitro, in vivo, and simulation studies have been performed in the past decades aiming to clarify the interrelations of cortical sources with their scalp and invasive EEG correlates. The amplitude ratio of cortical potentials to their scalp EEG correlates, the extent of the cortical area involved in the discharge, as well as the localization of the cortical source and its geometry, have been each independently linked to the recording of the cortical discharge with scalp electrodes. Simultaneous multiscale EEG recordings with scalp, subdural and depth electrodes, applied in presurgical epilepsy workup, offer an excellent opportunity to address this fundamental issue. Whereas past studies have considered predominantly neocortical sources in the context of temporal lobe epilepsy, the present work addresses deep sources, in mesial temporal and extra-temporal epilepsies. We showed that deep sources, such as those in mesial temporal or fronto-basal regions, are not visible, but are detectable in scalp EEG. Scalp EEG spikes correlate with extensive activation of the cortical convexity and high spike-to-background amplitude ratios