177 research outputs found
Clustering Approach to Quantify Long-Term Spatio-Temporal Interactions in Epileptic Intracranial Electroencephalography
Abnormal dynamical coupling between brain structures is believed to be primarily
responsible for the generation of epileptic seizures and their propagation. In this study, we
attempt to identify the spatio-temporal interactions of an epileptic brain using a previously
proposed nonlinear dependency measure. Using a clustering model, we determine the average
spatial mappings in an epileptic brain at different stages of a complex partial seizure. Results
involving 8 seizures from 2 epileptic patients suggest that there may be a fixed pattern associated
with regional spatio-temporal dynamics during the interictal to pre-post-ictal transition
Towards Accurate Forecasting of Epileptic Seizures: Artificial Intelligence and Effective Connectivity Findings
LâĂ©pilepsie est une des maladies neurologiques les plus frĂ©quentes, touchant prĂšs dâun
pourcent de la population mondiale. De nos jours, bien quâenviron deux tiers des patients
Ă©pileptiques rĂ©pondent adĂ©quatement aux traitements pharmacologiques, il reste quâun tiers des
patients doivent vivre avec des crises invalidantes et imprévisibles. Quoique la chirurgie
dâĂ©pilepsie puisse ĂȘtre une autre option thĂ©rapeutique envisageable, le recours Ă la chirurgie de
résection demeure trÚs faible en partie pour des raisons diverses (taux de réussite modeste, peur
des complications, perceptions nĂ©gatives). Dâautres avenues de traitement sont donc souhaitables.
Une piste actuellement explorĂ©e par des groupes de chercheurs est de tenter de prĂ©dire les crises Ă
partir dâenregistrements de lâactivitĂ© cĂ©rĂ©brale des patients. La capacitĂ© de prĂ©dire la survenue de
crises permettrait notamment aux patients, aidants naturels ou personnels médical de prendre des
mesures de prĂ©caution pour Ă©viter les dĂ©sagrĂ©ments reliĂ©s aux crises voire mĂȘme instaurer un
traitement pour les faire avorter. Au cours des derniĂšres annĂ©es, dâimportants efforts ont Ă©tĂ©
dĂ©ployĂ©s pour dĂ©velopper des algorithmes de prĂ©diction de crises et dâen amĂ©liorer les
performances.
Toutefois, le manque dâenregistrements Ă©lectroencĂ©phalographiques intracrĂąniens (iEEG) de
longue durée de qualité, la quantité limitée de crises, ainsi que la courte durée des périodes
interictales constituaient des obstacles majeurs à une évaluation adéquate de la performance des
algorithmes de prĂ©diction de crises. RĂ©cemment, la disponibilitĂ© en ligne dâenregistrements iEEG
continus avec échantillonnage bilatéral (des deux hémisphÚres) acquis chez des chiens atteints
dâĂ©pilepsie focale Ă lâaide du dispositif de surveillance ambulatoire implantable NeuroVista a
partiellement facilitĂ© cette tĂąche. Cependant, une des limitations associĂ©es Ă lâutilisation de ces
donnĂ©es durant la conception dâun algorithme de prĂ©diction de crises Ă©tait lâabsence
dâinformation concernant la zone exacte de dĂ©but des crises (information non fournie par les
gestionnaires de cette base de données en ligne). Le premier objectif de cette thÚse était la mise
en oeuvre dâun algorithme prĂ©cis de prĂ©diction de crises basĂ© sur des enregistrements iEEG canins
de longue durée. Les principales contributions à cet égard incluent une localisation quantitative
de la zone dâapparition des crises (basĂ©e sur la fonction de transfert dirigĂ© âDTF), lâutilisation
dâune nouvelle fonction de coĂ»t via lâalgorithme gĂ©nĂ©tique proposĂ©, ainsi quâune Ă©valuation
quasi-prospective des performances de prĂ©diction (donnĂ©es de test dâun total de 893 jours). Les rĂ©sultats ont montrĂ© une amĂ©lioration des performances de prĂ©diction par rapport aux Ă©tudes
antérieures, atteignant une sensibilité moyenne de 84.82 % et un temps en avertissement de 10 %.
La DTF, utilisée précédemment comme mesure de connectivité pour déterminer le réseau
épileptique (objectif 1), a été préalablement validée pour quantifier les relations causales entre les
canaux lorsque les exigences de quasi-stationnarité sont satisfaites. Ceci est possible dans le cas
des enregistrements canins en raison du nombre relativement faible de canaux. Pour faire face
aux exigences de non-stationnarité, la fonction de transfert adaptatif pondérée par le spectre
(Spectrum weighted adaptive directed transfer function - swADTF) a Ă©tĂ© introduit en tant quâune
version variant dans le temps de la DTF. Le second objectif de cette thĂšse Ă©tait de valider la
possibilitĂ© dâidentifier les endroits Ă©metteurs (ou sources) et rĂ©cepteurs dâactivitĂ© Ă©pileptiques en
appliquant la swADTF sur des enregistrements iEEG de haute densité provenant de patients
admis pour Ă©valuation prĂ©-chirurgicale au CHUM. Les gĂ©nĂ©rateurs dâactivitĂ© Ă©pileptique Ă©taient
dans le volume réséqué pour les patients ayant des bons résultats post-chirurgicaux alors que
différents foyers ont été identifiés chez les patients ayant eu de mauvais résultats postchirurgicaux.
Ces rĂ©sultats dĂ©montrent la possibilitĂ© dâune identification prĂ©cise des sources et
rĂ©cepteurs dâactivitĂ©s Ă©pileptiques au moyen de la swADTF ouvrant la porte Ă la possibilitĂ© dâune
meilleure sĂ©lection dâĂ©lectrodes de maniĂšre quantitative dans un contexte de dĂ©veloppement
dâalgorithme de prĂ©diction de crises chez lâhumain.
Dans le but dâexplorer de nouvelles avenues pour la prĂ©diction de crises Ă©pileptiques, un
nouveau prĂ©curseur a aussi Ă©tĂ© Ă©tudiĂ© combinant lâanalyse des spectres dâordre supĂ©rieur et les
réseaux de neurones artificiels (objectif 3). Les résultats ont montré des différences
statistiquement significatives (p<0.05) entre lâĂ©tat prĂ©ictal et lâĂ©tat interictal en utilisant chacune
des caractéristiques extraites du bi-spectre. Utilisées comme entrées à un perceptron multicouche,
lâentropie bispectrale normalisĂ©e, lâentropie carrĂ© normalisĂ©e, et la moyenne ont atteint des
précisions respectives de 78.11 %, 72.64% et 73.26%.
Les résultats de cette thÚse confirment la faisabilité de prédiction de crises à partir
dâenregistrements dâĂ©lectroencĂ©phalographie intracrĂąniens. Cependant, des efforts
supplĂ©mentaires en termes de sĂ©lection dâĂ©lectrodes, dâextraction de caractĂ©ristiques, dâutilisation
des techniques dâapprentissage profond et dâimplĂ©mentation Hardware, sont nĂ©cessaires avant
lâintĂ©gration de ces approches dans les dispositifs implantables commerciaux.----------ABSTRACT
Epilepsy is a chronic condition characterized by recurrent âunpredictableâ seizures. While
the first line of treatment consists of long-term drug therapy about one-third of patients are said to
be pharmacoresistant. In addition, recourse to epilepsy surgery remains low in part due to
persisting negative attitudes towards resective surgery, fear of complications and only moderate
success rates. An important direction of research is to investigate the possibility of predicting
seizures which, if achieved, can lead to novel interventional avenues.
The paucity of intracranial electroencephalography (iEEG) recordings, the limited number of
ictal events, and the short duration of interictal periods have been important obstacles for an
adequate assessment of seizure forecasting. More recently, long-term continuous bilateral iEEG
recordings acquired from dogs with naturally occurring focal epilepsy, using the implantable
NeuroVista ambulatory monitoring device have been made available on line for the benefit of
researchers. Still, an important limitation of these recordings for seizure-prediction studies was
that the seizure onset zone was not disclosed/available. The first objective of this thesis was to
develop an accurate seizure forecasting algorithm based on these canine ambulatory iEEG
recordings. Main contributions include a quantitative, directed transfer function (DTF)-based,
localization of the seizure onset zone (electrode selection), a new fitness function for the
proposed genetic algorithm (feature selection), and a quasi-prospective assessment of seizure
forecasting on long-term continuous iEEG recordings (total of 893 testing days). Results showed
performance improvement compared to previous studies, achieving an average sensitivity of
84.82% and a time in warning of 10 %.
The DTF has been previously validated for quantifying causal relations when quasistationarity
requirements are met. Although such requirements can be fulfilled in the case of
canine recordings due to the relatively low number of channels (objective 1), the identification of
stationary segments would be more challenging in the case of high density iEEG recordings. To
cope with non-stationarity issues, the spectrum weighted adaptive directed transfer function
(swADTF) was recently introduced as a time-varying version of the DTF. The second objective
of this thesis was to validate the feasibility of identifying sources and sinks of seizure activity
based on the swADTF using high-density iEEG recordings of patients admitted for pre-surgical monitoring at the CHUM. Generators of seizure activity were within the resected volume for
patients with good post-surgical outcomes, whereas different or additional seizure foci were
identified in patients with poor post-surgical outcomes. Results confirmed the possibility of
accurate identification of seizure origin and propagation by means of swADTF paving the way
for its use in seizure prediction algorithms by allowing a more tailored electrode selection.
Finally, in an attempt to explore new avenues for seizure forecasting, we proposed a new
precursor of seizure activity by combining higher order spectral analysis and artificial neural
networks (objective 3). Results showed statistically significant differences (p<0.05) between
preictal and interictal states using all the bispectrum-extracted features. Normalized bispectral
entropy, normalized squared entropy and mean of magnitude, when employed as inputs to a
multi-layer perceptron classifier, achieved held-out test accuracies of 78.11%, 72.64%, and
73.26%, respectively.
Results of this thesis confirm the feasibility of seizure forecasting based on iEEG recordings;
the transition into the ictal state is not random and consists of a âbuild-upâ, leading to seizures.
However, additional efforts in terms of electrode selection, feature extraction, hardware and deep
learning implementation, are required before the translation of current approaches into
commercial devices
Quantitative Multimodal Mapping Of Seizure Networks In Drug-Resistant Epilepsy
Over 15 million people worldwide suffer from localization-related drug-resistant epilepsy. These patients are candidates for targeted surgical therapies such as surgical resection, laser thermal ablation, and neurostimulation. While seizure localization is needed prior to surgical intervention, this process is challenging, invasive, and often inconclusive. In this work, I aim to exploit the power of multimodal high-resolution imaging and intracranial electroencephalography (iEEG) data to map seizure networks in drug-resistant epilepsy patients, with a focus on minimizing invasiveness. Given compelling evidence that epilepsy is a disease of distorted brain networks as opposed to well-defined focal lesions, I employ a graph-theoretical approach to map structural and functional brain networks and identify putative targets for removal. The first section focuses on mesial temporal lobe epilepsy (TLE), the most common type of localization-related epilepsy. Using high-resolution structural and functional 7T MRI, I demonstrate that noninvasive neuroimaging-based network properties within the medial temporal lobe can serve as useful biomarkers for TLE cases in which conventional imaging and volumetric analysis are insufficient. The second section expands to all forms of localization-related epilepsy. Using iEEG recordings, I provide a framework for the utility of interictal network synchrony in identifying candidate resection zones, with the goal of reducing the need for prolonged invasive implants. In the third section, I generate a pipeline for integrated analysis of iEEG and MRI networks, paving the way for future large-scale studies that can effectively harness synergy between different modalities. This multimodal approach has the potential to provide fundamental insights into the pathology of an epileptic brain, robustly identify areas of seizure onset and spread, and ultimately inform clinical decision making
A Fuzzy Logic System for Seizure Onset Detection in Intracranial EEG
We present a multistage fuzzy rule-based algorithm for epileptic seizure onset detection. Amplitude, frequency, and entropy-based features were extracted from intracranial electroencephalogram (iEEG) recordings and considered as the inputs for a fuzzy system. These features extracted from multichannel iEEG signals were combined using fuzzy algorithms both in feature domain and in spatial domain. Fuzzy rules were derived based on experts' knowledge and reasoning. An adaptive fuzzy subsystem was used for combining characteristics features extracted from iEEG. For the spatial combination, three channels from epileptogenic zone and one from remote zone were considered into another fuzzy subsystem. Finally, a threshold procedure was applied to the fuzzy output derived from the final fuzzy subsystem. The method was evaluated on iEEG datasets selected from Freiburg Seizure Prediction EEG (FSPEEG) database. A total of 112.45 hours of intracranial EEG recordings was selected from 20 patients having 56 seizures was used for the system performance evaluation. The overall sensitivity of 95.8% with false detection rate of 0.26 per hour and average detection latency of 15.8 seconds was achieved
On the Dynamics of Epileptic Spikes and Focus Localization in Temporal Lobe Epilepsy
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/ThesisPh.D. Electrical Engineering 201
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