89 research outputs found
Detection, Prediction and Control of Epileptic Seizures
abstract: From time immemorial, epilepsy has persisted to be one of the greatest impediments to human life for those stricken by it. As the fourth most common neurological disorder, epilepsy causes paroxysmal electrical discharges in the brain that manifest as seizures. Seizures have the effect of debilitating patients on a physical and psychological level. Although not lethal by themselves, they can bring about total disruption in consciousness which can, in hazardous conditions, lead to fatality. Roughly 1\% of the world population suffer from epilepsy and another 30 to 50 new cases per 100,000 increase the number of affected annually. Controlling seizures in epileptic patients has therefore become a great medical and, in recent years, engineering challenge.
In this study, the conditions of human seizures are recreated in an animal model of temporal lobe epilepsy. The rodents used in this study are chemically induced to become chronically epileptic. Their Electroencephalogram (EEG) data is then recorded and analyzed to detect and predict seizures; with the ultimate goal being the control and complete suppression of seizures.
Two methods, the maximum Lyapunov exponent and the Generalized Partial Directed Coherence (GPDC), are applied on EEG data to extract meaningful information. Their effectiveness have been reported in the literature for the purpose of prediction of seizures and seizure focus localization. This study integrates these measures, through some modifications, to robustly detect seizures and separately find precursors to them and in consequence provide stimulation to the epileptic brain of rats in order to suppress seizures. Additionally open-loop stimulation with biphasic currents of various pairs of sites in differing lengths of time have helped us create control efficacy maps. While GPDC tells us about the possible location of the focus, control efficacy maps tells us how effective stimulating a certain pair of sites will be.
The results from computations performed on the data are presented and the feasibility of the control problem is discussed. The results show a new reliable means of seizure detection even in the presence of artifacts in the data. The seizure precursors provide a means of prediction, in the order of tens of minutes, prior to seizures. Closed loop stimulation experiments based on these precursors and control efficacy maps on the epileptic animals show a maximum reduction of seizure frequency by 24.26\% in one animal and reduction of length of seizures by 51.77\% in another. Thus, through this study it was shown that the implementation of the methods can ameliorate seizures in an epileptic patient. It is expected that the new knowledge and experimental techniques will provide a guide for future research in an effort to ultimately eliminate seizures in epileptic patients.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201
Classification of Epileptic and Non-Epileptic Electroencephalogram (EEG) Signals Using Fractal Analysis and Support Vector Regression
Seizures are a common symptom of this neurological condition, which is caused by the discharge of brain nerve cells at an excessively fast rate. Chaos, nonlinearity, and other nonlinearities are common features of scalp and intracranial Electroencephalogram (EEG) data recorded in clinics. EEG signals that aren't immediately evident are challenging to categories because of their complexity. The Gradient Boost Decision Tree (GBDT) classifier was used to classify the majority of the EEG signal segments automatically. According to this study, the Hurst exponent, in combination with AFA, is an efficient way to identify epileptic signals. As with any fractal analysis approach, there are problems and factors to keep in mind, such as identifying whether or not linear scaling areas are present. These signals were classified as either epileptic or non-epileptic by using a combination of GBDT and a Support Vector Regression (SVR). The combined method's identification accuracy was 98.23%. This study sheds light on the effectiveness of AFA feature extraction and GBDT classifiers in EEG classification. The findings can be utilized to develop theoretical guidance for the clinical identification and prediction of epileptic EEG signals. Doi: 10.28991/ESJ-2022-06-01-011 Full Text: PD
Topological classifier for detecting the emergence of epileptic seizures
Objective
An innovative method based on topological data analysis is introduced for classifying EEG recordings of patients affected by epilepsy. We construct a topological space from a collection of EEGs signals using Persistent Homology; then, we analyse the space by Persistent entropy, a global topological feature, in order to classify healthy and epileptic signals.
Results
The performance of the resulting one-feature-based linear topological classifier is tested by analysing the Physionet dataset. The quality of classification is evaluated in terms of the Area Under Curve (AUC) of the receiver operating characteristic curve. It is shown that the linear topological classifier has an AUC equal to 97.2% while the performance of a classifier based on Sample Entropy has an AUC equal to 62.0%
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
Robust physiological mappings: from non-invasive to invasive
The goal of this paper is to highlight the challenges on the three methods of data analysis, namely: robust, component, and dynamical analysis with respect to the epilepsey. A forward and inverse mapping model for the human brain is presented. Research directions for obtaining robust inverse mapping, and conducting dynamical analysis of the epileptic brain are discussed.Проаналізовано проблеми, пов’язані з трьома методами аналізу даних щодо епілепсії головного мозку: робастним, покомпонентним і динамічним. Запропоновано пряму і обернену моделі відображення головного мозку. Також обговорюються напрями досліджень для отримання робастних обернених відображень і проведення динамічного аналізу епілептичного мозк
Towards Accurate Forecasting of Epileptic Seizures: Artificial Intelligence and Effective Connectivity Findings
L’épilepsie est une des maladies neurologiques les plus fréquentes, touchant près d’un
pourcent de la population mondiale. De nos jours, bien qu’environ deux tiers des patients
épileptiques répondent adéquatement aux traitements pharmacologiques, il reste qu’un tiers des
patients doivent vivre avec des crises invalidantes et imprévisibles. Quoique la chirurgie
d’épilepsie puisse être une autre option thérapeutique envisageable, le recours à la chirurgie de
résection demeure très faible en partie pour des raisons diverses (taux de réussite modeste, peur
des complications, perceptions négatives). D’autres avenues de traitement sont donc souhaitables.
Une piste actuellement explorée par des groupes de chercheurs est de tenter de prédire les crises à
partir d’enregistrements de l’activité cérébrale des patients. La capacité de prédire la survenue de
crises permettrait notamment aux patients, aidants naturels ou personnels médical de prendre des
mesures de précaution pour éviter les désagréments reliés aux crises voire même instaurer un
traitement pour les faire avorter. Au cours des dernières années, d’importants efforts ont été
déployés pour développer des algorithmes de prédiction de crises et d’en améliorer les
performances.
Toutefois, le manque d’enregistrements électroencéphalographiques intracrâniens (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 prédiction de crises. Récemment, la disponibilité en ligne d’enregistrements iEEG
continus avec échantillonnage bilatéral (des deux hémisphères) acquis chez des chiens atteints
d’épilepsie focale à l’aide du dispositif de surveillance ambulatoire implantable NeuroVista a
partiellement facilité cette tâche. Cependant, une des limitations associées à l’utilisation de ces
données durant la conception d’un algorithme de prédiction de crises était l’absence
d’information concernant la zone exacte de début des crises (information non fournie par les
gestionnaires de cette base de données en ligne). Le premier objectif de cette thèse était la mise
en oeuvre d’un algorithme précis de prédiction de crises basé sur des enregistrements iEEG canins
de longue durée. Les principales contributions à cet égard incluent une localisation quantitative
de la zone d’apparition des crises (basée sur la fonction de transfert dirigé –DTF), l’utilisation
d’une nouvelle fonction de coût via l’algorithme génétique proposé, ainsi qu’une évaluation
quasi-prospective des performances de prédiction (données de test d’un total de 893 jours). Les résultats ont montré une amélioration des performances de prédiction par rapport aux études
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 été introduit en tant qu’une
version variant dans le temps de la DTF. Le second objectif de cette thèse était de valider la
possibilité d’identifier les endroits émetteurs (ou sources) et récepteurs d’activité épileptiques en
appliquant la swADTF sur des enregistrements iEEG de haute densité provenant de patients
admis pour évaluation pré-chirurgicale au CHUM. Les générateurs d’activité épileptique étaient
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 résultats démontrent la possibilité d’une identification précise des sources et
récepteurs d’activités épileptiques au moyen de la swADTF ouvrant la porte à la possibilité d’une
meilleure sélection d’électrodes de manière quantitative dans un contexte de développement
d’algorithme de prédiction de crises chez l’humain.
Dans le but d’explorer de nouvelles avenues pour la prédiction de crises épileptiques, un
nouveau précurseur a aussi été étudié combinant l’analyse des spectres d’ordre supérieur et les
réseaux de neurones artificiels (objectif 3). Les résultats ont montré des différences
statistiquement significatives (p<0.05) entre l’état préictal et l’état interictal en utilisant chacune
des caractéristiques extraites du bi-spectre. Utilisées comme entrées à un perceptron multicouche,
l’entropie bispectrale normalisée, l’entropie carré normalisée, et la moyenne ont atteint des
précisions respectives de 78.11 %, 72.64% et 73.26%.
Les résultats de cette thèse confirment la faisabilité de prédiction de crises à partir
d’enregistrements d’électroencéphalographie intracrâniens. Cependant, des efforts
supplémentaires en termes de sélection d’électrodes, d’extraction de caractéristiques, d’utilisation
des techniques d’apprentissage profond et d’implémentation Hardware, sont nécessaires avant
l’intégration de ces approches dans les dispositifs implantables commerciaux.----------ABSTRACT
Epilepsy is a chronic condition characterized by recurrent “unpredictable” 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 “build-up”, 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
Predicting epileptic seizures using nonlinear dynamics
Epilepsy is a nervous system disorder which affects approximately 1% of the world's population. Nearly 25% of people who have epilepsy are resistant to traditional treatments such as medication and are not candidates for surgery [32]. A new form of treatment has emerged that attempts to disrupt epileptic activity
in the brain by electrically stimulating neural tissue. However, the nature of this treatment requires that it is able to accurately predict the onset of a seizure in order to time the intervention correctly. Recent studies suggest that EEG recordings may be generated by a low dimensional nonlinear process [35] [36] [6]. This paper will investigate nonlinearity tests, as well as the use of methods from the theory of nonlinear dynamical systems in the prediction of seizures or seizure like events (SLEs) from complex time series. To do this data is generated from a nonlinear dynamical system with a stochastic time dependent parameter, which attempts to emulate the different states of an epileptic brain. Two kinds of nonlinearity tests were used in simulations, one which specifies a model in the alternative hypothesis (Keenans test) and one which simply states that the process is `not linear' (Surrogate data test). The tests were applied to the generated data, as well as a short EEG recording from a person with epilepsy and a simple nonstationary example.
Both tests were able to correctly identify the model as nonlinear, neither test identified the EEG data as nonlinear and there were contradicting results when the tests were applied to nonstationary data. Estimates of the correlation dimension and Lyapunov exponent were then used to classify the preictal state of the model data. Correlation dimensions showed the best ability to classify states, so they were used in the prediction algorithm. The results of the simulation was that the correlation dimension was able to successfully predict half of the SLEs, however there was an alarmingly high false prediction rate. These results suggest that even though a complicated model may fit the data better, when dealing with prediction it is usually best to use a simple model. A simpler approach with better understood statistical properties may be able to improve on the prediction of SLEs as well as reduce the computational cost of performing them
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