Dissertations / Theses on the topic 'Epileptic Seizure'
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Truong, Nhan Duy. "Epileptic Seizure Detection and Forecasting Ecosystems." Thesis, The University of Sydney, 2020. https://hdl.handle.net/2123/21932.
Full textEberlein, Matthias, Raphael Hildebrand, Ronald Tetzlaff, Nico Hoffmann, Levin Kuhlmann, Benjamin Brinkmann, and Jens Müller. "Convolutional Neural Networks for Epileptic Seizure Prediction." Institute of Electrical and Electronics Engineers (IEEE), 2018. https://tud.qucosa.de/id/qucosa%3A33336.
Full textRamachandran, Ganesan. "Comparison of algorithms for epileptic seizure detection." [Gainesville, Fla.] : University of Florida, 2002. http://purl.fcla.edu/fcla/etd/UFE0000597.
Full textLiu, Hui. "Online automatic epileptic seizure detection from electroencephalogram (EEG)." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0012941.
Full textWang, Yujiang. "Multi-scale modelling of epileptic seizure rhythms as spatio-temporal patterns." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/multiscale-modelling-of-epileptic-seizure-rhythms-as-spatiotemporal-patterns(baad4a1e-fa22-47c2-84af-1c26b9399148).html.
Full textKang, Lövgren Sandy, and Christine Rosquist. "Machine Learning Methods for EEG-based Epileptic Seizure Detection." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259638.
Full textEpilepsi är en av de vanligaste neurologiska sjukdomarna, vilken påverkar miljontals av människor över hela världen. Sjukdomen har alltid varit relevant inom det biomedicinska området på grund av hälsoriskerna den orsakar. Epilepsi karakteriseras av upprepade, oprovocerade anfall och kan fastställas med hjälp av elektroencefalografi (EEG). EEG mäter den elektriska aktiviteten i hjärnan, och en viktig aspekt inom epilepsiforskning inkluderar analys av EEG-data för att kunna detektera epileptiska anfall i ett tidigt skede. Mycket arbete har hittills gjorts på patient-specifika klassificeringsmetoder, medan det är svårare att bygga patient-oberoende modeller. Denna studie fokuserar på patient-oberoende klassificering eftersom den är mer komplicerad på grund av hur EEG-data skiljer sig mellan olika individer. En jämförelse av maskinlärningsmetoder för EEG-baserad detektion av epileptiska anfall utfördes. Algoritmerna som jämfördes var Support Vector Machine (SVM) och K-Nearest Neighbor (KNN). Vår studie visar att båda metoderna gav liknande resultat, dock uppnådde KNN en något högre noggranhet under vissa omständigheter.
Yang, Yikai. "Towards advanced application of artificial intelligence (AI) in epileptic seizure management." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/30022.
Full textJuffali, Walid. "Neural anomalies monitoring : applications to epileptic seizure detection and prediction." Thesis, Imperial College London, 2012. http://hdl.handle.net/10044/1/10570.
Full textMoghim, Negin. "Exploring machine learning techniques in epileptic seizure detection and prediction." Thesis, Heriot-Watt University, 2014. http://hdl.handle.net/10399/2846.
Full textCammarota, Mario. "Reciprocal neuron-astrocyte signaling in epileptic seizure generation and propagation." Doctoral thesis, Università degli studi di Padova, 2013. http://hdl.handle.net/11577/3426301.
Full textL'idea che gli astrociti - la popalazione di cellule gliali più importante del cervello - sono partner attivi dei neuroni in molte delle funzioni del sistema nervoso, ha rappresentato una Rivoluzione Copernicana nello studio della neurobiologia. Per molti anni considerati alla stregua di un cemento (dal greco glia, colla) con l'unica funzione di tenere insieme i neuroni, gli astrociti sono riconosciuti oggi rivestire un ruolo centrale nel processamento dell'informazione. Questa nuova visione del funzionamento cerebrale si fonda sulla scoperta di una comunicazione bidirezionale tra neuroni ed astrociti, processo chiamato gliotrasmmissione. Gli astrociti rispondono ai neurotrasmettitori, ed attraverso un meccanismo calcio dipendente, possono a loro volta rilasciare sostante neuroattive che possono indurre cambiamente funzionali nei neuroni. Nonostante le resistenze opposte all'abbandono del dogma neurocentrico, una grande quantità di dati sperimentali raccolti negli ultimi trentanni ha contribuito a rimodellare il concetto di comunicazione sinaptica, considerando gli astociti, insieme ai terminali pre- e post- sinaptici, un elemento fondamentale della sinapsi tripartita. In altre parole, gli astrociti partecipano transversalmente al processamento dell'informazione nel cervello modulando sia la trasmissione sinaptica che differenti forme di plasticità. Questa nuova coscenza degli astrociti come elementi attivi nella fisiologia del cervello, suggerisce che essi possano essere coinvolti anche nelle patologie neurologiche. Molti studi hanno infatti rivelato che malfunzionamenti nella comunicazione tra neuroni ed astrociti sono direttamente legati a patologie quali il morbo di Alzheimer, il morbo di Parkinson, la sclerosi laterale amiotrofica e l'epilessia. L'obiettivo principale di questa tesi è stato capire come il rilascio di gliotrasmettitori, in particolare il glutammato, possa influenzare la generazione e la propagazione della scarica epilettica.
Levan, Ashley J. "Social Skills and Executive Functioning in Children with Epileptic and Non-Epileptic Seizures." BYU ScholarsArchive, 2015. https://scholarsarchive.byu.edu/etd/5814.
Full textZhu, Dongqing. "Time-frequency and Hidden Markov Model Methods for Epileptic Seizure Detection." University of Cincinnati / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1242070584.
Full textSzerszen, Lukas, and Mosulet Paul-Philip. "Subject-Independent Epileptic Seizure Prediction using Spectral Power and Correlation Coefficients." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208684.
Full textDe senaste 20 åren har algoritmer som kan förutspå epileptiska anfall utvecklats. Dessa har, med varierande resultat, kunnat förutspå epileptiska anfall med sannolikhetsestimeringar som varit bättre än en slumpmässig estimering. Algoritmerna är skräddarsydda för att användas på specifika egenskaper för epilepsin hos en specifik patient. Detta medför att en klassificerare anpassas efter träningen på den specifika EEG-datan för patienten. Att producera en ny patient-specifik algoritm är tidskrä- vande då det kräver både inspelning och att det sätts etiketter på ny EEGdata för varje patient. Därav undersöker denna rapport möjligheten att justera träningen av den patientspecifika algoritmens klassificerare för att göra den oberoende av patienten. För att kunna mäta detta undersöktes om en patientoberoende algoritm kunde uppnå sannolikhetsestimeringar som var lika bra eller bättre än en patientspecifik algoritm. Metoden har anpassats efter en algoritm som varit tillgänglig från en Kaggle-tävling. Algoritmens träning har ändrats för att bli patient-oberoende och resultaten har jämförts med resultaten från den patient-specifika algoritmen. Denna algoritm använder sig utav egenskaperna hos den spektrala energifördelningen, och korrelations koefficienter tillsammans med en Support Vector Machine. Resultaten visar att den patient-oberoende algoritmen presterar sämre än den ursprungliga patient-specifika versionen. Resultaten visar även att den inte överskrider en slumpmässig estimeringsmetod. Utifrån dessa resultat kan en slutsats dras; att baserat på de specifika egenskaperna hos en patients epilepsi, kan inte idag en patientoberoende algoritm, utvecklad genom att anpassa en patient-specifik algoritm, nå sannolikhetsestimeringar som är lika bra med eller bättre än en patient-specifik.
Esteller, Rosana. "Detection of seizure onset in epileptic patients from intracranial EEG signals." Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/15620.
Full textShoeb, Ali Hossam 1981. "Application of machine learning to epileptic seizure onset detection and treatment." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/54669.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 157-162).
Epilepsy is a chronic disorder of the central nervous system that predisposes individuals to experiencing recurrent seizures. It affects 3 million Americans and 50 million people world-wide. A seizure is a transient aberration in the brain's electrical activity that produces disruptive physical symptoms such as a lapse in attention and memory, a sensory hallucination, or a whole-body convulsion. Approximately 1 out of every 3 individuals with epilepsy continues to experience frequent seizures despite treatment with multiple anti-epileptic drugs. These intractable seizures pose a serious risk of injury, limit the independence and mobility of an individual, and result in both social isolation and economic hardship. This thesis presents novel technology intended to ease the burden of intractable seizures. At its heart is a method for computerized detection of seizure onset. The method uses machine learning to construct patient-specific classifiers that are capable of rapid, sensitive, and specific detection of seizure onset. The algorithm detects the onset of a seizure through analysis of the brain's electrical activity alone or in concert with other physiologic signals. When trained on 2 or more seizures and tested on 844 hours of continuous scalp EEG from 23 pediatric epilepsy patients, our algorithm detected 96% of 163 test seizures with a median detection delay of 3 seconds and a median false detection rate of 2 false detections per 24 hour period.
(cont.) In this thesis we also discuss how our detector can be embedded within a low power, implantable medical device to enable the delivery of just-in-time therapy that has the potential to either eliminate or attenuate the clinical symptoms associated with seizures. Finally, we report on the in-hospital use of our detector to enable delay-sensitive therapeutic and diagnostic applications. We demonstrate the feasibility of using the algorithm to control the Vagus Nerve Stimulator (an implantable neuro stimulator for the treatment of intractable seizures), and to initiate ictal SPECT (a functional neuroimaging modality useful for localizing the cerebral site of origin of a seizure).
by Ali Hossam Shoeb.
Ph.D.
Kharbouch, Alaa Amin. "Automatic detection of epileptic seizure onset and termination using intracranial EEG." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/75638.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 87-90).
This thesis addresses the problem of real-time epileptic seizure detection from intracranial EEG (IEEG). One difficulty in creating an approach that can be used for many patients is the heterogeneity of seizure IEEG patterns across different patients and even within a patient. In addition, simultaneously maximizing sensitivity and minimizing latency and false detection rates has been challenging as these are competing objectives. Automated machine learning systems provide a mechanism for dealing with these hurdles. Here we present and evaluate an algorithm for real-time seizure onset detection from IEEG using a machine-learning approach that permits a patient-specific solution. We extract temporal and spectral features across all intracranial EEG channels. A pattern recognition component is trained using these feature vectors and tested against unseen continuous data from the same patient. When tested on more than 875 hours of IEEG data from 10 patients, the algorithm detected 97% of 67 test seizures of several types with a median detection delay of 5 seconds and a median false alarm rate of 0.6 false alarms per 24-hour period. The sensitivity was 100% for 8 out of 10 patients. These results indicate that a sensitive, specific and relatively short-latency detection system based on machine learning can be employed for seizure detection tailored to individual patients. In addition, we describe and evaluate an algorithm for the detection of the cessation of seizure activity within IEEG. Seizure end detection algorithms can enable important clinical applications such as the delivery of therapy to ameliorate post-ictal symptoms, the detection of status epilepticus, and the estimation of seizure duration. Our machine-learning-based approach is patient-specific. The algorithm is designed to search for the termination of electrographic seizure activity once a seizure has been discovered by a seizure onset detector. When tested on 65 seizures, 88% of all seizure ends were detected within 15 seconds of the time determined by a clinical expert to represent the electrographic end of a seizure. We explore the effects of channel pre-selection on seizure onset detection. We evaluate and present the results from a seizure detector that has been restricted to use only a small subset of the channels available. These channels are manually chosen to be those that show the earliest ictal activity. The results indicate that performance can suffer in many cases when the algorithm uses a small set of selected channels, often in the form of an increase in false alarm rate. This suggests that the inclusion of a full channel set allows the system to leverage information that is not readily apparent to a clinical reader (from regions seemingly not involved in the onset) to better differentiate ictal and inter-ictal patterns. Finally, we present and evaluate an algorithm for patient-specific feature extraction, where the feature extraction process for a given patient leverages the training data available for that patient. The results from an evaluation of a detector that supplemented the original spectral energy features with features computed in a patient-specific manner show a significant improvement in 3 out of 5 patients. The results suggest that this is a promising avenue for further improvement in the performance of the seizure onset detector.
by Alaa Amin Kharbouch.
Ph.D.
Emilsson, Linnea, and Yevgen Tarasov. "Minimizing the Number of Electrodes for Epileptic Seizures Prediction." Thesis, KTH, Skolan för teknik och hälsa (STH), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-213001.
Full textLevin, April Robyn. "Early Seizure Blockade: Preventing Long-Term Epileptic Activity in Wag/Rij Rats." Yale University, 2008. http://ymtdl.med.yale.edu/theses/available/etd-08152007-130206/.
Full textKuo, Chia-Hung. "THE ANALYSIS OF HIGH FREQUENCY OSCILLATIONS AND SUPPRESSION IN EPILEPTIC SEIZURE DATA." Case Western Reserve University School of Graduate Studies / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=case1396411237.
Full textATTARD, TREVISAN ADRIAN. "NOVEL COMPUTATIONAL ELECTROENCEPHALOGRAPHIC (EEG) METHODOLOGIES FOR AUTISM MANAGEMENT AND EPILEPTIC SEIZURE PREDICTION." Doctoral thesis, Università degli Studi di Milano, 2015. http://hdl.handle.net/2434/333759.
Full textJiang, Lu-Lin. "The Pivotal Role of Nitric Oxide and Peroxynitrite Imbalance in Epileptic Seizures." Ohio University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1407723694.
Full textSayeed, Md Abu. "Epileptic Seizure Detection and Control in the Internet of Medical Things (IoMT) Framework." Thesis, University of North Texas, 2020. https://digital.library.unt.edu/ark:/67531/metadc1703334/.
Full textSaulnier-Comte, Guillaume. "A machine learning toolbox for the development of personalized epileptic seizure detection algorithms." Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=119550.
Full textL'épilepsie est un trouble neurologique cérébral chronique qui touche environ 50 millions de personnes dans le monde. Cette maladie est caractérisée par la présence de crises d'épilepsie; un événement clinique transitoire causé par une activité cérébrale synchronisée et/ou anormale et excessive. Cette thèse présente un nouvel outil, utilisant des techniques d'apprentissage automatique, capable de générer des algorithmes personnalisés pour la détection de crises épileptiques qui exploitent l'information contenue dans les enregistrements électroencéphalographiques. Une grande variété de caractéristiques conçues pour la recherche en détection/prédiction de crises ont été implémentées. Ce large éventail d'information est adapté à chaque patient grâce à l'utilisation de techniques de sélection de caractéristiques automatisées. Par la suite, l'information découlant de cette procédure est utilisée par un modèle de décision complexe, qui peut détecter les crises en temps réel. La performance des algorithmes est évaluée en utilisant une validation croisée sur des sujets présents dans trois ensembles de données accessibles au public. Nous observons des résultats dignes de l'état de l'art: des taux de détections allant de 76% à 86% avec des taux de faux positifs médians en deçà de 2 par jour. L'outil ainsi qu'un nouvel ensemble de données sont rendus publics afin d'améliorer les connaissances sur la maladie et réduire la surcharge de travail causée par la création d'algorithmes dérivés.
Proix, Timothée. "Large-scale modeling of epileptic seizures dynamics." Thesis, Aix-Marseille, 2015. http://www.theses.fr/2015AIXM4058.
Full textEpileptic seizures are paroxysmal hypersynchronizations of brain activity, spanning several temporal and spatial scales. In the present thesis, we investigate the mechanisms of epileptic seizure propagation on a slow temporal and large spatial scale in the human brain and apply them to a clinical context. For patients with partial refractory epilepsy, seizures arise from a localized region of the brain, the so-called epileptogenic zone, before recruiting distant regions. Success of the resective surgery of the epileptogenic zone depends on its correct delineation, which is often difficult in clinical practice. Furthermore, the mechanisms of seizure onset and recruitment are still largely unknown. We use a mathematical neural mass model to reproduce the time course of interictal and ictal mean activity of a brain region, in which the switching between these states is guided by an autonomous slow permittivity variable. We first introduce a slow permittivity coupling function between these neural masses, hypothesizing the importance of the slow manifold in the recruitment of brain regions into the seizure. Before exploring large-scale networks of such coupled systems, we present a processing pipeline for automatic reconstruction of a patient's virtual brain, including surface and connectivity (i.e., connectome), using structural and diffusion MRI, and tractography methods. Using linear stability analysis and large-scale connectivity, we predict the propagation zone. We apply our method to a dataset of 15 epileptic patients and establish the importance of the connectome in determining large-scale propagation of epileptic seizures
Koffler, Debbie J. "Relationships between rate and localization of interictal spiking and seizure occurrence in epileptic patients." Thesis, McGill University, 1986. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=66217.
Full textGupta, Disha. "Advances in epileptic seizure onset prediction in the EEG with ICA and phase synchronization." Thesis, University of Southampton, 2009. https://eprints.soton.ac.uk/72166/.
Full textCosteira, Daniela André. "Estudo de crises convulsivas em canídeos." Master's thesis, Universidade de Lisboa, Faculdade de Medicina Veterinária, 2018. http://hdl.handle.net/10400.5/16575.
Full textAs crises epiléticas correspondem à principal manifestação neurológica detetada em canídeos. A forma generalizada e a epilepsia idiopática são, respectivamente, o tipo e a etiologia que se observam com mais frequência. O estudo teve como objectivo a caracterização de 34 casos clínicos de canídeos com história de pelo menos uma crise convulsiva. Para tal, procedeu-se à caracterização da amostra populacional afetada e das crises verificadas, à identificação de meios de diagnóstico e protocolos terapêuticos aplicados (com posterior avaliação da sua eficácia) e compreensão da influência de determinadas variáveis na sobrevivência dos pacientes. A amostra populacional foi representada, maioritariamente, pelo sexo masculino (61,8%, n=21) e pelas raças Buldogue Francês (11,8%, n=4) e Labrador Retriever (14,7%, n=5). As crises generalizadas (88,2%, n=30) e as de origem intracraniana (50%, n=17), com destaque para a epilepsia idiopática, foram as mais frequentes. A idade média dos pacientes na primeira crise foi 6,60 ± 4,20 anos, sendo que em canídeos com epilepsia idiopática esta foi mais baixa. As situações de emergência (status epilepticus) foram detetadas em 12 pacientes (35,3%). Os meios de diagnóstico foram aplicados em 31 canídeos (91,2%), tendo-se privilegiado a realização de análises clínicas. A única modalidade terapêutica aplicada foi a farmacológica, em 52,9% da amostra (n=18), porém, o doseamento do(s) fármaco(s) só foi efetuado em 7 destes animais. A mortalidade foi de 23,5% (n=8) na sua maioria devido a motivos relacionados com crises convulsivas (n=5). Os pacientes sem historial de status epilepticus apresentaram maior probabilidade de sobrevivência, comparativamente aos que o desenvolveram. Com o aumento da idade esta probabilidade diminui. O estudo permitiu aprofundar conhecimentos relativamente às crises convulsivas em canídeos, nomeadamente ao nível da caracterização destas crises e da diversidade de meios de diagnóstico e modalidades terapêuticas a serem aplicadas. No entanto, em algumas situações, devido à impossibilidade da realização de um diagnóstico definitivo os protocolos terapêuticos aplicados nem sempre são os mais adequados. Apesar da amostra populacional analisada se restringir a apenas um Centro de Atendimento Médico-Veterinário (CAMV) e ser de baixa representatividade, os resultados obtidos revelam uma necessidade de melhoria relativamente ao aprofundamento das práticas aplicadas nestas situações, considerando uma prática idêntica nos diversos CAMV.
ABSTRACT - Epileptic seizure is the main neurological manifestation observed in dogs. Generalized seizures and idiopathic epilepsy are the most commonly observed type and etiology, respectively. The aim of this study is to characterize 34 cases of dogs with history of at least one seizure. For this purpose, the characterization of the sample and crisis, the diagnosis approach and therapeutic procedures (with efficacy evaluation) and the influence of certain variables on patient’s survival were analized. The sample was represented mostly by male dogs (61,8%, n = 21). Most affected breeds were Frech Bulldog (11,8%, n = 4) and Labrador Retriever (14,7%, n = 5). Generalized seizures (88,2%, n = 30) and intracranial seizures (50%, n = 17), especially idiopathic epilepsy, were the most frequent presentations. The mean age of the patients at their first crisis was 6.60 ± 4.20 years, being lower in dogs with idiopathic epilepsy. The emergencies (status epilepticus) were detected in 12 patients (35.3%). The diagnosis approach was applied in 31 dogs (91,2%), with blood tests being the most required. The pharmacological protocol was the only therapy applied in 52,9% of the sample (n=18), however dosing was only performed in 7. Mortality was 23,5% (n=8), namely due to seizures (n=5). Dogs with no status epilepticus history were more likely to survive than those that developed it. Furthermore, for each additional year, the survival decreases. The present study allowed to expand the knowledge about seizures in dogs, specially for the characterization of the crisis and the diversity of diagnostic approach and therapeutic protocols that can be applied. However, in some cases, the absence of a definitive diagnosis results in the non-application of the most adequate therapy. If results showned in this CAMV represents the reality of other CAMV, then there is a clear need for improvement.
N/A
Fan, Xiaoya. "Dynamics underlying epileptic seizures: insights from a neural mass model." Doctoral thesis, Universite Libre de Bruxelles, 2018. https://dipot.ulb.ac.be/dspace/bitstream/2013/279546/6/contratXF.pdf.
Full textDoctorat en Sciences de l'ingénieur et technologie
info:eu-repo/semantics/nonPublished
Van, Zyl Tiaan. "A longitudinal analysis of the prescribing patterns of anti–epileptic medicine by using a medicine claims database / T. van Zyl." Thesis, North-West University, 2010. http://hdl.handle.net/10394/4918.
Full textThesis (M.Pharm (Pharmacy Practice))--North-West University, Potchefstroom Campus, 2011.
Towne, Alan R. "Outcomes of Status Epilepticus in the Elderly." VCU Scholars Compass, 2007. http://hdl.handle.net/10156/2076.
Full textSprissler, Ryan S., Jacy L. Wagnon, Rosie K. Bunton-Stasyshyn, Miriam H. Meisler, and Michael F. Hammer. "Altered gene expression profile in a mouse model of SCN8A encephalopathy." ACADEMIC PRESS INC ELSEVIER SCIENCE, 2017. http://hdl.handle.net/10150/622816.
Full textSCN8A encephalopathy is a severe, early-onset epilepsy disorder resulting from de novo gain-of-function mutations in the voltage-gated sodium channel Na(v)1.6. To identify the effects of this disorder on mRNA expression, RNA-seq was performed on brain tissue from a knock-in mouse expressing the patient mutation p.Asn1768Asp (N1768D). RNA was isolated from forebrain, cerebellum, and brainstem both before and after seizure onset, and from age-matched wildtype littermates. Altered transcript profiles were observed only in forebrain and only after seizures. The abundance of 50 transcripts increased more than 3-fold and 15 transcripts decreased more than 3 fold after seizures. The elevated transcripts included two anti-convulsant neuropeptides and more than a dozen genes involved in reactive astrocytosis and response to neuronal damage. There was no change in the level of transcripts encoding other voltage-gated sodium, potassium or calcium channels. Reactive astrocytosis was observed in the hippocampus of mutant mice after seizures. There is considerable overlap between the genes affected in this genetic model of epilepsy and those altered by chemically induced seizures, traumatic brain injury, ischemia, and inflammation. The data support the view that gain-of-function mutations of SCN8A lead to pathogenic alterations in brain function contributing to encephalopathy.
Imamura, Hisaji. "Network specific change in white matter integrity in mesial temporal lobe epilepsy." Kyoto University, 2017. http://hdl.handle.net/2433/226747.
Full textMoyano, Vidal Luz Maria. "Epidemiologia de la epilepsia en el Peru : Neurocisticercosis como causa de epilepsia secundaria en la region norte del Peru." Thesis, Limoges, 2016. http://www.theses.fr/2016LIMO0135/document.
Full textBackgrounds. Neurocysticercosis is a parasitic infection of the brain and a common cause of epilepsy in poor regions. There are scarce community-based studies about its comorbidities as epilepsy and neurocysticercosis. Methods. In the northern region of Peru, we performed three community based-studies and one systematic review a) to assess the prevalence of asymptomatic NCC, b) the prevalence of epilepsy and epileptic seizures and NCC c) seroprevalence of cysticercosis (EITB-LLGP) and d) to perform a community intervention to interrupt the Taenia solium transmission. Results. Of the 256 residents who underwent CT scan, 48 (18.8%) had brain calcifications consistent with NCC. Lifetime prevalence of epilepsy was 17.25/1000, the proportion of NCC in people with epilepsy was 39% (109/282), and the seroprevalence of EITB-LLGP in individuals with epilepsy was 40% and between 23.4 to 36.9% in the general population. The association between CC and epilepsy had a OR of 2.7 (95% CI 2.1-3.6, p <0.001). Three rounds of mass treatment with niclosamida in humans and mass treatment and vaccination in pigs was implemented in 107 rural communities (n=81,170 people). No infected pigs with cysticercosis were found in 105 of 107 communities. Conclusion. NCC is a strong contributor of epilepsy and epileptic seizures. We showed that transmission of Taenia solium infection was interrupted on a regional scale in endemic regions in Peru
Introducción. La neurocisticercosis (NCC) es una de las enfermedades helmínticas más frecuentes del SNC y causa de epilepsia sintomática en regiones pobres. Hay escasos estudios basados en comunidad sobre esta zoonosis y sus comorbilidades la epilepsia y la NCC. Metodología. Se desarrollaron en la Región Norte del Perú tres estudios basados en la comunidad, y una revisión sistemática cuyos objetivos fueron: a) evaluar la prevalencia de NCC asintomática, b) la prevalencia de epilepsia asociada a cisticercosis, c) determinación de la exposición a cisticercosis y d) desarrollar una intervención comunitaria que interrumpa la transmisión de cisticercosis. Resultados. De 256 pacientes asintomáticos que tuvieron una tomografía axial computarizada (TAC) cerebral sin contraste, 48 (18%) tuvo una NCC calcificada. La prevalencia de epilepsia encontrada fue de 17.25/1000 habitantes y la proporción de NCC en personas con epilepsia fue de 39% (109/282). El Western Blot (EITB-LLGP) para cisticercosis fue positivo en el 40% de los individuos con epilepsia, y en el 36.9% de la población general. La asociación entre cisticercosis y epilepsia tuvo un OR de 2.7 (95% CI 2.1-3.6, p <0.001). El tratamiento masivo con niclosamida en humanos (n=3), y población porcina más vacunación fue implementada en 107 comunidades rurales de Tumbes; en 105 de 107 no hubo nuevos cerdos infectados con cisticercosis. Conclusiones. (1) La NCC es un factor contribuidor de epilepsia, (2) Se puede cortar la trasmisión de T. solium a escala regional
Ljungdahl, Malin. "Hur förstår du dina funktionella anfall? : En kvalitativ intervjustudie." Thesis, Ersta Sköndal Bräcke högskola, S:t Lukas utbildningsinstitut, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:esh:diva-8076.
Full textIntroduction: Functional seizures, seizures that are believed to have a psychological cause, may face misunderstanding also in health care. To understand this condition, one needs a process-oriented biopsychosocial model in which the patient's own experience of illness is an important part. Questions: How do patients with functional seizures experience their illness?How do patients with functional seizures understand their illness? Method: Five phenomenological semi-structured interviews have been conducted and condensed into four themes using thematic analysis. Results: The four themes are experience of the seizures, understanding of the seizures, consequences of the seizures and what helps against the seizures. Large differences emerge in the interviewees' experience and understanding of their seizures. The seizures are experienced as a feeling of altered contact or no contact between body and brain and they are described to both limit and enrich life. Understanding of illness is expressed in physiological terms, in more vague psychological terms or cannot be understood. There is some doubt as to whether psychological treatment will help. Discussion: It is discussed that the functional seizures can actually be understood, sometimes consciously and sometimes unconsciously as a defense mechanism according to the psychodynamic model. Therefore, short-term psychodynamic therapy such as ISTDP may be a treatment alternative when psychoeducation and CBT have not been sufficient.
Larsson, Mathias. "Stress and Seizures : Behavioural Stress-Reduction Interventions’ Efficiency in Lowering Seizure Frequency." Thesis, Högskolan i Skövde, Institutionen för biovetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-17173.
Full textTallentire, Liz. "Psychological characteristics related to epileptic and non-epileptic seizures." Thesis, Lancaster University, 2016. http://eprints.lancs.ac.uk/80268/.
Full textNovakova, Barbora. "Stress and seizures : exploring the associations between stress and epileptic and psychogenic non-epileptic seizures." Thesis, University of Sheffield, 2015. http://etheses.whiterose.ac.uk/12326/.
Full textVila-Vidal, Manel 1991. "Analysis of human intracranial recordings for clinical and cognitive studies." Doctoral thesis, TDX (Tesis Doctorals en Xarxa), 2021. http://hdl.handle.net/10803/672701.
Full textL’electroencefalografia intracranial (iEEG) és un procediment diagnòstic invasiu que se sol utilitzar en els casos més greus d’epilèpsia resistent a fàrmacs. Mitjançant aquesta tècnica s’obtenen registres de l’activitat cerebral de múltiples regions de forma simultània amb una elevada resolució temporal. En aquesta tesi ens preguntem de quina manera la neurociència computacional pot contribuir a analitzar aquests registres amb l’objectiu de donar resposta a preguntes concretes d’interès clínic i cognitiu. En primer lloc, conceptualitzem l’estudi de les xarxes cerebrals mitjançat l’ús d’EEG en humans en combinació amb tècniques d’anàlisi de dades i modelatge. A partir d’avenços recents, proposem una nova estratègia per localitzar el focus epilèptic mitjançant la identificació dels patrons d’inici de crisi a partir d’estimations espectrals en el domini temporo-freqüencial. En la vessant cognitiva, desenvolupem un nou marc analític que estableix les bases per avaluar el grau d’influència de l’activitat local en l’estat global del cervell durant processos cognitius.
Fairclough, Gillian. "Perspectives on psychogenic non-epileptic seizures." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/perspectives-on-psychogenic-nonepileptic-seizures(59158d34-1870-4b74-a170-29f68ea7fdea).html.
Full textLaurent, François. "Predicting epileptic seizures from intracranial EEG." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=32354.
Full textLes causes des crises d'épilepsie sont encore très mal comprises. L'existence d'un état préparatoire d'avant-crise occasionne de nombreux débats qui restent sans issue. La plupart des travaux sur ce sujet se concentrent sur la recherche de signes avant-coureur (avant la crise d'épilepsie) dans l'électroencéphalogramme. De nouveaux outils de traitement du signal permettent une meilleure description de l'électroencéphalogramme et ont, par conséquent, un plus fort potentiel pour la détection de signe avant-coureurs. Cette étude présente une évaluation statistique et algorithmique de ces mesures. L'évaluation se base sur un nombre de segments limité d'électroencéphalogramme échantillonné à 2000 Hz provenant de cinq patients avec épilepsie temporal. L'évaluation statistique a suggéré plusieurs facteurs pathophysiologique influençant la prédiction de crise d'épilepsie et l'algorithme a réussit à détecter 71% des états d'avant-crise à un temps moyen de 20.9 +/- 17.4 min avant la crise.
Chang, Wei-Chih. "High frequency activity preceding epileptic seizures." Thesis, University of Birmingham, 2010. http://etheses.bham.ac.uk//id/eprint/1252/.
Full textOto, Meritxell. "Undiagnosing and untreating psychogenic non epileptic seizures." Thesis, University of Glasgow, 2011. http://theses.gla.ac.uk/2710/.
Full textFeatherstone, Valerie Anne. "An exploration of epileptic and non-epileptic seizures : an interpretative phenomenological analytic study." Thesis, University of Hull, 2010. http://hydra.hull.ac.uk/resources/hull:5769.
Full textRyan, Bridget Louise. "Parents' and physicians' perceptions of children's epileptic seizures." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ58077.pdf.
Full textGuez, Arthur. "Adaptive control of epileptic seizures using reinforcement learning." Thesis, McGill University, 2010. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=95059.
Full textCette thèse présente une nouvelle méthodologie pour apprendre, de façon automatique, une stratégie optimale de neurostimulation pour le traitement de l'épilepsie. Le défi technique est de moduler automatiquement les paramètres de stimulation, en fonction de l'enregistrement de potentiel de champ observé, afin de minimiser la fréquence et la durée des crises d'épilepsie. Cette méthodologie fait appel à des techniques récentes développées dans le domaine de l'apprentissage machine, en particulier le paradigme d'apprentissage par renforcement, pour formaliser ce problème d'optimisation. Nous présentons un algorithme qui est capable d'apprendre une stratégie adaptative de neurostimulation, et ce directement à partir de données d'apprentissage, étiquetées, acquises depuis des tissus de cerveaux d'animaux. Nos résultats suggèrent que cette méthodologie peut être utiliser pour trouver, automatiquement, une stratégie de stimulation qui réduit efficacement l'indicence des crises d'épilepsie tout en minimisant le nombre de stimulations appliquées. Ce travail met en évidence le rôle crucial que les techniques modernes d'apprentissage machine peuvent jouer dans l'optimisation de stratégies de traitements pour des patients souffrant de maladies chroniques telle l'épilepsie.
Saab, Marc Emile. "A system to detect the onset of epileptic seizures." Thesis, McGill University, 2003. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=80139.
Full textThe system was designed using 307 hours of scalp EEG including a total of 56 seizures in 13 patients. Wavelet decomposition, feature extraction and data segmentation were employed to compute the a priori probabilities required for the Bayesian formulation used in training, testing and operation.
Results based on the analysis of separate testing data (354 hours of scalp EEG including 74 seizures in 15 patients) show an average sensitivity of 70.5% and a false detection rate of 0.25/hr. This average sensitivity is based on the successful detection of 47 of the 74 seizures with a mean detection delay of 10.8s and a delay of 10 seconds or less in 31 of these (66% of detections).
The system is considered to be preliminary and results are promising enough to encourage further work on probability-based seizure detection. The tuning mechanism was also seen to add value to the use of the system.
McGroggan, N. "Neutral network detection of epileptic seizures in the electroencephalogram." Thesis, University of Oxford, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.249426.
Full textPoh, Ming-Zher. "Continuous assessment of epileptic seizures with wrist-worn biosensors." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/68456.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 145-159).
Epilepsy is a neurological disorder characterized predominantly by an enduring predisposition to generate epileptic seizures. The apprehension about injury, or even death, resulting from a seizure often overshadows the lives of those unable to achieve complete seizure control. Moreover, the risk of sudden death in people with epilepsy is 24 times higher compared to the general population and the pathophysiology of sudden unexpected death in epilepsy (SUDEP) remains unclear. This thesis describes the development of a wearable electrodermal activity (EDA) and accelerometry (ACM) biosensor, and demonstrates its clinical utility in the assessment of epileptic seizures. The first section presents the development of a wrist-worn sensor that can provide comfortable and continuous measurements of EDA, a sensitive index of sympathetic activity, and ACM over extensive periods of time. The wearable biosensor achieved high correlations with a Food and Drug Administration (FDA) approved system for the measurement of EDA during various classic arousal experiments. This device offers the unprecedented ability to perform comfortable, long-term, and in situ assessment of EDA and ACM. The second section describes the autonomic alterations that accompany epileptic seizures uncovered using the wearable EDA biosensor and time-frequency mapping of heart rate variability. We observed that the post-ictal period was characterized by a surge in sympathetic sudomotor and cardiac activity coinciding with vagal withdrawal and impaired reactivation. The impact of autonomic dysregulation was more pronounced after generalized tonic-clonic seizures compared to complex partial seizures. Importantly, we found that the intensity of both sympathetic activation and parasympathetic suppression increased approximately linearly with duration of post-ictal EEG suppression, a possible marker for the risk of SUDEP. These results highlight a critical window of post-ictal autonomic dysregulation that may be relevant in the pathogenesis of SUDEP and hint at the possibility for assessment of SUDEP risk by autonomic biomarkers. Lastly, this thesis presents a novel algorithm for generalized tonic-clonic seizure detection with the use of EDA and ACM. The algorithm was tested on 4213 hours (176 days) of recordings from 80 patients containing a wide range of ordinary daily activities and detected 15/16 (94%) tonic-clonic seizures with a low rate of false alarms (<; 1 per 24 h). It is anticipated that the proposed wearable biosensor and seizure detection algorithm will provide an ambulatory seizure alarm and improve the quality of life of patients with uncontrolled tonic-clonic seizures.
by Ming-Zher Poh.
Ph.D.
Dorai, Arvind. "Automated Epileptic Seizure Onset Detection." Thesis, 2009. http://hdl.handle.net/10012/4342.
Full textChiang, Tzu-Chun, and 江子群. "Power Optimization of Epileptic Seizure Detector by Epileptic Channel Prediction." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/m465sn.
Full text國立交通大學
生醫工程研究所
107
Epileptic seizure control is always a popular issue. As time progresses, medication treatment and surgery are not the only way to control the symptom. Recently, there are there are much research about multi-channel seizure detection about multi-channel seizure detection. In order to get higher accuracy and stability, we require more number of channels. Increasing process for the channel may cause a heavy load of power consumption in the system. This thesis mentions that how to use the different channel characteristics to adjust the detecting time. The research extracts two kinds of features to be the based points of controlling detection. One is the frequency band power and the other one is the position of channel. The FFT calculates three bands of frequency power is the principal feature for predicting. Then each channel activity is classified by SVM model. Finally, the predictor decreases the detecting positions which includes numerous non-active channels. It protects from unwanted calculations which tends to decrease the power consumption efficiently. We build the new model for predicting. After simulation, the model can certainly decrease up to 45% of calculations and the seizure detecting still stay in high accuracy. In the future, the number of channels may continuously increase, and then the prediction system can bring more benefit.