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Dissertations / Theses on the topic 'Epileptic Seizure'
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1
Truong, Nhan Duy. "Epileptic Seizure Detection and Forecasting Ecosystems." Thesis, The University of Sydney, 2020. https://hdl.handle.net/2123/21932.
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Epilepsy affects almost 1% of the global population and considerably impacts the quality of life of those patients diagnosed with the disease. Ambulatory EEG monitoring devices that can detect or predict seizures could play an important role for people with intractable epilepsy. Many outstanding studies in detecting and forecasting epileptic seizures using EEG have been developed over the past three decades. Despite this success, their implementations as part of implantable or wearable devices are still limited. To achieve high performance, many of these studies relied on handcraft feature extraction. This approach is not generalizable and requires significant modifications for each new patient. This issue greatly limits the applicability of such methods to hardware implementation. In this thesis, we propose a deep learning-based solution for generalized epileptic seizure detection and forecasting that does not require handcraft feature extraction. The method can be applied to any other patient without the need for manual feature extraction. Secondly, we optimize seizure detection and forecasting systems to reduce computational complexity and power consumption. The optimization is performed from two aspects: algorithm and input signal. In the first aspect, we propose two approaches: automatic channel selection to reduce the number of necessary EEG electrodes; Integer-Net, an integer convolutional neural network, to reduce computational complexity and required memory. In the second aspect, we investigate how sensitive seizure detection algorithms are regarding EEG's resolution. Another problem that we would like to address is the lack of labeled EEG data for epilepsy. Today the process of epileptic seizure identification and data labeling is done by neurologists, which is expensive and time-consuming. We propose an unsupervised learning approach to make use of unlabeled EEG data which is more accessible.
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Eberlein, 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.
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Epilepsy is the most common neurological disorder and an accurate forecast of seizures would help to overcome the patient’s uncertainty and helplessness. In this contribution,
we present and discuss a novel methodology for the classification of intracranial electroencephalography (iEEG) for seizure prediction. Contrary to previous approaches, we categorically refrain from an extraction of hand-crafted features and use a convolutional neural network (CNN) topology instead for both the determination of suitable signal characteristics and the binary classification of preictal and interictal segments. Three different models have been evaluated on public datasets with long-term recordings from four dogs and three patients. Overall, our findings demonstrate the general applicability. In this work we discuss the strengths and limitations of our methodology.
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Ramachandran, Ganesan. "Comparison of algorithms for epileptic seizure detection." [Gainesville, Fla.] : University of Florida, 2002. http://purl.fcla.edu/fcla/etd/UFE0000597.
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Liu, Hui. "Online automatic epileptic seizure detection from electroencephalogram (EEG)." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0012941.
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Wang, 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.
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Epileptic seizures are characterised by an onset of abnormal brain activity that evolves in space and time, which ultimately returns to normal background activity. For different types of seizures, the abnormal activity can be vastly different both in duration, electrographic morphology and spatial extent. Mechanistic understanding of the different seizure dynamics (spatially, as well as temporally) is crucial for the advancement and improvement of clinical treatment. To gain a deeper mechanistic insight into different seizure dynamics, mathematical models of brain processes were developed in this thesis. These models are used to explain electrographic seizure dynamics in their temporal, as well as their spatio-temporal evolution. Our studies show that the temporal evolution of seizure dynamics can be understood in terms of prototypic waveforms, which in turn can be represented in terms of three neural population processes. Such a minimal framework lends itself to a detailed phase space analysis, which elucidates seizure waveforms and seizure transitions as topological properties of the phase space. Based on the phase space considerations we show how during spike-wave seizures, single-pulse stimuli can have more complex effects than previously thought. In terms of the spatio-temporal dynamics of seizures, mechanisms for focal seizure onset and propagation are investigated in a model cortical sheet of coupled, discretised columns. The coupling followed nearest-neighbour, as well as realistic mesoscopic cortical connectivities. Different possible causes (e.g. spatial heterogeneities) of seizure generation, as well as different seizure spreading patterns (via different networks) have been investigated. We conclude that focal seizure onset can be due to global (e.g. whole-brain level) causes, global conditions & local triggers, and local (e.g. cortical column level) causes. Clinically relevant predictions from this work include the suggestion of a specific stimulation protocol in spike-wave seizures that incorporates phase space information; and the suggestion of using microscopic cortical incisions to disrupt the integrity of abnormal cortical tissue in order to prevent focal seizure onset. In conclusion, multi-scale computational modelling of seizure dynamics is proposed as an important tool to link theoretical understanding, experimental results, and patient-specific clinical data.
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Kang, 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.
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Epilepsy is one of the most common neurological diseases that affects millions of persons all over the world. The disease has always been of great importance in the biomedical field, due to the health risks it causes. It is characterized by recurrent, unprovoked seizures and can be assessed by the electroencephalogram (EEG). EEG measures the electrical activity in the brain, and one important aspect of the epilepsy research includes analyzing the EEG data in order to detect epileptic seizures in early stages. A lot of work has been done on patient-specific classifiers, but building patient-independent models is more difficult. This thesis focuses on the cross-patient view as it is more complicated due to EEG variability between different subjects. A comparative analysis of pattern recognition algorithms employed for EEG-based epileptic seizure identification was done. The algorithms compared was the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). Our study shows that the two methods perform similar, although KNN achieved a slightly higher accuracy during certain conditions.Epilepsi 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.
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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.
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Epilepsy has a significant adverse impact on almost 1% of people's health and well-being globally. Clinical EEG monitoring devices that enable seizure onset detection and prediction are crucial for epilepsy patients to manage their seizure disorders. In the past three decades, many epileptic seizure detecting, and prediction methods have been developed and reported high performance. However, most of them are retrospective and lack continental and multi-dataset generalization, transparency, and reproducibility, making them hard to implement into clinical utility. Besides, the seizure prediction biomarker is yet to be fully answered, and this issue significantly limits clinician trust when using the seizure prediction algorithms.
In this thesis, we propose a generalized epileptic seizure detection AI-assisted system that tested on a large scale of the clinical EEG dataset and proved to improve time efficiency while accuracy alongside the human expert. The seizure detection performance is further improved by combining EEG and ECG using a novel multimodal AI system. Secondly, we propose a Bayesian convolutional neural network to facilitate the exploration of potential seizure forecasting biomarkers. Another problem we address is the need for long recording labeled EEG data for seizure prediction. We propose a novel real-time seizure prediction AI system that learns from the on-the-fly weak label generated by the detection model. Ultimately, we focus on developing a low-power, hardware-friendly implementation method using neuromorphic-compatible Spiking Neural Networks (SNNs) for seizure detection. Overall, the work presented in this thesis has tackled several research problems related to advanced AI applications in epileptic seizure detection and prediction and drove these emerging technologies toward building reliable AI systems in real-world clinical settings.
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Juffali, Walid. "Neural anomalies monitoring : applications to epileptic seizure detection and prediction." Thesis, Imperial College London, 2012. http://hdl.handle.net/10044/1/10570.
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There have been numerous efforts in the field of electronics with the aim of merging the areas of healthcare and technology in the form of low power, more efficient hardware. However one area of development that can aid in the bridge of healthcare and emerging technology is in Information and Communication Technology (ICT). Here, databasing and analysis systems can help bridge the wealth of information available (blood tests, genetic information, neural data) into a common framework of analysis. Also, ICT systems can integrate real-time processing from emerging technological solutions, such as developed low-power electronics. This work is based on this idea, merging technological solutions in the form of ICT with the need in healthcare to identify normality in a patients’ health profile. In this work we develop this idea and explain the concept more thoroughly. We then go on to explore two applications under development. The first is a system designed around monitoring neural activity and identifying, through a processing algorithm, what is normal activity, such that we can identify anomalies, or abnormalities in the signal. We explore Epilespy with seizure detection and prediction as an application case study to show the potential of this method. The motivation being that current methods of prediction have proven to be unsuccessful. We show that using our algorithm we can achieve significant success in seizure prediction and detection, above and beyond current methods. The second application explores the link between genetic information and standard tests (blood, urine etc.) and how they link in together to define a personalised benchmark. We show how this could work and the steps that have been made towards developing such a database.
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Moghim, Negin. "Exploring machine learning techniques in epileptic seizure detection and prediction." Thesis, Heriot-Watt University, 2014. http://hdl.handle.net/10399/2846.
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Epilepsy is the most common neurological disorder, affecting between 0.6% and 0.8% of the global population. Among those affected by epilepsy whose primary method of seizure management is Anti Epileptic Drug therapy (AED), 30% go on to develop resistance to drugs which ultimately leads to poor seizure management. Currently, alternative therapeutic methods with successful outcome and wide applicability to various types of epilepsy are limited. During an epileptic seizure, the onset of which tends to be sudden and without prior warning, sufferers are highly vulnerable to injury, and methods that might accurately predict seizure episodes in advance are clearly of value, particularly to those who are resistant to other forms of therapy. In this thesis, we draw from the body of work behind automatic seizure prediction obtained from digitised Electroencephalography (EEG) data and use a selection of machine learning and data mining algorithms and techniques in an attempt to explore potential directions of improvement for automatic prediction of epileptic seizures. We start by adopting a set of EEG features from previous work in the field (Costa et al. 2008) and exploring these via seizure classification and feature selection studies on a large dataset. Guided by the results of these feature selection studies, we then build on Costa et al's work by presenting an expanded feature-set for EEG studies in this area. Next, we study the predictability of epileptic seizures several minutes (up to 25 minutes) in advance of the physiological onset. Furthermore, we look at the role of the various feature compositions on predicting epileptic seizures well in advance of their occurring. We focus on how predictability varies as a function of how far in advance we are trying to predict the seizure episode and whether the predictive patterns are translated across the entire dataset. Finally, we study epileptic seizure detection from a multiple-patient perspective. This entails conducting a comprehensive analysis of machine learning models trained on multiple patients and then observing how generalisation is affected by the number of patients and the underlying learning algorithm. Moreover, we improve multiple-patient performance by applying two state of the art machine learning algorithms.
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Cammarota, 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.
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The idea that astrocytes – the main population of glial cells in the brain – are active
partners of neurons in many aspects of brain functions represented a Copernican
Revolution in neurobiology. Astrocytes, which were for many years considered just
like the cement (from Greek glia i.e. glue) that keeps neuronal cells together, have
now been moved from the periphery to the centre of the universe of information
processing in the brain providing a radically different point of observation in the study
of brain physiology. This new view of brain activity turns around the discovery of a
bidirectional communication between neurons and astrocytes, a process called
gliotransmission. Astrocytes respond to neurotransmitters and through a Ca2+
dependent mechanism release neuroactive substances that induce functional
changes in neurons. In spite of the resistances opposed against the desertion of the
neuronal dogma, a large amount of evidence collected during the last three decades
contributed to reshape the concept of synaptic communication, considering astrocytes
- together with the pre- and post- synaptic membranes - a fundamental element of
the tripartite synapse. In other words, astrocytes participate transversally to
information processing in the brain by modulating both synaptic transmission and
different forms of plasticity.
This new consciousness of astrocytes as active elements in brain physiology,
naturally suggests that these glial cell can potentially be involved in the development
of brain disorders. Indeed many studies revealed that dysfunctions in astrocyteneuron
signaling can be directly involved in many pathologies including Alzheimer’s
disease, Parkinson disease, amyotropic lateral sclerosis and epilepsy.
The main goal in my thesis was to understand how the release of gliotrasnmitters by
astrocytes, in particular glutamate, may influence two distinct phases of epileptic
activity: the generation and the propagation of a focal seizure.L'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.
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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.
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Prior studies have demonstrated that a sizeable percentage of children presenting to the epilepsy monitoring unit for evaluation of paroxysmal events (seizures) are found to have non-epileptic seizures (NES) (Asano et al., 2005). The importance of identifying NES cannot be overstated since misdiagnosis often leads to treatment with antiepileptic drugs, which may have side effects that may negatively impact cognition (Chen, Chow, & Lee, 2001) and perhaps even cognitive development. While studies in adults with epilepsy or NES have demonstrated impaired executive functioning and social outcome compared to healthy peers, less work is present among pediatric populations (Cragar, Berry, Fakhoury, Cibula, & Schmitt, 2002; Rantanen, Eriksson, & Nieminen, 2012). Furthermore, research is void of information regarding social skills between these pediatric groups. The aims of this study were to examine group differences between social skills and executive functioning between pediatric epileptic and NES patients, determine if social skills predict diagnostic classification, and examine correlations between executive functioning and social skill measures. This study was conducted on the epilepsy monitoring units (EMU) at Phoenix Children's Hospital and Primary Children's Medical Center. The parent/caregiver of patients admitted to the EMU for video-EEG diagnosis of seizures was approached regarding study participation. A total of 43 children and parent/caregiver participated in this study. The NES group consisted of15 participants (67% female; M age at testing = 12.62, SD = 3.33), and the epilepsy (ES) group consisted of 28 participants (50% female, M age at testing = 11.79, SD = 3.12). Both the parents and children completed brief questionnaires measuring executive functioning and social skills. These measures included The Behavior Rating Inventory of Executive Functioning, The Behavioral Assessment System for Children, Second Edition, and the Social Skills Improvement System Rating Scales. Binomial logistic regression analysis showed social skills did not significantly predict diagnostic group. No group differences were found between children with epilepsy and NES on measures of executive functioning or social skills. Parents of both groups rated their children as having below average social skills, while children rated their social skills in the average range compared to healthy peers. Both children and parents of both groups rated their executive functioning within the average range. Executive functioning scores and social skill scores significantly correlated and regression analyses indicated that the Behavioral Regulation Index on the BRIEF significantly predicted Social Skills on the SSIS. Interpretationof results, limitations, and future directions are discussed.
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Zhu, 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.
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Szerszen, 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.
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Epileptic seizure prediction algorithms with prediction rates above random have been produced, with varying success, during the last ten to twenty years. The algorithms produced have been tailored to the specific characteristics of a subject’s epilepsy, referred to as subject-specific prediction algorithms. Such customization entails the training of the algorithm’s classifier on the specific EEG-data pertaining to the subject. An inherent requirement is the time-intensive task of recording and labeling the subjects EEG, which will be used for the training of the classifier. As such, this thesis investigates the possibility of adjusting the training of a subject-specific algorithm’s classifier to make it subject-independent. The investigation is based on whether the subject-independent version could achieve prediction rates equal to or better than that of the original subject-specific version. The methodology carried out employs a subjectspecific algorithm, sourced from a Kaggle competition, which utilizes a Support Vector Machine and spectral power and correlation coefficients as its features. The training of the classifier was modified to be subjectindependent and then compared to the performance of the subject-specific version. The results indicate that the subject-independent version performed worse than the original subject-specific one, in fact it performs below or equal to random prediction rates. It is concluded that: due to the dependency of epileptic seizure prediction algorithms on the strict characteristics of a subjects epilepsy, a subject-independent algorithm, produced with the adjustment of a subject-specific version, can’t, at this time, achieve prediction rates equal to or higher than that of the subjectspecific version.De 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.
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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.
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Shoeb, 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.
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Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2009.Cataloged 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.
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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.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged 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.
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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.
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Epilepsy is a neurological disorder affecting 1-2 % of the population in the world. People diagnosed with epilepsy are put at high risk of getting injured due to the unpredictable seizures caused by the disorder. Electroencephalography (EEG) in combination with machine learning can be used for prediction of an epileptic seizure. Therefore, a portable prediction device is of great interest with high emphasis for it to be user-friendly. One way to achieve this is by minimizing the number of electrodes placed on the scalp. This study examines the number of electrodes that provide sufficient information for prediction of a seizure. The highest prediction accuracy of 91 %, 97 % sensitivity and 85 % specificity was achieved with as few as 16 electrodes. Due to the limitation of the intracranial EEG recordings further testing must be performed on scalp EEG recordings to provide valid results.
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Levin, 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/.
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The purpose of this study was to determine how early seizure blockade with ethosuximide (ESX) would influence ion channel expression and long-term spike-wave discharge (SWD) activity in epileptic WAG/Rij rats. The goal was to elucidate the question Do seizures beget seizures? in a genetically prone model and if so, to attempt to interrupt this cycle by early intervention. In our first experiment, we used immunocytochemistry to determine the effect of ESX on cortical expression of ion channels in treated and untreated WAG/Rij rats and age-matched Wistar controls. This experiment revealed that treatment with ESX blocked the upregulation of Nav1.1 and Nav1.6 as well as the downregulation of HCN1 that is associated with epileptic activity in rats (p < .05). In a second experiment, WAG/Rij rats were divided into 3 groups: untreated (H2O), temporary early treatment (ESX 4 month), and continuous early treatment (ESX continuous), and SWD activity was measured by electroencephalogram (EEG) at various timepoints. This second experiment revealed that animals in the ESX 4 month group spent less percent time in SWD (0.242 ± .068 SEM) than animals in the H2O group (0.769 ± .060 SEM, p < .001), although they spent slightly more percent time in SWD than animals in the ESX continuous group (0.020 ± .065 SEM, p = .003). This effect was predominantly due to seizure number, and average seizure duration did not vary among the three groups. Additionally, power spectrum analysis revealed a significant correlation when the difference between power spectra for H2O and ESX 4 month rats was compared to the power spectrum of a seizure (Pearson correlation equals 0.955, 2-tailed significance < .000000001), suggesting quantitatively that seizures were reduced by temporary early treatment. This suggests that early prevention of SWD may reduce the burden of seizures later in life, and that possibilities for prevention of genetic absence epilepsy should be further investigated.
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Kuo, 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.
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ATTARD, 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.
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The doctoral thesis deals with a novel methodology of looking and processing electroencephalographic (EEG) data. The first part deals with real-time brain stimulation in the form of a sonified neurofeedback therapy derived from a clinically comparable portable, 4-channel EEG system. The therapy aims to provide an effective management for symptoms of the Autism Spectrum Disorder (ASD). ASD is characterized with a high level of delta electroencephalographic waveform levels, while alpha and beta prove to be present at lower levels especially in the frontal-temporal regions. The treatment aims at lowering delta waves and promoting alpha and beta waveforms. The second part of the thesis focuses on using EEG data in the prediction of epileptic seizures. With the help of custom built algorithms and neural networks, an effective prediction of an epileptic seizure can be achieved.
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Jiang, 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.
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Sayeed, 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/.
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Epilepsy affects up to 1% of the world's population and approximately 2.5 million people in the United States. A considerable portion (30%) of epilepsy patients are refractory to antiepileptic drugs (AEDs), and surgery can not be an effective candidate if the focus of the seizure is on the eloquent cortex. To overcome the problems with existing solutions, a notable portion of biomedical research is focused on developing an implantable or wearable system for automated seizure detection and control. Seizure detection algorithms based on signal rejection algorithms (SRA), deep neural networks (DNN), and neighborhood component analysis (NCA) have been proposed in the IoMT framework. The algorithms proposed in this work have been validated with both scalp and intracranial electroencephalography (EEG, icEEG), and demonstrate high classification accuracy, sensitivity, and specificity. The occurrence of seizure can be controlled by direct drug injection into the epileptogenic zone, which enhances the efficacy of the AEDs. Piezoelectric and electromagnetic micropumps have been explored for the use of a drug delivery unit, as they provide accurate drug flow and reduce power consumption. The reduction in power consumption as a result of minimal circuitry employed by the drug delivery system is making it suitable for practical biomedical applications. The IoMT inclusion enables remote health activity monitoring, remote data sharing, and access, which advances the current healthcare modality for epilepsy considerably.
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Saulnier-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.
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Epilepsy is a chronic neurological disorder affecting around 50 million people worldwide. It is characterized by the occurrence of seizures; a transient clinical event caused by synchronous and/or abnormal and excessive neuronal activity in the brain. This thesis presents a novel machine learning toolbox that generates personalized epileptic seizure detection algorithms exploiting the information contained in electroencephalographic recordings. A large variety of features designed by the seizure detection/prediction community are implemented. This broad set of features is tailored to specific patients through the use of automated feature selection techniques. Subsequently, the resulting information is exploited by a complex machine learning classifier that is able to detect seizures in real-time. The algorithm generation procedure uses a default set of parameters, requiring no prior knowledge on the patients' conditions. Moreover, the amount of data required during the generation of an algorithm is small. The performance of the toolbox is evaluated using cross-validation, a sound methodology, on subjects present in three different publicly available datasets. We report state of the art results: detection rates ranging from 76% to 86% with median false positive rates under 2 per day. The toolbox, as well as a new dataset, are made publicly available in order to improve the knowledge on the disorder and reduce the overhead of creating derived algorithms.L'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.
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Proix, Timothée. "Large-scale modeling of epileptic seizures dynamics." Thesis, Aix-Marseille, 2015. http://www.theses.fr/2015AIXM4058.
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Les crises épileptiques sont des épisodes paroxysmiques d'activité cérébrale hypersynchrone. Ce travail de thèse s'attache à examiner les mécanismes de propagation des crises d'épilepsie sur une échelle temporelle lente et une grande échelle spatiale dans le cerveau humain et à les appliquer au contexte clinique. Chez les patients souffrant d'épilepsie partielle réfractaire, les crises débutent dans certaines régions localisées du cerveau, dénommées zone épileptogène, avant de recruter des régions distantes. Le succès de l'ablation chirurgicale de la zone epileptogène dépend principalement de sa délimitation adéquate, un problème souvent épineux en pratique clinique. À cela s'ajoute notre compréhension parcellaire des mécanismes à l'origine des crises et de leur propagation. Nous utilisons un modèle mathématique de masse neuronale reproduisant le décours temporel de l'activité moyenne critique et intercritique d'une région cérébrale, guidé de manière autonome par une variable permittive lente. Nous introduisons tout d'abord un couplage permittif lent entre ces masses neuronales, afin de révéler l'importance de la variété lente dans le recrutement des régions cérébrales dans la crise. Nous présentons ensuite un pipeline de traitement des données structurelles et de diffusion IRM pour reconstruire automatiquement le cerveau virtuel d'un patient. Nous utilisons ensuite une analyse de stabilité linéaire et la connectivité large-échelle pour prédire la zone de propagation. Nous appliquons notre méthode à un jeu de données de 15 patients épileptiques et démontrons l'importance du connectome pour prédire la direction de propagation des crisesEpileptic 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
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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.
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Gupta, 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/.
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Seizure onset prediction in epilepsy is a challenge which is under investigation using many and varied signal processing techniques, across the world. This research thesis contributes to the advancement of digital signal analysis of neurophysiological signals of epileptic patients. It has been studied especially in the context of epileptic seizure onset prediction, with a motivation to help epileptic patients by advancing the knowledge on the possibilities of seizure prediction and inching towards a clinically viable seizure predictor. In this work, a synchrony based multi-stage system is analyzed that brings to bear the advantages of many techniques in each substage. The 1st stage of the system unmixes and de-noises continuous long-term (2-4 days) multichannel scalp Electroencephalograms using spatially constrained Independent Component Analysis. The 2d stage estimates the long term significant phase synchrony dynamics of narrowband (2-8 Hz and 8-14 Hz) seizure components. The synchrony dynamics are assessed with a novel statistic, the PLV-d, analyzing the joint synchrony in two frequency bands of interest. The 3rd stage creates multidimensional features of these synchrony dynamics for two classes (‘seizure free’ and ‘seizure predictive’) which are then projected onto a 2-dimensional map using a supervised Neuroscale, a topographic projection scheme based on a Radial Basis Neural Network. The 4th stage evaluates the probability of occurrence of predictive events using Gaussian Mixture Models used in supervised and semi-supervised forms. Preliminary analysis is performed on shorter data segments and the final system is based on nine patient’s long term (2-4 days each) continuous data. The training and testing for feature extraction analysis is performed on five patient datasets. The features extracted and the parameters ascertained with this analysis are then applied on the remaining four long-term datasets as a test of performance. The analysis is tested against random predictors as well. We show the possibility of seizure onset prediction (performing better than a random predictor) within a prediction window of 35-65 minutes with a sensitivity of 65-100% and specificity of 60-100% across the epileptic patients.
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Costeira, 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.
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Dissertação de Mestrado Integrado em Medicina VeterináriaAs 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.
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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.
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In this work, we propose an approach that allows to explore the potential pathophysiological mechanisms (at neuronal population level) of ictogenesis by combining clinical intracranial electroencephalographic (iEEG) recordings with a neural mass model. IEEG recordings from temporal lobe epilepsy (TLE) patients around seizure onset were investigated. Physiologically meaningful parameters (average synaptic gains of the excitatory, slow and fast inhibitory population, Ae, B and G) were identified during interictal to ictal transition. We analyzed the temporal evolution of four ratios, i.e. Ae/G, Ae/B, Ae/(B + G), and B/G. The excitation/inhibition ratio increased around seizure onset and decreased before seizure offset, suggesting the disturbance and restoration of balance between excitation and inhibition around seizure onset and before seizure offset, respectively. Moreover, the slow inhibition may have an earlier effect on the breakdown of excitation/inhibition balance. Results confirm the decrease in excitation/inhibition ratio upon seizure termination in human temporal lobe epilepsy, as revealed by optogenetic approaches both in vivo in animal models and in vitro. We further explored the distribution of the average synaptic gains in parameter space and their temporal evolution, i.e. the path through the model parameter space, in TLE patients. Results showed that the synaptic gain values located roughly on a plane before seizure onset, dispersed during ictal and returned when the seizure terminated. Cluster analysis was performed on seizure paths and demonstrated consistency in synaptic gain evolution across different seizures from individual patients. Furthermore, two patient groups were identified, each one corresponding to a specific synaptic gain evolution in the parameter space during a seizure. Results were validated by a bootstrapping approach based on comparison with random paths. The differences in the path revealed variations in EEG dynamics for patients despite showing an identical seizure onset pattern. Our approach may have the potential to classify the epileptic patients into subgroups based on different mechanisms revealed by subtle changes in synaptic gains and further enable more robust decisions regarding treatment strategy. The increase of excitation/inhibition ratios, i.e. Ae/G, Ae/B and Ae/(B+G), around seizure onset makes them potential cues for seizure detection. We explored the feasibility of a model based seizure detection algorithm. A simple thresholding method was employed. We evaluated the algorithm against the manual scoring of a human expert on iEEG samples from patients suffering from different types of epilepsy. Results suggest that Ae/(B+G), i.e. excitation/(slow + fast inhibition) ratio, allowed the best performance and that the algorithm best suited TLE patients. Leave-one-out cross-validation showed that the algorithm achieved 94.74% sensitivity for TLE patients. The median false positive rate was 0.16 per hour, and median detection delay was -1.0 s. Of interest, the values of the threshold determined by leave-one-out cross-validation for TLE patients were quite constant, suggesting a general excitation/inhibition balance baseline in background iEEG among TLE patients. Such a model-based seizure detection approach is of clinical interest and could also achieve good performance for other types of epilepsy provided that more appropriate model, i.e. better describe epileptic EEG waveforms for other types of epilepsy, is implemented. Altogether, this thesis contributes to the field of epilepsy research from two perspectives. Scientifically, it gives new insights into the mechanisms underlying interictal to ictal transition, and facilitates better understanding of epileptic seizures. Clinically, it provides a tool for reviewing EEG data in a more efficient and objective manner and offers an opportunity for on-demand therapeutic devices.Doctorat en Sciences de l'ingénieur et technologie
info:eu-repo/semantics/nonPublished
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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.
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The prevalence of epilepsy in society is general knowledge; however the impact on
social activity as well as other daily factors are not always fully recognised. Epilepsy
frequently poses a problem with regard to work–related activities (Heaney, 1999:44).
Moran et al. (2004:425) indicated that the major impacts of epilepsy on life were work
and school difficulties, driving prohibition, psychological and social life of which
restriction of work or schooling has the greatest impact on epileptic’s life. In all cases
the type, severity, and frequency of the seizures as well as the age would be relevant.
Davis et al. (2008:451) established that 39% of all epileptics were not adherent to their
therapy and in patients over 65 this was even higher at 43 %. Non–adherence with antiepileptic
medicine appears to be related to increased health care utilisation and costs
and may also lead to an increased probable accidents or injuries
The general objective was to investigate anti–epileptic medicine prescribing patterns
and treatment cost in a section of the private health care sector by using a medicine
claims database.
A retrospective drug utilisation study was done on the data claims from a pharmacy
benefit management company for the study period 1 January 2005 to 31 December
2008.
Firstly epilepsy was investigated in order to understand the disease and to determine
the prevalence and treatment thereof. It was found that epilepsy is still one of the most
common neurological conditions and according to the findings, 2 out of every hundred
patients were using anti–epileptic medicine in this section of the private health care
sector. To make this condition socially more acceptable and understandable, public
education for special target groups concerning the disorder must be conducted as well
as employment training programmes for people with epilepsy themselves. The utilisation patterns of anti–epileptic drugs were reviewed, analysed and interpreted.
It was determined that anti–epileptic medicine items are relatively expensive with
regards to other medicine items on the total database. With regard to gender, more
females are using anti–epileptic medicine than males on the database. The largest age
group of patients using anti–epileptic medicine, is between > 40 years and <= 64 years of
age. It was also clear that prevalence increase as age increase.
With regard to the different prescribers, the number of items prescribed by a general
practitioner was almost double that of the other prescribers. It was further established
that newer anti–epileptic medicines are more expensive than older anti–epileptic
medicine according to the cost per tablet in this section of the private health care
sector.
Carbamazepine and valproate were the two active ingredients that were most
frequently prescribed as a single item on a prescription. After a cost–minimisation
analysis was done, R134 685.66 could have been saved when generic substitution was
implemented.
The refill–adherence rate decreased as age increased. Only 30.46% of the trade
names was refilled according to acceptable refill–adherence rates. The refill–adherence
rate according to active ingredient showed that medicine items containing,
phenobarbitone/vit B or gabapentin had the lowest unacceptable refill–adherence rate.
The limitations for this study was stipulated and recommendations for further research
regarding anti–epileptic medicine were also made.Thesis (M.Pharm (Pharmacy Practice))--North-West University, Potchefstroom Campus, 2011.
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Towne, Alan R. "Outcomes of Status Epilepticus in the Elderly." VCU Scholars Compass, 2007. http://hdl.handle.net/10156/2076.
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Sprissler, 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.
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12 month embargo; Available online 9 November 2016SCN8A 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.
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Imamura, Hisaji. "Network specific change in white matter integrity in mesial temporal lobe epilepsy." Kyoto University, 2017. http://hdl.handle.net/2433/226747.
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Moyano, 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.
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Introduction. La neurocysticercosis (NCC) est l'un des maladies helminthiques les plus courantes du SNC et elle cause de l'épilepsie symptomatique dans les régions pauvres. Il existe peu d'études communautaires sur cette zoonose et leurs comorbidités comme l'épilepsie et la NCC. Méthodologie. Dans la région nord du Pérou, trois études sur la communauté et une révision systématique ont été développés dont les objectifs étaient les suivants: a) évaluer la prévalence de la NCC asymptomatique, b) la prévalence de l'épilepsie associée à la cysticercose, c) la détermination de l'exposition à la cysticercose d) développer une intervention communautaire pour interrompre la transmission de la cysticercose. Résultats. 256 patients asymptomatiques qui avaient une tomodensitométrie (T) sans contraste, 48 (18%) avaient la NCC calcifiés. La prévalence de l'épilepsie trouvée était de 17.25 / 1000 habitants et la proportion de NCC en personnes atteintes d'épilepsie était de 39% (109/282). Le Western Blot (EITB-LLGP) pour la cysticercose a été positive dans le 40% des personnes atteintes d'épilepsie, et dans le 36,9% de la population générale. L'association entre la cysticercose et l'épilepsie avait un OR de 2,7 (95% CI 2.1 – 3.6, p <0,001). Le traitement massif avec niclosamide chez l'homme (n = 3), et plus la vaccination de la population porcine a été mis en oeuvre dans 107 communautés rurales de Tumbes. Aucun porc infecté avec la cysticercose n’a été trouvé en 105 des 107 communautés. Conclusions. (1) La NCC est un facteur contributeur de l'épilepsie, (2) La transmission de T. solium peut être réduite à échelle régionaleBackgrounds. 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
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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.
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Inledning: Funktionella anfall, anfall som inte är epilepsi utan tros ha en psykologisk orsak, kan mötas av oförståelse också inom sjukvården. För att förstå detta tillstånd behöver man en processinriktad biopsykosocial sjukdomsmodell där patientens egen sjukdomsförståelse är en viktig del. Frågeställningar: Hur upplever patienter med funktionella anfall sin sjukdom?Hur förstår patienter med funktionella anfall sin sjukdom? Metod: Fem fenomenologiska halvstrukturerade intervjuer har genomförts och med hjälp av tematisk analys kondenserats till fyra teman. Resultat: De fyra temana är upplevelsen av anfallen, egen förståelse av anfallen, konsekvenser av anfallen och vad hjälper mot anfallen. Stora skillnader framkommer i intervjupersonernas upplevelse och förståelse av sina anfall. Anfallen upplevs som en känsla av förändrad kontakt eller ingen kontakt mellan kropp och hjärna och de beskrivs både begränsa och berika livet. Sjukdomsförståelsen uttrycks i fysiologiska termer, i mer vaga psykologiska termer eller att det inte går att förstå. Det framkommer tveksamhet till om psykologisk behandling hjälper. Diskussion: Det diskuteras att de funktionella anfallen faktiskt går att förstå, ibland medvetet och ibland omedvetet som en försvarsmekanism enligt psykodynamisk modell. Korttids psykodynamisk terapi såsom ISTDP kan därför vara ett behandlingsalternativ när psykoedukation och KBT inte har varit tillräckligt.Introduction: 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.
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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.
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Epilepsy is the most common, chronic, serious neurological disease in the world, with an estimated 65 million people affected worldwide. Recent studies on people diagnosed with epilepsy suggest that stress might trigger epileptic seizures. Interventions aimed at lowering stress might be able to reduce the risk for epileptic seizures among epileptics. In an attempt to explore this possibility, I conducted a systematic review addressing the efficacy of behavioral interventions targeted at lowering stress on seizure frequency among an epileptic population. This article also investigated the efficacy of these interventions on lowering self-perceived stress in the same population. Three databases were searched for obtaining 54 references. After a systematic filtering process, a set of 2 studies was retained after the full search procedure. The results suggest stress-reducing behavioral interventions do not have any statistically significant effects on lowering seizure frequency but have a statistically significant effect on lowering self-perceived stress ratings among an epileptic population. The small but promising results from trials and systematic reviews not included in this review warrant further research into the topic. Limitations regarding search procedure included studies and consideration for further research and reading for the presented topics are discussed.
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Tallentire, Liz. "Psychological characteristics related to epileptic and non-epileptic seizures." Thesis, Lancaster University, 2016. http://eprints.lancs.ac.uk/80268/.
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This thesis consists of a quantitative systematic review, a quantitative empirical research paper, and a reflective critical appraisal. The review included 16 published empirical research papers. It examined how psychological characteristics have been used to differentiate subgroups of people who experience non-epileptic seizures (NES) and contextualised subgroup differences in theories of NES aetiology. Results indicated that trauma experiences, alexithymia, and presence of intellectual disability were characteristics that were important in differentiating subgroups. The aims of the empirical paper were to check data against hypotheses based on previous research, before comparing the psychological characteristics of people who reported experiencing NES and epileptic seizures (ESs). Data were collected via online surveys. NES subgroups were formed using cluster analysis of alexithymia and childhood trauma data. Subgroups were found to differ on childhood trauma, alexithymia, and adult attachment style. There were parallels between the subgroups indicated in the review and empirical paper, which are explored further in the empirical paper discussion and critical appraisal. The empirical paper and systematic review emphasised the complexity of NESs and the importance of assessing and understanding individual differences in research and clinical settings. The alexithymia and adult attachment measures used in the empirical project may be useful as part of an assessment of individual differences. These measures could form a basis for psychological assessment and formulation for NES patients, and may help identify ES patients with attachment and/or alexithymia difficulties who may benefit from psychological assessment and therapy. The two research papers also make recommendations for research relating to treatments appropriate to the identified subgroup characteristics. The critical appraisal reflects on the impact of mind-body dualism in the other two papers and discusses how such considerations may influence clinical practice.
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Novakova, 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/.
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Stress is one of the most frequently self-identified seizure precipitants in patients with epilepsy, and psychogenic non-epileptic seizures (PNES) are by definition associated with psychological distress. Stress is a multifaceted phenomenon, yet few studies have systematically examined its different components in patients with seizures. The main aim of this thesis was therefore to assess the association between stress and seizures using a combination of stress measures, and to develop an intervention targeting stress in patients with seizures. The first study prospectively explored a range of psychological and physiological stress markers in patients undergoing video-telemetry. A diurnal pattern was observed in the physiological measures but, whereas some of the physiological measures were shown to be associated with each other, no close relationship was found with self-reported stress. Notably, none of the stress measures predicted occurrence of epileptic seizures or PNES; however, the occurrence of seizures was found to predict greater self-reported stress and autonomic arousal up to 12 hours after the seizures. A second part of the study assessed implicit attentional responses to stress-related stimuli and suggested patients with epilepsy show heightened vigilance towards threat (especially seizure threat), associated with increased autonomic arousal. A self-help stress-management intervention, developed as part of the second study, was evaluated in a pilot randomised controlled trial. Results from the pilot demonstrated the intervention was acceptable and provided preliminary evidence for its effectiveness in reducing self-perceived stress. Further evaluation in a larger trial may be justified, although future studies should include measures to reduce the high attrition rates observed in the pilot study. Ultimately, examination of the role of stress in seizure disorders continues to be an important area for future research. Simple interventions such as the one developed in this thesis could be a useful complementary treatment option for reducing the distress associated with seizures.
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Vila-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.
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Intracranial electroencephalography (iEEG) is an invasive diagnostic procedure used in severe drug-resistant epilepsy patients that provides simultaneous recordings of multiple brain regions at a very high temporal resolution. In this thesis, we investigate how a number of computational approaches can be used to analyze human intracranial recordings to shed light on specific questions of both clinical and cognitive nature. With this regard, we first conceptualize the problem of mapping human brain networks from EEG with existing data-driven and model-based methods. Building on recent advances, we propose a new strategy to detect the epileptic focus based on a seizure-specific identification of spectral patterns in the time and frequency domains. Along the cognitive line, we develop a novel analytical framework relying on intracranial recordings to assess the level of influence of local neural activations on the brain’s global state during cognition.L’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.
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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.
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This thesis explores the perspectives of people on psychogenic non-epileptic seizures (PNES). It is presented in three separate papers: a systematic literature review; an empirical research paper and a critical reflection of the research process as a whole. The systematic literature review aimed to provide a detailed understanding of stakeholder perspectives on PNES. A systematic search identified relevant studies that were subsequently synthesised using thematic analysis and the broader principles of narrative synthesis. Three broad themes relating to stakeholder perspectives were identified: the nature of PNES as a condition; diagnosis; and management and treatment issues. It was found that both patients and professionals experienced uncertainties in relation to understanding and managing the condition. This highlighted the need for further information and awareness of PNES and the development of clear treatment guidelines. Important differences in opinion were also identified between patients and professionals and consideration was given to how these may disrupt the development of effective partnerships in care. The research into patients' and families' perspectives was found to be lacking and further research was identified as being needed in this area. The empirical paper reports an exploratory qualitative study that aimed to provide an in-depth understanding of the perceived treatment needs of patients with PNES. Semi-structured interviews were conducted and findings were analysed inductively using the principles of thematic analysis. Four key themes were identified: return to normality; post-diagnostic limbo; uncertainty and apprehension about therapy; and need for validation. Patients with PNES described clear goals for their recovery and clear ideas about their treatment needs. However, following their diagnosis, many felt caught in 'limbo' due to uncertainties about their diagnosis and as a result of a lack of post-diagnostic support. Being in 'limbo' also linked to patients' uncertainties about psychology meeting their needs and for some there was apprehension about the potential negative consequences of therapy. The clinical implications of the research are discussed and recommendations for future research are made. The third paper is a critical reflection of the research process as a whole. It provides an overview and evaluation of the first two papers and personal reflections of the lead researcher are offered throughout. Implications for further research and clinical practice are offered and a summary of the research as a whole is offered.
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Laurent, 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.
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The cause of seizures in epileptic patients is still poorly understood. Ongoing debates regarding the existence of a pre-seizure state initiating the seizure remain unresolved. Most of the work on this topic has focused on the identification of forerunners (prior to the seizure occurring) in the electroencephalogram, by using measures intended to isolate and distinguish recognizable patterns. New signal processing tools have been developed to allow for a more accurate characterization of the electroencephalogram, and therefore increase the potential to detect forerunners. This study presents both a statistical and an algorithmic evaluation of the predictive value of these measures. The evaluation was carried out on limited electroencephalogram segments of five temporal epilepsy patients whose EEG was recorded at 2000 Hz. The statistical analysis suggested several pathophysiological factors influencing the seizure prediction, and the algorithm implementation succeeded in detecting 71% of pre-seizure states at a mean time of 20.9 +/- 17.4 min prior to the seizure.Les 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.
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Chang, Wei-Chih. "High frequency activity preceding epileptic seizures." Thesis, University of Birmingham, 2010. http://etheses.bham.ac.uk//id/eprint/1252/.
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High frequency activity (>100 Hz, HFA) is suggested to be related to seizure genesis, but the mechanism of the HFA is not clear. In the present work HFAs and epileptic features including electrographic seizures (trains of hypersynchronous population spikes lasting ~46 sec) and interictal discharges (abrupt potential deflections, ~40 ms) were induced in rat hippocampal slices by increasing potassium concentration in artificial cerebrospinal fluid (8-10 mM, ACSF). We demonstrated that 1) the HFA was formed mainly by synchronous firing of pyramidal neurons while a subset of interneurons might contribute the HFA; 2) the frequencies of the HFAs were region-specific (186 Hz in CA1 and 240 Hz in CA3), and seizures were present only in CA1; 3) build-up of HFA preceding seizures was observed and it was disrupted by refractory periods triggered by interictal discharges, which were abrupt potential deflections present between seizures every 0.8 sec; 4) interictal discharges have both pro- and anti-effects on seizure genesis, and the dual consequences might be due to modifying HFA; 5) synaptic transmission through glutamatergic and γ-aminobutyric acid-ergic synapses were not essential in HFA formation but they were related to the modulations of HFA. Our findings suggested the crucial role of HFA in the seizure genesis and the potential value in seizure prediction by monitoring the HFA.
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Oto, Meritxell. "Undiagnosing and untreating psychogenic non epileptic seizures." Thesis, University of Glasgow, 2011. http://theses.gla.ac.uk/2710/.
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Thesis Overview Psychogenic nonepileptic seizures (PNES) can be defined as paroxysmal events that resemble epileptic seizures, without being associated with either abnormal electrical activity of the brain or primary physiological disturbances otherwise. It is estimated that about 10% of new presentations seen in an epilepsy clinic, and up to 30% of patients with intractable epilepsy will eventually be diagnosed as having PNES (Benbadis & Hauser, 2000). Attributing a specific ‘cause’ to PNES is conceptually and clinically contentious but it seems reasonable to say that they represent a physical expression of psychological distress involving behaviour that the patient finds difficult or impossible to control or disavows as being intentional. Most patients with PNES are initially thought to have epilepsy and treated with antiepileptic drugs (AED), sometimes for many years. Up to 40% of patients are inappropriately maintained on AEDs after the diagnosis of PNES has been established. As such, rather than being intrinsic to the condition, the widely reported poor outcomes associated with PNES may be substantially confounded by continued inappropriate medical management and iatrogenic harm. Withdrawing or continuing antiepileptic medication in patients with PNES could have important physical and psychological consequences, which may affect the prognosis of the attack disorder. If this is the case, manipulating medication following the diagnosis of PNES may have a role in the management of this disorder. The work contained in this thesis aims to explore some aspects of the effects that continuing or withdrawing AED has on the course and outcome of PNES. Following an initial general overview on the subject of PNES (chapter 1), a systematic review of the literature is presented in chapter 2; the conclusion being a lack of good quality and reliable evidence for the effects of AED treatment in patients with PNES and a need for further original research in this area. The rationale and programme of research is presented in chapter 3 Chapter 4 presents the results of a large observational study to establish the feasibility and safety of supervised AED withdrawal in patients with an established diagnosis of PNES. Only 3 of the 78 patients included reported a new type of event requiring the reintroduction of AED, and no serious medical events were reported. The study therefore shows that, with appropriate diagnostic investigations and surveillance during follow-up, withdrawal of AED can be achieved safely in patients with PNES. A randomised controlled trial presented in chapter 5 aims to evaluate the potential therapeutic effect of AED withdrawal. Of the 25 subjects recruited, 14 were randomised to immediate withdrawal (IW) and 11 to delayed withdrawal (DW). Patients randomised to IW had a significant reduction in the use of emergency treatment for PNES, and a lower proportion was found to be using emergency services. The IW group also had a sustained reduction of attacks throughout the study and by 18 months post-diagnosis 50% were attack free as compared with 27% in the DW group. The results of this exploratory trial suggested a possible therapeutic effect of AED withdrawal, with a sustained reduction of attacks following the withdrawal of medication, coupled with a significant reduction in health care utilisation and no evidence of any deterioration. The last original paper presented in chapter 6 investigates the longer term psychosocial outcome of PNES with an observational study of the 25 patients included in the RCT. This study reports a significant improvement in some psychological measures; particularly in illness representations and mood, as well as for some measures of social adjustment. The evidence presented in these three studies (chapter 4, 5 and 6) suggests that a clear delivery of the diagnosis of PNES, followed by AED withdrawal, is safe and has possible beneficial effects on the clinical and psychosocial outcome of PNES. In particular medication withdrawal in and of itself appears to be a helpful concomitant in the successful removal of an inappropriate label of label of epilepsy, reduces the potential for iatrogenic harm, may help patients to shift towards a more psychologically-based explanation, and is associated with positive psychosocial outcomes. Finally, chapter 7 gives a summary of the main findings as well as discussing methodological limitations of the current research. The clinical implications of the evidence from this body of work are also discussed, as well as possible avenues for future research in the field.
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Featherstone, 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.
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Background: Differentiating epileptic seizures from non-epileptic seizures (NES) has always been difficult. Seizures can look very similar, substantial physical injury and incontinence can occur in both conditions and people can have both conditions simultaneously. Treatment for each condition is very different however, epilepsy needing anti-epileptic medication whereas NES is a psychologically rooted condition. Aims: To develop previous work; To document a number of detailed seizures descriptions and to analyse these using Interpretative Phenomenological Analysis (IPA); To identify linguistic markers to differentiate NES from epilepsy. Methodology: This project used IPA as a more expansive method of 'history taking' being completely patient led. The approach and its theoretical antecedents have been described in depth in the thesis. Four newly referred patients with uncertain diagnoses were interviewed once, three twice. There was additional, contextual data. Results: The interpretation illustrated that subjective seizure experiences using IPA can contribute to previous work: It heralded the potential beginnings of the development of an alternative 'seizure discourse' for lay and professionals. It had the potential to contribute to patient information material and a screening tool. It offered new ideas for clinical practice and research. Discussion: As an approach, IPA has the potential to combine its findings with those in the field of neurophenomenology in terms of expanding knowledge of corresponding subjective experiences. Conclusions: Given that subjective experiences of people can help locate seizure foci, IPA has the potential for establishing itself as a qualitative scientific research approach in the area of seizure experiences.
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Ryan, 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.
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Guez, 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.
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This thesis presents a new methodology for automatically learning an optimal neurostimulation strategy for the treatment of epilepsy. The technical challenge is to automatically modulate neurostimulation parameters, as a function of the observed field potential recording, so as to minimize the frequency and duration of seizures. The methodology leverages recent techniques from the machine learning literature, in particular the reinforcement learning paradigm, to formalize this optimization problem. We present an algorithm which is able to learn an adaptive neurostimulation strategy directly from labeled training data acquired from animal brain tissues. Our results suggest that this methodology can be used to automatically find a stimulation strategy which effectively reduces the incidence of seizures, while also minimizing the amount of stimulation applied. This work highlights the crucial role that modern machine learning techniques can play in the optimization of treatment strategies for patients with chronic disorders such as epilepsy.Cette 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.
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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.
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During prolonged EEG monitoring of epileptic patients, the continuous recording may be marked where seizures are likely to have taken place. Several methods of automatic seizure detection exist, but few can operate as an on-line seizure alert system. Proposed is a seizure detection system that can be used in real-time to alert medical staff to the onset of a patient seizure and hence improve clinical diagnosis. Proposed is a system based on the seizure probability of a section of EEG. Final operation features a user-tuneable threshold to exploit the trade-off between sensitivity and detection delay and an acceptable false detection rate.The 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.
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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.
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Poh, Ming-Zher. "Continuous assessment of epileptic seizures with wrist-worn biosensors." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/68456.
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Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2011.Cataloged 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.
49
Dorai, Arvind. "Automated Epileptic Seizure Onset Detection." Thesis, 2009. http://hdl.handle.net/10012/4342.
Full textAbstract:
Epilepsy is a serious neurological disorder characterized by recurrent unprovoked seizures due to abnormal or excessive neuronal activity in the brain. An estimated 50 million people around the world suffer from this condition, and it is classified as the second most serious neurological disease known to humanity, after stroke. With early and accurate detection of seizures, doctors can gain valuable time to administer medications and other such anti-seizure countermeasures to help reduce the damaging effects of this crippling disorder.
The time-varying dynamics and high inter-individual variability make early prediction of a seizure state a challenging task. Many studies have shown that EEG signals do have valuable information that, if correctly analyzed, could help in the prediction of seizures in epileptic patients before their occurrence. Several mathematical transforms have been analyzed for its correlation with seizure onset prediction and a series of experiments were done to certify their strengths. New algorithms are presented to help clarify, monitor, and cross-validate the classification of EEG signals to predict the ictal (i.e. seizure) states, specifically the preictal, interictal, and postictal states in the brain. These new methods show promising results in detecting the presence of a preictal phase prior to the ictal state.
50
Chiang, 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 textAbstract:
碩士國立交通大學
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.