Relevant bibliographies by topics / Epileptic Seizure

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  2. Dissertations / Theses
  3. Books
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Academic literature on the topic 'Epileptic Seizure'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 September 2023

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Journal articles on the topic "Epileptic Seizure"

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Rangarajan, Punitha, Kulandaiammal Moorthy, and Arunan Subbiah. "Observational study on the utilization pattern of adjuvant anti-epileptics and their adverse effects." International Journal of Basic & Clinical Pharmacology 9, no. 1 (December 24, 2019): 170. http://dx.doi.org/10.18203/2319-2003.ijbcp20195781.

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Background: Epilepsy is a disease characterized by an enduring predisposition to generate epileptic seizures. Pharmacological therapy is the cornerstone of treatment of epilepsy. In more than 50% patients seizure not controlled with first-line anti-epileptic drugs so added with adjuvant drugs. Therefore adjuvant anti-epileptic drugs play an important role in preventing seizure remission in known epilepsy patients. Observational study was to evaluate the utilisation pattern of adjuvant anti-epileptic drugs and to assess their clinical correlation and observe the adverse effects of the adjuvant anti-epileptics.Methods: Eligible 100 patients who attended the neurology outpatient department where enrolled in the study. Demographic data, type of epilepsy, presence or absence of seizure episode (4 months), adjuvant anti-epileptic prescribed along with the first-line drugs and adverse effects were noted. Clinical correlation and rationale for the usage of adjuvant anti-epileptics were assessed. Descriptive statistics used for statistical analysis.Results: The most common types of seizures were generalised tonic clonic seizures (41%) and complex partial seizures (37%). Most commonly used 1st line drug was phenytoin tablet. Most common adjuvant anti-epileptics used were clonazepam (30), clobazam (24) tablets. Most common adverse effect noted was dizziness (31%).Conclusions: Tablet clonazepam is effective adjunct for tonic clonic seizures. Clobazam table is recommended as add-on drug for focal and generalised seizures. Adjuvant anti-epileptic drugs decrease seizure remission with fewer tolerable adverse effects.
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Schiefer, Johannes, and Rainer Surges. "Notfallversorgung und Erstbehandlung epileptischer Anfälle im Erwachsenenalter." DMW - Deutsche Medizinische Wochenschrift 144, no. 02 (January 2019): 83–92. http://dx.doi.org/10.1055/a-0660-3174.

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AbstractSuspected epileptic seizures are a frequent cause of emergency hospital care. After single seizures, the emergency management includes safety measures and diagnostic efforts to distinguish epileptic seizures from its manifold mimics and to possibly detect acute causes of epileptic seizures. Convulsive status epilepticus requires rapid anticonvulsant treatment according to established protocols and diagnostics to rule out underlying acute brain diseases. After a first seizure, typical EEG- and MRI findings may indicate an elevated recurrence risk, thereby justifying the ultimate diagnosis of epilepsy and initiation of anticonvulsant therapy. This article reviews the recent definition of epilepsy, summarizes clinical characteristics of epileptic seizures and its mimics and provides an overview of established therapies of single convulsive seizures, convulsive status epilepticus and early care of adults after first unprovoked seizures.
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Catenoix, H., R. Marignier, C. Ritleng, M. Dufour, F. Mauguière, C. Confavreux, and S. Vukusic. "Multiple sclerosis and epileptic seizures." Multiple Sclerosis Journal 17, no. 1 (September 22, 2010): 96–102. http://dx.doi.org/10.1177/1352458510382246.

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Background: The association between epilepsy and multiple sclerosis (MS) is not a coincidence. Objective: Our objective was to compare MS patients with or without history of seizures. Methods: In a population of 5041 MS patients, we identified 102 (2%) patients with epileptic seizures. In 67 patients (1.3%), epileptic seizure could not be explained by any cause other than MS. Results: In these 67 patients, the median age at occurrence of the first epileptic seizure was 33 years. Epilepsy was the initial clinical manifestation of MS in seven patients. In total, 62 patients (92.5%) presented only one or a few seizures, and 18 patients (27%) presented at least one episode of status epilepticus, fatal in two. Compared with MS patients without epilepsy, there was no difference in gender, type of MS course and time from onset of MS to the progressive phase. Conversely, the median age at MS onset was earlier (25.0 years vs. 30, p < 0.0001) and there was a trend for a shorter time from MS onset to non-reversible disability. Conclusions: Our study confirms an increased risk of epileptic seizures in MS patients. It underlines that seizures may be the first observable symptom in MS and the frequency and seriousness of status epilepticus.
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Pawar, Sanjay Shamrao, and Sangeeta Rajendra Chougule. "Classification and Severity Measurement of Epileptic Seizure using Intracranial Electroencephalogram (iEEG)." International Journal of Innovative Technology and Exploring Engineering 10, no. 2 (December 10, 2020): 36–41. http://dx.doi.org/10.35940/ijitee.b8249.1210220.

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The Epileptic seizure is one of major neurological brain disorders and about 50 million of world’s population is affected by it. Electroencephalography is medical test which records brain signal by mounting electrodes on scalp or brain cortex to diagnosis seizure. Scalp Electroencephalography has low spatial resolution and presence of external artifact as compared to Intracranial Electroencephalography. In Intracranial Electroencephalography strip, grid and depth type of electrodes are implanted on cortex of brain by surgery to measure brain signal. Analysis of brain signal was carried out in past in diagnosis of Epileptic seizure. Seizure classification and Severity measurement of Epileptic Seizure are still challenging areas of research. Seizures are classified as focal seizure, generalized and secondary generalized seizure depending upon the area of brain which it generates and how it spreads. Classification of seizure helps in treatment of seizure and during brain surgery to operate on brain part which is responsible for continuous seizures generation. Developed seizure classification algorithm classifies seizures as focal Seizure, generalized Seizure and secondary generalized seizure depending on the percentage of iEEG electrodes detecting seizure activity. Seizure severity measurement scale is developed by modification in National Hospital Seizure Severity Scale. Seizures are graded as Mild seizure, Moderate seizure and severe seizure depending on its severity. Seizure Classification and Seizure Severity Measurement improves life quality of Epileptic patients by proper drug management.
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Zambrana-Vinaroz, David, Jose Maria Vicente-Samper, Juliana Manrique-Cordoba, and Jose Maria Sabater-Navarro. "Wearable Epileptic Seizure Prediction System Based on Machine Learning Techniques Using ECG, PPG and EEG Signals." Sensors 22, no. 23 (December 1, 2022): 9372. http://dx.doi.org/10.3390/s22239372.

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Epileptic seizures have a great impact on the quality of life of people who suffer from them and further limit their independence. For this reason, a device that would be able to monitor patients’ health status and warn them for a possible epileptic seizure would improve their quality of life. With this aim, this article proposes the first seizure predictive model based on Ear EEG, ECG and PPG signals obtained by means of a device that can be used in a static and outpatient setting. This device has been tested with epileptic people in a clinical environment. By processing these data and using supervised machine learning techniques, different predictive models capable of classifying the state of the epileptic person into normal, pre-seizure and seizure have been developed. Subsequently, a reduced model based on Boosted Trees has been validated, obtaining a prediction accuracy of 91.5% and a sensitivity of 85.4%. Thus, based on the accuracy of the predictive model obtained, it can potentially serve as a support tool to determine the status epilepticus and prevent a seizure, thereby improving the quality of life of these people.
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Bhardwaj, Ankit, Atma Ram Sharma, and Sarla Sharma. "Cefixime induce non convulsive status epileptics: a neurotoxic effect." International Journal of Basic & Clinical Pharmacology 8, no. 10 (September 25, 2019): 2341. http://dx.doi.org/10.18203/2319-2003.ijbcp20194284.

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Nonconvulsive status epileptics comprises a group of syndromes that display a great diversity regarding response to anticonvulsants ranging from virtually self-limiting variants to entirely refractory forms cephalosporins are thought to provoke seizure through inhibitory effects on gamma-aminobutyric acid (GABA) transmission and GABA receptors. Interference with GABA transmission result in pre-disposition towards excitatory neurotransmission, which can leads to seizures. Antibiotics can alter the serum concentration of anti-epileptic, resulting in seizures and anti-epileptic drugs toxicity.
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Sills, Graeme J. "Seizures Beget Seizures: A Lack of Experimental Evidence and Clinical Relevance Fails to Dampen Enthusiasm." Epilepsy Currents 7, no. 4 (July 2007): 103–4. http://dx.doi.org/10.1111/j.1535-7511.2007.00189.x.

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Three Brief Epileptic Seizures Reduce Inhibitory Synaptic Currents, GABAACurrents, and GABAA-Receptor Subunits. Evans MS, Cady CJ, Disney KE, Yang L, LaGuardia JJ. Epilepsia 2006;4710):1655–1664. PURPOSE: Cellular mechanisms activated during seizures may exacerbate epilepsy. γ-Aminobutyric acid (GABA) is the major inhibitory neurotransmitter in brain, and we hypothesized that brief epileptic seizures may reduce GABA function. METHODS: We used audiogenic seizures (AGSs) in genetically epilepsy-prone rats (GEPRs) to investigate effects of seizures on GABA-mediated inhibition in the presence of epilepsy. GEPRs are uniformly susceptible to AGSs beginning at 21 postnatal days. AGSs are brief convulsions lasting 20 s, and they begin in inferior colliculus (IC). We evoked three seizures in GEPRs and compared the results with those in seizure-naive GEPRs and nonepileptic Sprague-Dawley (SD) rats, the GEPR parent strain. RESULTS: Whole-cell recording in IC slices showed that GABA-mediated monosynaptic inhibitory postsynaptic currents (IPSCs) were reduced 55% by three brief epileptic seizures. Whole-cell recording in IC neuronal cultures showed that currents elicited by GABA were reduced 67% by three seizures. Western blotting for the alpha1 and alpha4 subunits of the GABAA receptor showed no statistically significant effects. In contrast, three brief epileptic seizures reduced gamma2 subunit levels by 80%. CONCLUSIONS: The effects of the very first seizures, in animals known to be epileptic, in an area of brain known to be critical to the seizure network, were studied. The results indicate that even brief epileptic seizures can markedly reduce IPSCs and GABA currents and alter GABAA-receptor subunit protein levels. The cause of the reductions in IPSCs and GABA currents is likely to be altered receptor subunit composition, with reduced gamma2 levels causing reduced GABAA-receptor sensitivity to GABA. Seizure-induced reductions in GABA-mediated inhibition could exacerbate epilepsy.
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Ben-Menachem, Elinor. "Is Prolactin a Clinically Useful Measure of Epilepsy?" Epilepsy Currents 6, no. 3 (May 2006): 78–79. http://dx.doi.org/10.1111/j.1535-7511.2006.00104.x.

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Use of Serum Prolactin in Diagnosing Epileptic Seizures: Report of the Therapeutics and Technology Assessment Subcommittee of the American Academy of Neurology Chen DK, So YT, Fisher RS Neurology 2005;65(5):668–675 (Review) Objective The purpose of this article is to review the use of serum prolactin assay in epileptic seizure diagnosis. Methods The authors identified relevant studies in multiple databases and reference lists. Studies that met inclusion criteria were summarized and rated for quality of evidence, and the results were analyzed and pooled where appropriate. Results Most studies used a serum prolactin of at least twice baseline value as abnormal. For the differentiation of epileptic seizures from psychogenic nonepileptic seizures, one Class I and seven Class II studies showed that elevated serum prolactin was highly predictive of either generalized tonic–clonic or complex partial seizures. Pooled sensitivity was higher for generalized tonic–clonic seizures (60.0%) than for complex partial seizures (46.1%), while the pooled specificity was similar for both (approximately 96%). Data were insufficient to establish validity for simple partial seizures. Two Class II studies were consistent in showing prolactin elevation after tilt-test–induced syncope. Inconclusive data exist regarding the value of serum prolactin following status epilepticus, repetitive seizures, and neonatal seizures. Recommendations Elevated serum prolactin assay, when measured in the appropriate clinical setting at 10 to 20 minutes after a suspected event, is a useful adjunct for the differentiation of generalized tonic–clonic or complex partial seizure from psychogenic nonepileptic seizure among adults and older children (Level B). Serum prolactin assay does not distinguish epileptic seizures from syncope (Level B). The use of serum PRL assay has not been established in the evaluation of status epilepticus, repetitive seizures, and neonatal seizures (Level U).
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Masood, Eatemadifar, and Sepanj neya khaterah. "Seizure relationship and epileptic seizures." Pars of Jahrom University of Medical Sciences 3, no. 3 (July 1, 2006): 34–38. http://dx.doi.org/10.29252/jmj.3.3.34.

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Alves, Lucas Victor, Danielle Di Cavalcanti Sousa Cruz, Ana Maria Campos van der Linden, Ana Rodrigues Falbo, Maria Júlia Gonçalves de Mello, Camila Esteves Paredes, Germanna Virginya Cavalcanti Silva, José Natal Figueiroa, and Patrícia Gomes de Matos Bezerra. "Epileptic seizures in children with congenital Zika virus syndrome." Revista Brasileira de Saúde Materno Infantil 16, suppl 1 (November 2016): S27—S31. http://dx.doi.org/10.1590/1806-9304201600s100003.

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Abstract Objectives: to describe preliminary data referred to epileptic seizures and the probability of occurring these epileptic seizures in the infants' first months of life with congenital Zika virus (ZIKV) syndrome. Methods: concurrent cohort study including newborns and infants with congenital Zika virus syndrome attended at the specialized outpatient clinic at IMIP, Recife, Pernambuco, from October 2015 to May 2016. Results: data on 106 infants were analyzed with confirmed or suspected association to ZIKV infection. Forty children (38.7%) presented an epileptic seizure, classified at 43.3% of the cases as being spasms, 22.7% as generalized tonic seizures, 20.5% as partial and 4.5% other types of seizures. The median of days until the first report on the occurrence of epileptic seizure was 192 days of life. Conclusions: children with congenital Zika virus syndrome presented a high incidence of epileptic seizures before the end of the first semester of life, and spasm was the epileptic seizure mostly observed.
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Dissertations / Theses on the topic "Epileptic Seizure"

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Truong, Nhan Duy. "Epileptic Seizure Detection and Forecasting Ecosystems." Thesis, The University of Sydney, 2020. https://hdl.handle.net/2123/21932.

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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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Books on the topic "Epileptic Seizure"

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J, Rowan A., and Gates John R, eds. Non-epileptic seizures. Boston: Butterworth-Heinemann, 1993.

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R, Gates John, and Rowan A. J, eds. Non-epileptic seizures. 2nd ed. Boston: Butterworth-Heinemann, 2000.

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Lennart, Gram, ed. Pseudo-epileptic seizures. Petersfield, UK: Wrightson Biomedical Pub., 1993.

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Panayiotopoulos, C. P. Imitators of epileptic seizures. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4023-8.

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Caplan, Rochelle, Julia Doss, Sigita Plioplys, and Jana E. Jones. Pediatric Psychogenic Non-Epileptic Seizures. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55122-7.

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Hosten, Willard, and Aleph Burtsev. Seizures and anti-epileptic drugs. New York: Nova Science Publishers, 2012.

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Panayiotopoulos, C. P. Reflex seizures and related epileptic syndromes. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4042-9.

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Hans, Lüders, and Noachtar Soheyl, eds. Epileptic seizures: Pathophysiology and clinical semiology. New York: Churchill Livingstone, 2000.

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Lüders, Hans. Atlas of epileptic seizures and syndromes. Philadelphia: Saunders, 2001.

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Seizures and epilepsy. Philadelphia: F.A. Davis Co., 1989.

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Book chapters on the topic "Epileptic Seizure"

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Alzami, Farrikh, Daxing Wang, Zhiwen Yu, Jane You, Hau-San Wong, and Guoqiang Han. "Robust Epileptic Seizure Classification." In Intelligent Computing Theories and Application, 363–73. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42294-7_32.

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Pardeshi, K. V., and P. A. Dhulekar. "Detection of Epileptic Seizure Patient." In Communications in Computer and Information Science, 757–67. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3433-6_91.

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Acharya, Divya, Anushna Gowreddygari, Richa Bhatia, Varsha Shaju, S. Aparna, and Arpit Bhardwaj. "Epileptic Seizure Detection Using CNN." In Communications in Computer and Information Science, 3–16. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0401-0_1.

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Poeck, Klaus. "First Epileptic Seizure(s) in Adulthood." In Diagnostic Decisions in Neurology, 55–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-70693-6_14.

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Biswal, Sushree Sangita, Itishree Biswal, and Mihir Narayan Mohanty. "Epileptic Seizure Characterization Using Transform Domain." In Advances in Intelligent Systems and Computing, 941–51. New Delhi: Springer India, 2016. http://dx.doi.org/10.1007/978-81-322-2656-7_86.

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Grigorovsky, Vasily, Uilki Tufa, Daniel Jacobs, and Berj L. Bardakjian. "Machine Intelligence-Based Epileptic Seizure Forecasting." In Neural Engineering, 535–65. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43395-6_19.

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Paul, Yash, and Sándor Fridli. "Epileptic Seizure Detection Using Piecewise Linear Reduction." In Computer Aided Systems Theory – EUROCAST 2019, 364–71. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45096-0_45.

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Kumar, Sudesh, Rekh Ram Janghel, and Satya Prakash Sahu. "Epileptic Seizure Detection Using Machine Learning Techniques." In Advances in Intelligent Systems and Computing, 255–66. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5148-2_23.

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Sreekumar, Akshay, A. N. Sasidhar Reddy, D. Udaya Ravikanth, M. Chaitanya Chowdary, G. Nithin, and P. S. Sathidevi. "Epileptic Seizure Detection Using Machine Learning Techniques." In Lecture Notes in Electrical Engineering, 919–26. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5341-7_69.

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Ebenezer Rajadurai, T., and C. Valliyammai. "Epileptic Seizure Prediction Using Weighted Visibility Graph." In Soft Computing Systems, 453–61. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1936-5_48.

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Conference papers on the topic "Epileptic Seizure"

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Burns, Samuel P., Sabato Santaniello, William S. Anderson, and Sridevi V. Sarma. "State Dynamics of the Epileptic Brain." In ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-3708.

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Communication between specialized regions of the brain is a dynamic process allowing for different connections to accomplish different tasks. While the content of interregional communication is complex, the pattern of connectivity (i.e., which regions communicate) may lie in a lower dimensional state-space. In epilepsy, seizures elicit changes in connectivity, whose patterns shed insight into the nature of seizures and the seizure focus. We investigated connectivity in 3 patients by applying network-based analysis on multi-day subdural electrocorticographic recordings (ECoG). We found that (i) the network connectivity defines a finite set of brain states, (ii) seizures are characterized by a consistent progression of states, and (iii) the focus is isolated from surrounding regions at the seizure onset and becomes most connected in the network towards seizure termination. Our results suggest that a finite-dimensional state-space model may characterize the dynamics of the epileptic brain, and may ultimately be used to localize seizure foci.
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Kumano, Saya, Takashi Saito, and Kenyu Uehara. "Influence of Brain Cooling on Frequency Characteristics of the Epileptic Focus and its Surrounding Area." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-87288.

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In this study, we examined the suppression effect of brain cooling on the epileptic focus and surrounding area. An epileptic seizure was induced in rats to obtain electrocorticography (ECoG) data when brain cooling was performed on the epileptic focus and its surroundings. Then, the frequency response characteristics were calculated by applying fast Fourier transform (FFT) and band pass filter to the obtained multichannel brain wave data. At this time, the frequency band calculated by the band pass filter was α waves (8.0–13.0Hz) and β waves (13.0–30.0 Hz) which were remarkably observed in epileptic seizure in the previous study, the analysis window of FFT was 4.095 seconds, and the overlap was 75%. As a result of comparing the calculated frequency responses for each rat, it was found that at the site where epileptic seizures were observed, power was reduced by cooling and suppressing effect was observed, whereas at the same time, the power increased at the site a few millimeters adjacent to the seizure site. This result suggests that epileptic waves suppressed by brain cooling might propagate to the surrounding area by a few millimeters.
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Silva, André Douglas Marinho da, Ana Caroline Fonseca Silva, Lucas Pablo Almendro, and Pedro da Cunha Dantas. "Non-epileptic seizure caused by selective serotonin reuptake inhibitors (SSRI) - case report." In XIII Congresso Paulista de Neurologia. Zeppelini Editorial e Comunicação, 2021. http://dx.doi.org/10.5327/1516-3180.258.

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Context: Seizures are the most frequent clinical emergency neurological manifestation, corresponding to 1-5% of the visits, except for trauma. Several conditions have the potential to reduce the seizure threshold, and the use of antidepressant drugs as selective serotonin reuptake inhibitors is one of those reported. The seizure triggering risk related to SSRIs use is low, being 0.1%, perceptibly lower than that of tricyclic antidepressants, with a 1% rate. Case report: Male patient, previously healthy, 23-year-old, was seen at the Emergency Room in Rio Branco after a generalized seizure lasting 3 minutes. Complementary exams, including computed tomography, were all normal. Magnetic resonance imaging of the skull without atypical findings and electroencephalogram showed dysrhythmia by waves and discrete spicules. Patient reported using escitalopram (esc) 20mg for 3 months after 10mg progression dose, in use for 1 year, without clinical improvement. Due to the seizure event, medication management was switched for sertraline 50mg intake. After 2 months, the patient had a new generalized seizure, preceded by prolonged depersonalization. Complementary exams were normal, 10mg of esc was reestablished and the patient ceased with the seizures. Conclusions: The diagnostic hypothesis: patient’s seizure threshold is low, and seizures are triggered by SSRI higher doses adverse effect. Due to case rarity and SSRI efficacy and tolerance, it is suggested to encourage the discussion about administration safety of these drugs.
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Anand, Satvika, and M. K. Mukul. "Early Epileptic Seizure Detection." In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, 2019. http://dx.doi.org/10.1109/icccnt45670.2019.8944429.

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Sedaghati, M., C. Chao, Z. Khan, and M. Bachan. "Psychogenic Nonepileptic Seizure Concealed a Focal Epileptic Seizure." In American Thoracic Society 2023 International Conference, May 19-24, 2023 - Washington, DC. American Thoracic Society, 2023. http://dx.doi.org/10.1164/ajrccm-conference.2023.207.1_meetingabstracts.a3529.

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Dorai, Arvind, and Kumaraswamy Ponnambalam. "Automated epileptic seizure onset detection." In 2010 International Conference on Autonomous and Intelligent Systems (AIS). IEEE, 2010. http://dx.doi.org/10.1109/ais.2010.5547053.

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Shekokar, Kishori, Shweta Dour, and Gufran Ahmad. "Epileptic Seizure Classification using LSTM." In 2021 8th International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2021. http://dx.doi.org/10.1109/spin52536.2021.9566118.

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Istiaq Ahsan, M. N., Csaba Kertesz, Annamaria Mesaros, Toni Heittola, Andrew Knight, and Tuomas Virtanen. "Audio-Based Epileptic Seizure Detection." In 2019 27th European Signal Processing Conference (EUSIPCO). IEEE, 2019. http://dx.doi.org/10.23919/eusipco.2019.8902840.

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Raiesdana, Somayeh, Mohammad R. Hashemi Golpayegani, and Ali M. Nasrabadi. "Complexity evolution in epileptic seizure." In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2008. http://dx.doi.org/10.1109/iembs.2008.4650113.

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Kamini, K. P., and R. Arthi. "Epileptic Seizure Prediction: A Review." In 2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART). IEEE, 2021. http://dx.doi.org/10.1109/smart52563.2021.9676229.

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Reports on the topic "Epileptic Seizure"

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Hamlin, Alexandra, Erik Kobylarz, James Lever, Susan Taylor, and Laura Ray. Assessing the feasibility of detecting epileptic seizures using non-cerebral sensor. Engineer Research and Development Center (U.S.), December 2021. http://dx.doi.org/10.21079/11681/42562.

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This paper investigates the feasibility of using non-cerebral, time-series data to detect epileptic seizures. Data were recorded from fifteen patients (7 male, 5 female, 3 not noted, mean age 36.17 yrs), five of whom had a total of seven seizures. Patients were monitored in an inpatient setting using standard video electroencephalography (vEEG), while also wearing sensors monitoring electrocardiography, electrodermal activity, electromyography, accelerometry, and audio signals (vocalizations). A systematic and detailed study was conducted to identify the sensors and the features derived from the non-cerebral sensors that contribute most significantly to separability of data acquired during seizures from non-seizure data. Post-processing of the data using linear discriminant analysis (LDA) shows that seizure data are strongly separable from non-seizure data based on features derived from the signals recorded. The mean area under the receiver operator characteristic (ROC) curve for each individual patient that experienced a seizure during data collection, calculated using LDA, was 0.9682. The features that contribute most significantly to seizure detection differ for each patient. The results show that a multimodal approach to seizure detection using the specified sensor suite is promising in detecting seizures with both sensitivity and specificity. Moreover, the study provides a means to quantify the contribution of each sensor and feature to separability. Development of a non-electroencephalography (EEG) based seizure detection device would give doctors a more accurate seizure count outside of the clinical setting, improving treatment and the quality of life of epilepsy patients.
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Hively, LM. Epileptic Seizure Forewarning by Nonlinear Techniques. Office of Scientific and Technical Information (OSTI), February 2001. http://dx.doi.org/10.2172/814360.

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Hively, L. M. Epileptic Seizure Forewarning by Nonlinear Techniques. Office of Scientific and Technical Information (OSTI), April 2002. http://dx.doi.org/10.2172/814483.

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Elarton, J., and K. Koepsel. Epileptic Seizure Detection & Warning Device. Office of Scientific and Technical Information (OSTI), June 1999. http://dx.doi.org/10.2172/7856.

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Shujaa, Asaad Suliman, and Qasem Almulihi. The efficacy and safety of ketamine in treating refractory and super-refractory status epilepticus in pediatric and adult populations, A systemic review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2022. http://dx.doi.org/10.37766/inplasy2022.11.0011.

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Review question / Objective: This study is to assess the efficacy and safety of ketamine in treating refractory and super-refractory status epilepticus in pediatric and adult populations. Rationale: Refractory status epilepticus (RSE) is either generalized or complex partial status epilepticus (SE) that fails to respond to first and second-line therapies. Super refractory status epilepticus (SRSE) is SE that remains unresponsive despite 24 hours of therapy with general anesthesia [1, 2]. Both RSE and SRSE pose significant challenges for the managing intensivist. There exists a race against time for control of epileptic activity in the RSE/SRSE patient to preserve cortical function and reduce morbidity/mortality. However, despite the best intentions, and not uncommonly, standard frontline antiepileptic drugs (AEDs) fail to control or reduce seizure activity once seizures approach the 30-minute mark. The following review provides an analysis of ketamine in treating RSE/SRSE, focusing on the potential target population, dosing, concerns, and the role of early administration.
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Gerster, Moritz, Rico Berner, Jakub Sawicki, Anna Zakharova, Antonín Škoch, Jaroslav Hlinka, Klaus Lehnertz, and Eckehard Schöll. FitzHugh–Nagumo oscillators on complex networks mimic epileptic-seizure-related synchronization phenomena. Peeref, May 2023. http://dx.doi.org/10.54985/peeref.2305p6428893.

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Xin, Wu, and Xue Tao. The efficacy and safety of neuromodulation in refractory epilepsy: a systematic review and network meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, April 2022. http://dx.doi.org/10.37766/inplasy2022.4.0042.

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Review question / Objective: To assess the efficacy and safety of different neuromodulation applied to the refractory epilepsy and provide a better choice for clinical practice. Condition being studied: Epilepsy is a frequent neurologic illness defined by bursts of hypersynchronized neural network activity that afflict about 1% of the global population. Unfortunately, roughly 30% of people with drug-resistant epilepsy (DRE) continue to experience seizures despite three anti-seizure drugs. In most cases, resective surgery, as the first-line treatment for DRE, is considered a curative therapy for achieving long-term seizure-free status, but about half of patients are not candidates for surgery due to a variety of factors such as multiple/diffuse/widespread seizure foci, epileptic foci arising from eloquent, primary generalized epilepsy, or patients unwilling to undergo surgery. Neuromodulation, albeit palliative, is an important alternative treatment for these individuals to prevent or decrease ictal episodes, which can affect the nervous system in a variety of ways.
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Hively, L. M., N. E. Clapp, C. S. Daw, W. F. Lawkins, and M. L. Eisenstadt. Nonlinear analysis of EEG for epileptic seizures. Office of Scientific and Technical Information (OSTI), April 1995. http://dx.doi.org/10.2172/366563.

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Gopalsami, Nachappa. Brain Implants for Prediction and Mitigation of Epileptic Seizures - Final CRADA Report. Office of Scientific and Technical Information (OSTI), September 2016. http://dx.doi.org/10.2172/1331319.

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Stefanova, Irina, Rumyana Kuzmanova, Sevda Naydenska, and Katerina Stambolieva. Character of Epileptic Seizures and Electroencephalographic Changes in Patients with Epilepsy and Comorbid Diseases. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, July 2018. http://dx.doi.org/10.7546/crabs.2018.07.16.

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