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Epileptic seizure

About: Epileptic seizure is a research topic. Over the lifetime, 3062 publications have been published within this topic receiving 66944 citations.


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Journal ArticleDOI
TL;DR: The International League Against Epilepsy (ILAE) and the International Bureau for Epilepsia (IBE) have come to consensus definitions for the terms epileptic seizure and epilepsy.
Abstract: The International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE) have come to consensus definitions for the terms epileptic seizure and epilepsy. An epileptic seizure is a transient occurrence of signs and/or symptoms due to abnormal excessive or synchronous neuronal activity in the brain. Epilepsy is a disorder of the brain characterized by an enduring predisposition to generate epileptic seizures and by the neurobiologic, cognitive, psychological, and social consequences of this condition. The definition of epilepsy requires the occurrence of at least one epileptic seizure.

2,201 citations

Journal ArticleDOI
TL;DR: The present study focuses on EPILEPTIC SEIZURE SEMIOLOGY, a subset of Epilepsy Research, which aims to clarify the meaning of “seizure” and “disruption” in order to facilitate diagnosis and treatment.
Abstract: INTRODUCTION PRINCIPLES FOR TERMS AND DEFINITIONS DATA SOURCES I GENERAL TERMS 1.0 SEMIOLOGY 2.0 EPILEPTIC SEIZURE 3.0 ICTUS 4.0 EPILEPSY 5.0 FOCAL 6.0 GENERALIZED 7.0 CONVULSION II TERMS DESCRIBING EPILEPTIC SEIZURE SEMIOLOGY 1.0 MOTOR 1.1 ELEMENTARY MOTOR 1.1.1 TONIC 1.1.1.1 EPILEPTIC SPASM 1.1.1.2 POSTURAL 1.1.1.2.1 VERSIVE 1.1.1.2.2 DYSTONIC 1.1.2 MYOCLONIC 1.1.2.1 NEGATIVE MYOCLONIC 1.1.2.2 CLONIC 1.1.2.2.1 JACKSONIAN MARCH 1.1.3 TONIC-CLONIC 1.1.3.1 GENERALIZED TONIC-CLONIC SEIZURE 1.1.4 ATONIC 1.1.5 ASTATIC 1.1.6 SYNCHRONOUS 1.2 AUTOMATISM 1.2.1 OROALIMENTARY 1.2.2 MIMETIC 1.2.3 MANUAL OR PEDAL 1.2.4 GESTURAL 1.2.5 HYPERKINETIC 1.2.6 HYPOKINETIC 1.2.7 DYSPHASIC 1.2.8 DYSPRAXIC 1.2.9 GELASTIC 1.2.10 DACRYSTIC 1.2.11 VOCAL 1.2.12 VERBAL 1.2.13 SPONTANEOUS 1.2.14 INTERACTIVE 2.0 NON-MOTOR 2.1 AURA 2.2 SENSORY 2.2.1 ELEMENTARY 2.2.1.1 SOMATOSENSORY 2.2.1.2 VISUAL 2.2.1.3 AUDITORY 2.2.1.4 OLFACTORY 2.2.1.5 GUSTATORY 2.2.1.6 EPIGASTRIC 2.2.1.7 CEPHALIC 2.2.1.8 AUTONOMIC 2.2.2 EXPERIENTIAL 2.2.2.1 AFFECTIVE 2.2.2.2 MNEMONIC 2.2.2.3 HALLUCINATORY 2.2.2.4 ILLUSORY 2.3 DYSCOGNITIVE 3.0 AUTONOMIC EVENTS 3.1 AUTONOMIC AURA 3.2 AUTONOMIC SEIZURE 4.0 SOMATOTOPIC MODIFIERS 4.1 LATERALITY 4.1.1 UNILATERAL 4.1.1.1 HEMI4.1.2 GENERALIZED (syn. “bilateral”) 4.1.2.1 ASYMMETRICAL 4.1.2.2 SYMMETRICAL 4.2 BODY PART 4.3 CENTRICITY 4.3.1 AXIAL Epilepsia, 42(9):1212–1218, 2001 Blackwell Science, Inc. © International League Against Epilepsy

787 citations

Journal ArticleDOI
TL;DR: In this article, a 10-member subcommission of the Commission on Therapeutic Strategies of The International League Against Epilepsy (ILAE) evaluated available evidence found through a structured literature review including MEDLINE, Current Contents and the Cochrane Library for all applicable articles from 1940 until July 2005.
Abstract: Summary: Purpose: To assess which antiepileptic medications (AEDs) have the best evidence for long-term efficacy or effectiveness as initial monotherapy for patients with newly diagnosed or untreated epilepsy. Methods: A 10-member subcommission of the Commission on Therapeutic Strategies of The International League Against Epilepsy (ILAE), including adult and pediatric epileptologists, clinical pharmacologists, clinical trialists, and a statistician evaluated available evidence found through a structured literature review including MEDLINE, Current Contents and the Cochrane Library for all applicable articles from 1940 until July 2005. Articles dealing with different seizure types (for different age groups) and two epilepsy syndromes were assessed for quality of evidence (four classes) based on predefined criteria. Criteria for class I classification were a double-blind randomized controlled trial (RCT) design, ≥48-week treatment duration without forced exit criteria, information on ≥24-week seizure freedom data (efficacy) or ≥48-week retention data (effectiveness), demonstration of superiority or 80% power to detect a ≤20% relative difference in efficacy/effectiveness versus an adequate comparator, and appropriate statistical analysis. Class II studies met all class I criteria except for having either treatment duration of 24 to 47 weeks or, for noninferiority analysis, a power to only exclude a 21–30% relative difference. Class III studies included other randomized double-blind and open-label trials, and class IV included other forms of evidence (e.g., expert opinion, case reports). Quality of clinical trial evidence was used to determine the strength of the level of recommendation. Results: A total of 50 RCTs and seven meta-analyses contributed to the analysis. Only four RCTs had class I evidence, whereas two had class II evidence; the remainder were evaluated as class III evidence. Three seizure types had AEDs with level A or level B efficacy and effectiveness evidence as initial monotherapy: adults with partial-onset seizures (level A, carbamazepine and phenytoin; level B, valproic acid), children with partial-onset seizures (level A, oxcarbazepine; level B, None), and elderly adults with partial-onset seizures (level A, gabapentin and lamotrigine; level B, None). One adult seizure type [adults with generalized-onset tonic–clonic (GTC) seizures], two pediatric seizure types (GTC seizures and absence seizures), and two epilepsy syndromes (benign epilepsy with centrotemporal spikes and juvenile myoclonic epilepsy) had no AEDs with level A or level B efficacy and effectiveness evidence as initial monotherapy. Conclusions: This evidence-based guideline focused on AED efficacy or effectiveness as initial monotherapy for patients with newly diagnosed or untreated epilepsy. The absence of rigorous comprehensive adverse effects data makes it impossible to develop an evidence-based guideline aimed at identifying the overall optimal recommended initial-monotherapy AED. There is an especially alarming lack of well-designed, properly conducted RCTs for patients with generalized seizures/epilepsies and for children in general. The majority of relevant existing RCTs have significant methodologic problems that limit their applicability to this guideline's clinically relevant main question. Multicenter, multinational efforts are needed to design, conduct and analyze future clinically relevant RCTs that can answer the many outstanding questions identified in this guideline. The ultimate choice of an AED for any individual patient with newly diagnosed or untreated epilepsy should include consideration of the strength of the efficacy and effectiveness evidence for each AED along with other variables such as the AED safety and tolerability profile, pharmacokinetic properties, formulations, and expense. When selecting a patient's AED, physicians and patients should consider all relevant variables and not just efficacy and effectiveness.

704 citations

Journal ArticleDOI
01 Sep 2009
TL;DR: The suitability of the time-frequency ( t-f) analysis to classify EEG segments for epileptic seizures, and several methods for t- f analysis of EEGs are compared.
Abstract: The detection of recorded epileptic seizure activity in EEG segments is crucial for the localization and classification of epileptic seizures. However, since seizure evolution is typically a dynamic and nonstationary process and the signals are composed of multiple frequencies, visual and conventional frequency-based methods have limited application. In this paper, we demonstrate the suitability of the time-frequency ( t-f) analysis to classify EEG segments for epileptic seizures, and we compare several methods for t- f analysis of EEGs. Short-time Fourier transform and several t-f distributions are used to calculate the power spectrum density (PSD) of each segment. The analysis is performed in three stages: 1) t-f analysis and calculation of the PSD of each EEG segment; 2) feature extraction, measuring the signal segment fractional energy on specific t-f windows; and 3) classification of the EEG segment (existence of epileptic seizure or not), using artificial neural networks. The methods are evaluated using three classification problems obtained from a benchmark EEG dataset, and qualitative and quantitative results are presented.

658 citations

Dissertation
01 Jan 2009
TL;DR: The feasibility of using the algorithm to control the Vagus Nerve Stimulator, to initiate ictal SPECT (a functional neuroimaging modality useful for localizing the cerebral site of origin of a seizure), and to enable delay-sensitive therapeutic and diagnostic applications are demonstrated.
Abstract: 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. In this thesis we also discuss how our detector can be embedded within a lowpower, 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 neurostimulator 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). Thesis Supervisor: John V. Guttag Title: Professor of Electrical Engineering and Computer Science

648 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023122
2022282
2021211
2020235
2019232
2018180