Premonitory features and seizure self-prediction: Artifact or real?
ABSTRACT Seizure prediction is currently largely investigated by means of EEG analyses. We here report on evidence available on the ability of epilepsy patients themselves to predict seizures either by means of subjective experiences ("prodromes"), apparent awareness of precipitants, or a feeling of impending seizure (self-prediction). These data have been collected prospectively by paper or electronic diaries. Whereas evidence for a predictive value of prodromes is missing, some patients nevertheless can forsee impending seizures above chance level. Relevant cues and practical implications are discussed.
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ABSTRACT: Identifying the pre-ictal state clinically would improve our understanding of seizure onset and suggest opportunities for new treatments. In our previous paper-diary study, increased stress and less sleep predicted seizures. Utilizing electronic diaries, we expanded this investigation. Variables were identified by their association with subsequent seizure using logit-normal random effects models fit by maximum likelihood. Nineteen subjects with localization-related epilepsy kept e-diaries for 12-14 weeks and reported 244 eligible seizures. In univariate models, several mood items and ten premonitory features were associated with increased odds of seizure over 12h. In multivariate models, a 10-point improvement in total mood decreased seizure risk by 25% (OR 0.75, CI 0.61-0.91, p=004) while each additional significant premonitory feature increased seizure risk by nearly 25% (OR 1.24, CI 1.13-1.35, p<001) over 12h. Pre-ictal changes in mood and premonitory features may predict seizure occurrence and suggest a role for behavioral intervention and pre-emptive therapy in epilepsy.Epilepsy & Behavior 03/2012; 23(4):415-21. DOI:10.1016/j.yebeh.2012.02.007 · 2.06 Impact Factor
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ABSTRACT: Great effort has been made toward defining and characterizing the pre-ictal state. Many studies have pursued the idea that there are recognizable electrographic (EEG-based) features which occur before overt clinical seizure activity. However, development of reliable EEG-based seizure detection and prediction algorithms has been difficult. In this review, we discuss the concepts of seizure detection vs. prediction and the pre-ictal "clinical milieu" and "EEG milieu". We proceed to discuss novel concepts of seizure detection based on the pre-ictal "physiological milieu"; in particular, we indicate some early evidence for the hypothesis that pre-ictal cell swelling/extracellular space constriction can be detected with novel optical methods. Development and validation of optical seizure detection technology could provide an entirely new translational approach for the many patients with intractable epilepsy. This article is part of a Special Issue entitled "Translational Epilepsy Research".Epilepsy & Behavior 12/2012; 26(3). DOI:10.1016/j.yebeh.2012.10.027 · 2.06 Impact Factor
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ABSTRACT: A subset of patients with epilepsy successfully self-predicted seizures in a paper diary study. We conducted an e-diary study to ensure that prediction precedes seizures, and to characterize the prodromal features and time windows that underlie self-prediction. Subjects 18 or older with localization-related epilepsy (LRE) and ≥3 seizures per month maintained an e-diary, reporting a.m./p.m. data daily, including mood, premonitory symptoms, and all seizures. Self-prediction was rated by, "How likely are you to experience a seizure (time frame)?" Five choices ranged from almost certain (>95% chance) to very unlikely. Relative odds of seizure (odds ratio, OR) within time frames was examined using Poisson models with log normal random effects to adjust for multiple observations. Nineteen subjects reported 244 eligible seizures. OR for prediction choices within 6 h was as high as 9.31 (CI 1.92-45.23) for "almost certain." Prediction was most robust within 6 h of diary entry, and remained significant up to 12 h. For nine best predictors, average sensitivity was 50%. Older age contributed to successful self-prediction, and self-prediction appeared to be driven by mood and premonitory symptoms. In multivariate modeling of seizure occurrence, self-prediction (2.84; CI 1.68-4.81), favorable change in mood (0.82; CI 0.67-0.99), and number of premonitory symptoms (1.11; CI 1.00-1.24) were significant. Some persons with epilepsy can self-predict seizures. In these individuals, the odds of a seizure following a positive prediction are high. Predictions were robust, not attributable to recall bias, and were related to self-awareness of mood and premonitory features. The 6-h prediction window is suitable for the development of preemptive therapy.Epilepsia 09/2013; 54(11). DOI:10.1111/epi.12355 · 4.58 Impact Factor