Unpredictability of Seizures and the Burden of Epilepsy

Schulze‐Bonhage, Andreas; Kühn, Anne

doi:10.1002/9783527625192.ch1

Cited by 42 publications

(37 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Resective surgery 3 and neurostimulation 4,5 can be effective treatments if medications fail, but for many patients seizures persist despite these procedures. Further, a significant aspect of epilepsy-related disability beyond the occurrence of the seizures is their unpredictability 6–8 . One consequence is that patients taking medication typically do so daily, and incur considerable side effects to prevent a condition that may happen only a few times per year 2,9,10 .…”

Section: Introductionmentioning

confidence: 99%

Seizure Forecasting and the Preictal State in Canine Epilepsy

Varatharajah

Iyer

Berry

et al. 2016

Int. J. Neur. Syst.

View full text Add to dashboard Cite

The ability to predict seizures may enable patients with epilepsy to better manage their medications and activities, potentially reducing side effects and improving quality of life. Forecasting epileptic seizures remains a challenging problem, but machine learning methods using intracranial electroencephalographic (iEEG) measures have shown promise. A machine-learning-based pipeline was developed to process iEEG recordings and generate seizure warnings. Results support the ability to forecast seizures at rates greater than a Poisson random predictor for all feature sets and machine learning algorithms tested. In addition, subject-specific neurophysiological changes in multiple features are reported preceding lead seizures, providing evidence supporting the existence of a distinct and identifiable preictal state.

show abstract

Section: Introductionmentioning

confidence: 99%

Seizure Forecasting and the Preictal State in Canine Epilepsy

Varatharajah

Iyer

Berry

et al. 2016

Int. J. Neur. Syst.

View full text Add to dashboard Cite

show abstract

“…The unpredictability of seizures remains one of the most challenging aspects of epilepsy (Murray, ; Fisher, ). Simply knowing when a seizure is coming, may in itself, reduce the burden of unpredictability and improve health‐related quality of life (Schulze‐Bonhage & Buller, ). For the most part, efforts to predict seizures have relied on electroencephalography (EEG) data, although the concept of self‐prediction of seizures by persons with epilepsy has been the focus of increasing research and discussion (Spector et al., ; Lee & No, ; Schulze‐Bonhage et al., ; Haut et al., ; Dionisio & Tatum, ; DuBois et al., ).…”

mentioning

confidence: 99%

Modeling seizure self‐prediction: An e‐diary study

et al. 2013

View full text Add to dashboard Cite

Purpose 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. Methods Subjects 18 or older with LRE and ≥3 seizures/month maintained an e-diary, reporting AM/PM 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 (OR) within time frames was examined using Poisson models with log normal random effects to adjust for multiple observations. Key Findings Nineteen subjects reported 244 eligible seizures. OR for prediction choices within 6hrs was as high as 9.31 (1.92,45.23) for “almost certain”. Prediction was most robust within 6hrs of diary entry, and remained significant up to 12hrs. For 9 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; 1.68,4.81), favorable change in mood (0.82; 0.67,0.99) and number of premonitory symptoms (1,11; 1.00,1.24) were significant. Significance 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-hour prediction window is suitable for the development of pre-emptive therapy.

show abstract

“…A system capable of alerting patients to approaching seizures could make a considerable contribution to improving their wellbeing (Schulze-Bonhage and Kühn, 2008). Such a system could be an implantable device that 'silently' abates seizures by altering their generation mechanism in response to warnings.…”

Section: Introductionmentioning

confidence: 99%

Seizure prediction in patients with mesial temporal lobe epilepsy using EEG measures of state similarity

Gadhoumi

Lina

Gotman

2013

Clinical Neurophysiology

View full text Add to dashboard Cite

Objectives-In patients with intractable epilepsy, predicting seizures above chance and with clinically acceptable performance has yet to be demonstrated. In this study, an intracranial EEGbased seizure prediction method using measures of similarity with a reference state is proposed.Methods-1565 h of continuous intracranial EEG data from 17 patients with mesial temporal lobe epilepsy were investigated. The recordings included 175 seizures. In each patient the data was split into a training set and a testing set. EEG segments were analyzed using continuous wavelet transform. During training, a reference state was defined in the immediate preictal data and used to derive three features quantifying the discrimination between preictal and interictal states. A classifier was then trained in the feature space. Its performance was assessed using testing set and compared with a random predictor for statistical validation.Results-Better than random prediction performance was achieved in 7 patients. The sensitivity was higher than 85%, the warning rate was less than 0.35/h and the proportion of time under warning was less than 30%.Conclusion-Seizures are predicted above chance in 41% of patients using measures of state similarity.Significance-Sensitivity and specificity levels are potentially interesting for closed-loop seizure control applications.

show abstract

Unpredictability of Seizures and the Burden of Epilepsy

Cited by 42 publications

References 70 publications

Seizure Forecasting and the Preictal State in Canine Epilepsy

Seizure Forecasting and the Preictal State in Canine Epilepsy

Modeling seizure self‐prediction: An e‐diary study

Seizure prediction in patients with mesial temporal lobe epilepsy using EEG measures of state similarity

Contact Info

Product

Resources

About