The unpredictability of seizures remains one of the most challenging aspects of epilepsy (Murray 1993, Fisher 2000). 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, 2008). For the most part, efforts to predict seizures have relied on 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 2000, Lee & No 2005, Schulze-Bonhage et al 2006, Haut et al 2007a, DuBois et al 2010, Dionisio et al 2010). In questionnaire studies, many patients report a “pre-seizure state” characterized by prodromal or premonitory symptoms (Hughes et al 1993, Rajna et al 1997, Lee & No 2005, Schulze-Bonhage et al 2006, Petitmengin et al 2006, Scaramelli et al 2009); more recently, prodromes and seizure self-prediction have been investigated in prospective studies (Haut et al 2007a, Dubois et al 2010, Maiwald et al 2011).

In a paper diary study, we showed that a subset of patients with localization-related epilepsy (LRE) successfully predicted their seizures over a 24 hour window (Haut et al, 2007a). We conceptualize seizure self-prediction as a conscious or subconscious awareness of prodromal features, trigger factors, and possibly unmeasured variables such as state correlates of electrophysiological changes.

To further explore the nature of clinical seizure self-prediction, we conducted an e-diary study that is the basis of the present report. We also included an extensive inventory of trigger factors, premonitory symptoms and measures of mood, expanding our ability to characterize the pre-ictal state. Based on this data, we reported clinical features of the pre-ictal state, demonstrating that mood changes and premonitory features predicted seizure occurrence over 12 hours (Haut et al 2012).

Our primary aim in the present report is to confirm clinical seizure self-prediction utilizing electronic data capture to provide time stamped data collection, reducing the potential for retrospective reporting and recall bias. Furthermore, because of the collection of exposure data twice daily and the time-stamped reporting of seizure onset, we are in a strong position to explore a number of secondary aims, including: define time frames of seizure occurrence following self-prediction; assess self-prediction as an outcome in its own right, independent of accuracy; identify components of self-prediction and ultimately to improve its’ accuracy; and finally, determine the separate and joint effect of seizure self-prediction, mood and change in mood, as well as premonitory features on the subsequent occurrence of seizures. Insights into the predictability of seizures could lead to a novel approach to epilepsy treatment, namely preemptive therapy during the pre-ictal state.

The 19 subjects were predominantly female (84%), had a median age of 35 years with mean duration of epilepsy 16.1 years. Median frequency was 3.5 seizures per 30 days. Epilepsy localization was temporal (n=14); frontal (n=1); extratemporal other (n=2) and non-localizable (n=2).

Diaries were completed for a median of 103 days (range 50–151). Subjects provided 1680 AM entries, 1594 PM entries, and reported 258 seizures. Fourteen seizures were excluded: Five occurred as a first diary entry with no preceding diary data; nine occurred >24 hrs after last diary entry due to missed diaries. Thus the analyses presented were performed on of the remaining 244 seizures.

This study demonstrates that 9 of 19 (43%) participants with refractory LRE were able to accurately predict their seizures, drawing on awareness of prodromal features such as mood and premonitory symptoms. Self -prediction was more accurate in participants who were more confident in the accuracy of their predictions. For the most confident prediction choices, the odds of seizure increased more than 8 fold compared to times when seizures were thought to be “very unlikely” in unadjusted models. Self-prediction was most robust for prediction windows of 6 hours or less, remaining highly significant over 12 hours but not for longer time frames.

These results confirm and extend findings from our previous paper diary study with nightly measures (Haut et al 2007a). The present report is more robust and informative because the electronic diary format provides time stamping, because exposures were captured twice daily and because we included detailed inventories of mood and premonitory features. We also show that elements of the prodromal state play a large role in seizure self-prediction and ultimately, in accurate modeling of seizure occurrence.

Identifying the elements that contribute to seizure self-prediction offers the possibility of both understanding and improving self-prediction (Figure 1a). The most significant variables associated with self-prediction were favorable mood and number of reported premonitory features. As both of these elements are relatively easy for patients to attend to and record, this observation offers the promise of improved self-prediction with use of education and training.

Mood and stress are reported to be among the strongest seizure precipitants in both questionnaire and prospective diary studies (Neugebauer et al 1994, Spector et al 2000, Nakken et al 2005, Haut et al 2007b, Sperling et al 2008, Haut et al 2012). In this e-diary study, current mood and not change in mood influenced self-prediction in multivariate models, while change in mood was associated with actual seizure occurrence. Training patients to be more aware of mood change from one day to the next might improve their ability to self-predict seizures accurately and yield more powerful models of seizure probability (Figure 1b).

Premonitory symptoms make a strong contribution to self-prediction, which similarly offers opportunities to train patients on their own symptoms. Of note, premonitory features have been examined in a number of studies to date with conflicting results (Schulze-Bonhage et al 2006, Maiwald et al 2011, Haut et al 2012), as was discussed in a recent review (Schulze-Bonhage & Haut, 2011).

In the modeling of seizure occurrence, self-prediction, favorable change in mood and premonitory features remain independent predictors (Figure 1b). The OR for the associations in multivariate modeling suggest that self-prediction and premonitory symptoms both contribute to accurately assessing the probability of seizure occurrence.

Significant seizure self-prediction has been similarly reported in the inpatient epilepsy monitoring setting (DuBois et al 2010). Developing seizure self-prediction and seizure occurrence models may have important clinical implications. If “at risk” seizure states can be identified, interventions can range from taking precautionary measures to the actual use of pre-emptive therapies. Pre-emptive treatment will rely on robust modeling of seizure probability, of which seizure self-prediction may be a significant contributor. There is no evidence-based approach for pre-emptive therapy in adult epilepsy, although in practice clinicians may prescribe oral benzodiazepines for use in certain settings. If clinically based seizure prediction becomes more robust, candidate pre-emptive treatments might include short-term use of benzodiazepines, or even supplemental anti-epileptic medications. The association between mood and prediction suggests the possibility of utilizing a behavioral intervention during periods of increased seizure risk. In fact, a randomized controlled e-diary trial of a behavioral intervention is currently being conducted (Polak et al 2012).

As in other studies (Haut et al 2007a, DuBois et al 2010), predictive ability was not uniformly distributed among patients. The current cohort was enriched with subjects who described perceived self-predictive ability and/or awareness of precipitants, and almost 50% of the subjects demonstrated significant self- prediction. This percentage is much higher than in our previous study where 21% of the subjects were significant predictors. Here, older age was associated with better predictive ability, in contrast to our prior study where younger patients were better predictors (Haut et al 2007a). DuBois et al (2010) reported that subjects with a longer duration of epilepsy were better at predicting “no-seizure” days. The current findings support the concept that longer experience with seizures is associated with more accurate prediction. DuBois et al (2010) also found that higher seizure rates were associated with better prediction which was not the case in our current study. This disparity may well relate to differences between outpatient and inpatient seizure frequencies.

Is self-prediction and seizure modeling ready for clinical use? Seizure self-prediction has a very high specificity (Haut et al 2007a, Dubois et al 2010), reflecting the accuracy of negative predictions. Successful negative prediction is important for pre-emption; if the intervention carries any risk, this will limit unnecessary treatment. A clinically relevant pre-emptive therapy also requires high sensitivity. In the group of predictors, median sensitivity and specificity were 50% and 95%. These numbers, while sufficient for a behavioral intervention, will not support a pre-emptive pharmacologic trial, but may be improved with training.

Our study has certain limitations. Our primary outcome measure is the occurrence of self-reported seizures as recorded using an electronic diary. This approach is vulnerable to errors of both under-reporting or over-reporting of seizures (Neugebauer 1989, Blum et al 1996, Tatum et al 2001, Hoppe et al 2007, Cook et al 2013).

The accuracy of self-reported seizures is a concern, as recently reported in a long-term study using implanted electrodes, where disparities between reports of seizures in patient diaries and electrographic seizure patterns on EEG reached statistical significant in almost a third of subjects (Cook et al 2013). Though this area requires additional attention, continuous EEG monitoring is rarely available. As a consequence, we will continue to rely on self-report both in clinical trials and clinical practice for the foreseeable future. However, unless errors in seizure reporting are associated with the exposures of interest, we would expect our reported associations to get stronger with perfect reporting of seizure occurrence.

Another challenge in a seizure self-prediction study is that patients may be predicting a seizure during their aura, reporting the “ictal” and not “pre-ictal” state. Again, absent EEG monitoring, this possibility cannot be completely ruled out. However, the most accurate prediction window of this study was 4–6 hours after a self-prediction, whereas a reported seizure would be expected to follow an aura report by minutes. Finally, although the number of subjects is modest we had over 3 thousand diary days and almost 250 seizures. The positive results support our feeling that the sample size is appropriate to confirm seizure self-prediction using electronic data capture.

There remains modeling evidence that as yet unmeasured variables are contributing to seizure self-prediction. These variables may represent other biological phenomenon that patients recognize as heralding a seizure, for example self-awareness of electrophysiological changes. A follow up study that includes continuous EEG monitoring, while logistically challenging, would likely clarify the phenomenon of self-prediction even further.

Our data confirms our previous findings that seizure self-prediction is possible for a subgroup of patients with epilepsy, and that in these individuals, the odds of a seizure following a positive prediction is high. While these findings may only be generalizable to patients who report either self-predictive ability or awareness of seizure precipitants to their clinicians, prevalence studies indicate that this may be a substantial subgroup. Improvement in predictive ability will be necessary for a planned pre-emptive trial; this may be accomplished with education and training individuals on their own data, focusing on features of the prodromal state such as premonitory symptoms, and change in mood. Ultimately, quantitative EEG analysis may also be utilized in combination with self-prediction, to enhance the effectiveness of both techniques. We anticipate that this work may represent a step towards a new paradigm of treatment, namely pre-emptive therapy for epilepsy.