Seizure prediction, anticipation, or forecasting (despite their different meanings, these terms are currently used interchangeably) is a field of great interest in the clinical and basic neuroscience communities. This is not only because of its potential clinical application in warning and therapeutic antiepileptic devices, but also for its promise to increase our understanding of the mechanisms underlying epilepsy and seizure generation. The motivation for research into the predictability of seizures is straightforward. The fact that seizures occur without warning in the majority of cases is one of the most disabling aspects of epilepsy. If it were possible to predict seizures with high sensitivity and specificity, even seconds before their onset, therapeutic possibilities would change dramatically.⁵⁰ One might envision a simple warning system capable of decreasing both the risk of injury and the feeling of helplessness that results from seemingly unpredictable seizures. Side effects from treatment with antiepileptic drugs, such as sedation and clouded thinking, could be reduced by on-demand release of a short-acting drug^48,197 or electrical stimulation^62,175,213 during the pre-ictal state. Paired with other suitable interventions, such as focal cooling⁷⁰ or biofeedback operant conditioning,^56,170,198 such applications could reduce morbidity and mortality, and greatly improve the quality-of-life for people with epilepsy. In addition, identifying a pre-ictal state would greatly contribute to our understanding of the pathophysiologic mechanisms that generate seizures.

In most patients seizures appear to occur unpredictably, with no discernible pattern, while in others, seizures appear to be entrained to biologic rhythms, such as menstrual or sleep–wake cycles. Clustering patterns, where one seizure appears to increase the likelihood of subsequent seizures, also appear common in clinical practice. Despite these observations, analyses of long-term patterns based upon seizure diaries^{4,11,16,74,115,147,204} have yielded inconsistent findings. While some authors conclude that the timing of seizure recurrence is random, others hypothesize that seizures occur in a probabilistic nonlinear fashion. Because of this inconsistency, based upon clinical observations, the transition to a seizure has generally been thought of as an abrupt phenomenon, occurring without warning. Nevertheless, there is physiologic support for the idea that at least certain types of seizures are predictable.

Several seizure-facilitating factors are known. Lennox¹²⁵ defined seizure facilitation as the input of sensory, metabolic, emotional, or other yet unknown factors that “fill up some reservoir until it overflows,” which in turn results in a seizure. State of consciousness, sleep deprivation, being tense, disturbances of electrolytes and acid–base balance, sensory stimulation, and exposure to certain drugs are factors known to potentiate seizures. Apart from the rare exception of sensory-evoked or reflex epilepsies, however, these factors are rather nonspecific and highly variable, since they depend on individual habits, susceptibility, and daily routine.

Clinicians who care for patients with epilepsy have long known that individual patients can identify periods when seizures are more likely to occur, though they can rarely specify an exact time when seizures will happen. Rajna et al.¹⁷⁴ found that the vague sensations that characterize these periods, called “clinical prodromes,” occurred in more than 50% of 562 investigated patients, though the reliability of these reports was not evaluated prospectively. Reported sensations included mood changes, irritability, sleep problems, nausea, and headache. There are also physiologic studies in small numbers of patients, usually collected serendipitously before seizures, that support the existence of a pre-ictal period. Weinand et al.²¹⁶ detected a significant increase in blood flow in the epileptic temporal lobe that started 10 minutes before seizure onset that spread to both temporal lobes 2 minutes before seizure onset. Similarly, Baumgartner et al.¹⁴ demonstrated increased blood flow in the epileptic temporal lobe in two patients, 11 and 12 minutes, respectively, before seizure onset. Using near-infrared spectroscopy in three patients, Adelson et al.² reported an increase in cerebral oxygen availability that began more than 13.5 hours, and was identified as early as 1.5 hours, before documented seizure onset. Pre-ictal changes in other variables, such as R–R interval on the electrocardiogram (ECG)^40,95,159 may also have predictive value, perhaps as epiphenomena related to seizure precursors, in some types of epilepsy. More recently, functional magnetic resonance imaging has demonstrated changes in perfusion prior to seizure onset.⁵⁵

During recent years, a variety of potential ictogenic (seizure-generating) mechanisms have been identified in experimental models of focal epilepsy, including alterations in synaptic and cellular plasticity and changes in the extracellular milieu (see Section II). However, it is still a matter of debate whether these mechanisms can be regarded as specifically ictogenic, apart from their critical role in normal brain function. On the level of neuronal networks, focal seizures are assumed to be initiated by abnormally discharging neurons (so-called bursters^{21,22,34,182,208}; see reference 226 for an overview) that recruit and entrain neighboring neurons into a critical mass. This build-up might be mediated by an increasing synchronization of neuronal activity that is accompanied by a loss of inhibition, or by processes that facilitate seizures by lowering the threshold for excitation or synchronization. In this context the term “critical mass” might be misleading in the sense that it implies an increasing number of neurons that are entrained into an abnormal firing pattern. This mass phenomenon would be easily accessible to conventional electroencephalogram (EEG) analysis, which, to date, has failed to detect it. Rather, the seizure-initiating process might better be visualized as a process in which an increasing number of critical interactions between neurons in a focal region and connected units in an abnormal functional network unfold over time. Indeed, there is now converging evidence from different laboratories that quantitative analyses appears to be capable of characterizing this collective neuronal behavior from the gross EEG, allowing definition of a transitional pre-ictal phase, in a high percentage of cases.

As early as 1975, researchers considered analysis techniques such as pattern recognition, analytic procedures of spectral data,^44,189,214 or autoregressive modeling of EEG data^179,181 for the prediction of seizures. Their findings indicated that EEG changes characteristic for a pre-ictal state may be detectable a few seconds before their actual seizure onset on EEG. None of these mostly linear techniques has been implemented clinically.

Since spikes in the EEG are usually considered the hallmark of an epileptic brain, their possibly altered pre-ictal occurrence was investigated in several studies. Sherwin¹⁸⁸ noted increased correlation of epileptiform activity (spikes) between two adjacent cortical sites in the 15 to 20 minutes prior to EEG onset of focal seizures in a cat model of epilepsy. In 1983, Lange et al.¹⁰⁷ demonstrated a similar correlation of interictal spikes between the side of the seizure focus and the “normal” temporal lobe within the 20 minutes prior to EEG seizure onset in patients with temporal lobe epilepsy. While other authors reported a decrease or even total cessation of spikes before seizures,^63,64,221 re-examination did not confirm this phenomenon in a larger test set.⁹⁴

With the advent of the physical-mathematical theory of nonlinear dynamics (colloquially termed chaos theory) in the early 1980s, new analysis techniques were developed to characterize apparently irregular behavior—a distinctive feature of the EEG—and thus to extract features from the EEG that are not obvious to the human eye (see references 12, 45, 84, 118 for an overview). During the last two decades, these techniques have generated a large body of evidence for the existence of a pre-ictal state. The earliest attempts to use nonlinear time series analysis (see reference 91 for an overview) were started in the 1990s using the “largest Lyapunov exponent” to describe changes in brain dynamics.^78,79,83 The investigators observed transient drops in the temporal evolution of this measure several minutes prior to seizures and proposed that the EEG became progressively less chaotic as seizures approached. The first studies to describe characteristic changes in the EEG shortly before an impending seizure in a larger group of patients used the “correlation dimension” as an estimate for neuronal complexity^{51,53,117,119,121,124} and the “correlation density.”¹⁴¹ These studies were followed by others using measures such as dynamical similarity index,^{108,111,112,113,157} Kolmogorov entropy,^153,210,211 or marginal predictability.^43,126,127 In parallel, other techniques have focused on extracting neurophysiologic features from the EEG associated with epileptiform activity in human and animal physiology, such as bursts of complex epileptiform activity, slowing, chirps, and changes in signal energy.^{60,133,158,225} Other methods focused on defining pre-ictal states include catastrophe theory,^25,26 self-organized criticality,^128,224 recurrent neural networks,¹⁶⁷ and simulated neuronal cell models.¹⁸⁶ Similar to the studies using the largest Lyapunov exponent, all of the above studies showed characteristic changes minutes to hours prior to seizure onset on the EEG, and were interpreted by their authors as defining pre-ictal states of various durations, some lasting hours.

A problem with most of these studies is that the measures used to characterize the EEG are difficult to interpret in terms of their physiologic correlate. Also, since almost all of these measures are univariate (i.e., related to only a single recording site), they fail to reflect any interactions between different regions of the brain. The epileptogenic process, on the other hand, is commonly accepted to be closely associated with changes in neuronal synchronization in a network of components that may be spatially distributed. The analysis of synchronization in the EEG can therefore a priori be regarded as a promising approach for the investigation of the spatiotemporal dynamics of ictogenesis. Based on newly developed physical-mathematical concepts for synchronization (see reference 169 for an overview), some researchers have focused on bivariate or, more generally, multivariate measures over the last 5 to 6 years that permit assessment of synchronous activity from multiple sites.^61,212 These measures include nonlinear interdependence,^9,110 measures for phase synchronization and cross-correlation,^{28,76,109,149,150,152} the difference of the largest Lyapunov exponents of two or more channels,^27,75,80,82 nonlinear causality,²⁹ a classification approach based on a fusion of multiple EEG features from multiple sites.^38,133

Results obtained indicate that seizures are not random events, but rather are related to ongoing dynamical processes that may begin minutes to hours to days beforehand (for an overview, see references 73, 132, 134, 192, 193). The fact that most of the approaches result in different prediction horizons indicates that they may reflect different aspects of ictogenesis, but it is likely that none of these techniques appears to depict the process fully. As many of these studies suggest, seizure precursors may wax and wane in attempts to ignite a clinical event, but the forces both driving and suppressing seizure generation remain hidden. Other concepts abstracted from the above body of work indicate that seizure precursors may begin locally and then expand spatially, and even “entrain” other brain structures before reaching the critical mass required to initiate a clinical seizure. Patterns appear to be patient specific, within a finite range of pattern types, and it appears that different approaches may be required to predict seizures with clinically useful accuracy in different individuals or in different epilepsy syndromes. This may be a function of individual physiology or potentially confounding variables such as electrode placement and the amount and speed of medication taper during inpatient video-EEG monitoring.^132,134

At the beginning of the new millennium there was great enthusiasm for the ability of a variety of analysis methods to define the pre-ictal state. By that time work in the area had also extended to scalp EEG,^{43,71,77,110,173} though the majority of researchers confined their investigations to intracranial EEG recordings. Careful review of the literature at that time, however, revealed considerable contradiction in results from different research groups. Of even more concern was that despite over a decade of excellent work in the field, convincing evidence demonstrating unequivocal seizure prediction in blinded, prospective, randomized clinical trials, with appropriate statistical validation, remained elusive. Central to the problem was the challenge of developing algorithms to detect unknown patterns associated with seizure generation, a process that remains poorly understood. Much of the EEG data analyzed in studies up to that time were highly selected and restricted with regard to seizure type, patient state, signal:noise ratio, duration of recordings, artefacts, etc. In addition, there were no standardized methods or nomenclature for marking continuous EEG data, no accepted methods for assessing algorithm performance, and no agreement on acceptable test data. Even clear definitions of exactly what constitutes seizure onset, seizure prediction, anticipation, and the definition of ictal events either clinically or by EEG were nebulous. For these reasons, beginning with an impromptu meeting at the American Epilepsy Society Meeting in Los Angeles, California, in 2000, the International Seizure Prediction Group (ISPG) was formed to provide an informal
structure for the major groups working in this area to share data and ideas.

The ISPG was established with the specific goal of moving the field of seizure prediction forward from “proof of principle” experiments into validated, well-understood methods that could be applied to both basic science and clinical applications. The first international workshop of this group was held in Bonn, Germany, in 2002, funded by grants from the German Section of the International League Against Epilepsy, the German Section of the International Federation of Clinical Neurophysiology, and the American Epilepsy Society. At the core of the workshop was an assessment of the state of the field at that time by having each major group apply its methods to predict seizures from a shared set of continuous intracranial EEG data.¹²² Findings obtained from applying a large number of analysis techniques are summarized in eight peer-reviewed articles published together in the journal Clinical Neurophysiology.^{38,54,66,81,86,88,114,151} Although substantial efforts were made to provide uniform data in terms of disease type, conditions, and recordings, the results of all these investigations were inconsistent and at times contradictory. Three studies had positive results, predicting seizures for different time horizons; four studies had negative results; and one had both, depending upon which techniques were employed. In none of the investigations, even those with positive findings, could seizures be predicted with any exact timing. Rather, a state of increased seizure likelihood lasting up to several hours was identified. There was agreement that, at present, none of the EEG analysis techniques was sufficient for broad clinical application, and that there were major practical problems to overcome. Nevertheless, much was learned from the exercise, particularly with regard to the need for standardization of analyses, data requirements, performance criteria, and nomenclature. Some of the results were encouraging, while other results illustrated that certain approaches are unlikely to be worthwhile. The current impact of the latter point is stressed by recent controversies about the relevance of nonlinear approaches for the prediction of epileptic seizures^{140,144,145,150,151} and by studies raising doubts about the reproducibility of previously reported claims.^{10,39,67,105,106,123} These contradictory findings emphasize the need for reliable methods for evaluating the performance of seizure prediction techniques.

Over the last three decades two main categories of analysis techniques have been used to extract pre-ictal information from the EEG: Linear and nonlinear techniques. Depending upon whether EEG data from two or more sites are analyzed independently, or for possible interactions, these techniques can further be divided into univariate and multivariate approaches. All techniques permit reduction of large amounts of EEG data to a small number of parameters for downstream processing.

Linear EEG analysis techniques (see reference 135 for an overview) are important contributors to understanding physiologic and pathophysiologic conditions in the brain. Nonparametric linear methods comprise analysis techniques such as evaluation of amplitude, interval or period distributions, and estimation of auto- and cross-correlation functions, as well as analysis in the frequency domain (using the Fast Fourier Transform or other time-frequency transformations) such as power spectral estimates, cross-spectral functions, or linear coherence.²³ Parametric linear methods include, among others, autoregressive (AR) and autoregressive moving average (ARMA) models,^57,87,88 and provide an alternative way to estimate properties of the power spectrum. These main branches are accompanied by pattern recognition methods involving either a mixture of techniques mentioned above or, more recently, taking features extracted from neurosignals and inputting them into a variety of novel classifiers, such as probabilistic artificial neural networks. Since linear methods provide only limited information as to the dynamical aspects of the EEG, it is argued that they cannot fully characterize the complicated, apparently irregular behavior of the complex nonlinear dynamical system brain. In this system, nonlinearity is introduced already on the cellular level, since the dynamical behavior of individual neurons is governed by integration, threshold, and saturation phenomena. There is evidence, that the epileptic process enhances the nonlinear deterministic structure in the EEG.^6,8,24 In order to allow for an improved characterization of complex dynamics, nonlinear analysis techniques have been developed that provide a methodologically different approach to EEG analysis. Within this framework, the dynamical behavior is embedded in a so-called state space. This generally high-dimensional cartesian space is spanned by all state variables (i.e., the number of degrees of freedom) of a system, and the system dynamics generate a trajectory through this space. Properties of the trajectory in state space can then quantitatively be characterized by nonlinear measures (see below). When embedding EEG time series, the number of state variables (i.e., the number of degrees of freedom of the system brain) are unknown. Fortunately, the theorem of Takens²⁰² allows to reconstruct a so-called equivalent state space even from a single time series using the so-called method of delays (time-delay embedding) (see also references 164 and 183). Here the basic assumption is that a single but long enough and accurate measurement of a stationary dynamics is sufficient to capture all the relevant system properties necessary to reconstruct the state space. In terms of EEG analysis, one may assume that an EEG signal reflects the influence of the multiple variables participating in brain dynamics.^49,143 The reconstruction of an m-dimensional state space (here m is the so-called embedding dimension) requires the generation of m time-delayed versions of an EEG time series; that is, each version consists of successive points of the original time series separated by a fixed time delay (τ). A variety of techniques have been proposed that allow one to estimate either m or τ from a measured time series, assuming, however, that the other parameter has been chosen appropriately beforehand. Because of this mutual dependence, the time-delay embedding of an EEG time series in a high-dimensional state space is regarded as a crucial point in nonlinear EEG analysis. An improper state space reconstruction is a common source of errors and can lead to a mischaracterization of the dynamics. In case of multichannel EEG recordings, an alternative embedding scheme would be to use each channel as an axis of the cartesian space. In this case the embedding dimension m is fixed and equals the number of recording channels. Although this spatial embedding is regarded to be the more natural scheme, several assumptions have to be made beforehand that might lead to similar problems as with the time-delay embedding and are matter of debate.^103,172 These problems include the optimal distance between different recording sites, which is usually fixed, among others. It remains to be established whether the combined use of techniques (so-called spatial-temporal embedding¹⁴¹) can be regarded as more appropriate for EEG analysis.