Epileptic seizures are preceded by a decrease in synchronization

The exact mechanisms leading to the occurrence of epileptic seizures in humans are still poorly understood. It is widely accepted, however, that the process of seizure generation is closely associated with an abnormal synchronization of neurons. In order to investigate this process, we here measure phase synchronization between different regions of the brain using intracranial EEG recordings. Based on our preliminary finding of a preictal drop in synchronization, we investigate whether this phenomenon can be used as a sensitive and specific criterion to characterize a preseizure state and to distinguish this state from the interictal interval.Applying an automated technique for detecting decreased synchronization to EEG recordings from a group of 18 patients with focal epilepsy comprising a total of 117 h, we observe a characteristic decrease in synchronization prior to 26 out of 32 analyzed seizures at a very high specificity as tested on interictal recordings. The duration of this preictal state is found to range from several minutes up to a few hours. Investigation of the spatial distribution of preictal desynchronization indicates that the process of seizure generation in focal epilepsy is not necessarily confined to the focus itself but may instead involve more distant, even contralateral areas of the brain. Finally, we demonstrate an intrahemispheric asymmetry in the spatial dynamics of preictal desynchronization that is found in the majority of seizures and appears to be an immanent part of the mechanisms underlying the initiation of seizures in humans.     

Seizure probability in animal models of acquired epilepsy: a perspective on the concept of the preictal state

The concept of a preictal state is based on the belief that it may be possible to predict seizures before they occur. The preictal state is viewed as a time period when a seizure is practically inevitable, or at least a period of greatly increased seizure probability. Changes in seizure frequency may provide insight into how seizure probability increases after brain injury. Here, time-dependent changes in the frequency of spontaneous recurrent seizures after brain injury are summarized from published, nearly continuous, electrographic (EEG) recordings of kainate-treated rats and neonatal rats subjected to hypoxia-ischemia. For these animal models, seizure frequency - and thus seizure probability - was a sigmoid function of time after the brain injury. This observation differs from the traditional view, where the development of epilepsy after brain injury is a step-function of time, and the latent period is the time between a brain injury and the first spontaneous seizure. Based on backward extrapolation of the plots of seizure frequency versus time, these data suggest that seizure probability increases continuously during the latent period. Also, spontaneous recurrent seizures frequently occurred in clusters, suggesting that the intra-cluster seizure intervals are periods of high seizure probability. Thus, seizure probability progressively increases as a function of time after an epileptogenic brain injury, and is particularly high between seizures within a cluster, as compared to the time between clusters. These data suggest that the detectors of the preictal state need to be accurate (and tested) over a very wide range of seizure probabilities, and that studies on the physiological events that occur during seizure clusters may provide insight on the properties of the preictal state.     

Diagnosis of epilepsy

0.5-1% of the population suffers from epilepsy, while another 5% undergoes diagnostic evaluations due to the possibility of epilepsy. In the case of suspected epileptic seizures we face the following questions: Is it an epileptic seizure? The main and most frequent differential-diagnostic problems are the psychogenic non-epileptic seizures ("pseudo-seizures") and the convulsive syncope, which is often caused by heart disorders. Is it epilepsy? After an unprovoked seizure, the information on recurrence risk is an important question. The reoccurrence is more possible if a known etiological factor is present or the EEG shows epileptiform discharges. After an isolated epileptic seizure, the EEG is specific to epilepsy in 30-50% of cases. The EEG should take place within 24 hours postictally. If the EEG shows no epileptiform potentials, a sleep-EEG is required. What is the cause of seizures? Hippocampal sclerosis, benign tumors, and malformations of the cortical development are the most frequent causes of the focal epilepsy. Three potentially life-threatening conditions may cause chronic epilepsy: vascular malformations, tumors, and neuroinfections. The diagnosis in theses cases can usually be achieved by MRI, therefore, MRI is obligatory in all epilepsies starting in adulthood. The presence of epileptogenic lesion has a prognostic significance in treatment. If the MRI shows a circumscribed lesion then the pharmacological treatment will likely to be unsuccessful, while surgery may result in seizure freedom. The new and quantitative MRI techniques, such as volumetry, T2-relaxometry, MR-spectroscopy, and functional MRI play a growing role in the epilepsy diagnosis.     

On the proper selection of preictal period for seizure prediction

Supervised machine learning-based seizure prediction methods consider preictal period as an important prerequisite parameter during training. However, the exact length of the preictal state is unclear and varies from seizure to seizure. We propose a novel statistical approach for proper selection of the preictal period, which can also be considered either as a measure of predictability of a seizure or as the prediction capability of an understudy feature. The optimal preictal periods (OPPs) obtained from the training samples can be used for building a more accurate classifier model. The proposed method uses amplitude distribution histograms of features extracted from electroencephalogram (EEG) recordings. To evaluate this method, we extract spectral power features in different frequency bands from monopolar and space-differential EEG signals of 18 patients suffering from pharmacoresistant epilepsy. Furthermore, comparisons among monopolar channels with space-differential channels, as well as intracranial EEG (iEEG) and surface EEG (sEEG) signals, indicate that while monopolar signals perform better in iEEG recordings, no significant difference is noticeable in sEEG recordings.     

High-frequency neuronal network modulations encoded in scalp EEG precede the onset of focal seizures

Modulations of neuronal network interactions by seizure precursors are only partially understood and difficult to measure, in part due to inherent intra- and inter-patient seizure heterogeneities and EEG variability. This study investigated preictal neuromodulations associated with seizures originating in the temporal and/or frontal lobes, using information theoretic parameters estimated from awake scalp EEGs in two frequency ranges, ≤100 Hz and >100 Hz, respectively. Seizure-related activity at high frequencies has not been extensively estimated in awake scalp EEGs. Based on the statistical similarity of preictal and ictal information parameters, preictal network interactions appeared to be specifically modulated at frequencies >100 Hz, but not at lower frequencies. The dynamics of these parameters varied distinctly according to the origin of seizure onset (temporal versus frontal). Although preliminary, and based on a small patient sample for which the potential heterogeneity of multiple anticonvulsive medications was difficult to control, these results suggest that preictal modulations may be estimated from high-frequency scalp EEGs using directional information measures with high specificity to ictal events, and may thus be promising for improving seizure prediction.     

Auditory event-related potentials (P300) in the identification of psychogenic nonepileptic seizures

The feasibility and conceivable value of postictal event-related potential (ERP) recordings were studied in patients with nonepileptic seizures (NES) admitted for long-term video/EEG monitoring. Ten patients with NES underwent preictal (on hospital admission) and postictal (< or =6 hours after seizure) ERP recordings of an auditory oddball paradigm. Additionally, 10 temporal lobe epilepsy (TLE) patients with partial seizures and secondary generalization underwent preictal, postictal (< 6 hours after seizures), and interictal (7-48 hours after seizure) ERP recordings. We recently reported that ERPs recorded in TLE patients with partial epilepsy undergo a temporary change postictally, while returning to their preictal state during interictal recordings. In the current study intraclass correlations, transformed into z scores, are used to determine test-retest validity of repeated ERP recordings. An independent sample t test with z scores for the comparison of preictal and postictal recordings showed that ERP activation differed between NES and TLE patients (P=0.009). More specifically, ERP recordings in the preictal and postictal states were similar in NES patients, but dissimilar in TLE patients. On the other hand, this dissimilarity in ERPs disappeared when comparing z scores for the preictal and postictal recordings in NES patients with z scores for the preictal and interictal recordings in TLE patients. This further supports the notion that identical waveforms during preictal and postictal recordings in NES patients reflect nonepileptic seizure activity. The current findings suggest that postictal ERP recordings are useful in the diagnosis of NES and differentiate TLE from NES.     

Seizure prediction and the preseizure period

Beginning in the 1970s engineers designed systems to predict epileptic seizures based upon quantitative changes in the electroencephalogram, which they hypothesized began well in advance of clinical seizure onset. These efforts flourished in the 1990s, as independent laboratories demonstrated evidence of a 'preseizure period' up to 20 min prior to clinical symptoms in patients implanted with intracranial electrodes during evaluation for epilepsy surgery. Years later, clinical and laboratory experiments leave little doubt that a preseizure period exists in temporal lobe and perhaps other forms of epilepsy. Its existence, however, raises fundamental questions about what constitutes a seizure, what brain regions are involved in seizure generation, and whether discrete interictal, preictal, ictal and post-ictal physiologies exist, or blend together in a continuous process. Pressing milestones, necessary for clinical utility, are: (1) demonstrating prospective seizure prediction from prolonged human data sets, (2) elucidating mechanisms underlying seizure precursors and (3) implementing these algorithms on implantable hardware platforms. The notion of a preseizure state is catalyzing new clinical and basic science research, which has the potential to dramatically increase our understanding of epilepsy, and to generate exciting new therapies for patients.     

EEG spike activity precedes epilepsy after kainate‐induced status epilepticus

Purpose: Chronic epilepsy frequently develops after brain injury, but prediction of which individual patient will develop spontaneous recurrent seizures (i.e., epilepsy) is not currently possible. Here, we use continuous radiotelemetric electroencephalography (EEG) and video monitoring along with automated computer detection of EEG spikes and seizures to test the hypothesis that EEG spikes precede and are correlated with subsequent spontaneous recurrent seizures. Methods: The presence and pattern of EEG spikes was studied during long recording epochs between the end of status epilepticus (SE) induced by three different doses of kainate and the onset of chronic epilepsy. Results: The presence of spikes, and later spike clusters, over several days after SE before the first spontaneous seizure, was consistently associated with the development of chronic epilepsy. The rate of development of epilepsy (i.e., increase in seizure frequency) was strongly correlated with the frequency of EEG spikes and the cumulative number of EEG spikes after SE. Conclusions: The temporal features of EEG spikes (i.e., their presence, frequency, and pattern [clusters]) when analyzed over prolonged periods, may be a predictive biomarker for the development of chronic epilepsy after brain injury. Future clinical trials using prolonged EEG recordings may reveal the diagnostic utility of EEG spikes as predictors of subsequent epilepsy in brain‐injured humans.     

Clinical neurophysiology of epilepsy

Electroencephalography (EEG) continues to be the most important diagnostic tool in the management of patients with epilepsy. In particular, the high specificity of interictal epileptiform discharges makes scalp EEG a valuable tool in the evaluation of patients with a history of seizures or seizure-like episodes. Advances in technology, most notably the development of digital video-EEG, have significantly expanded the utility of EEG. In addition to the routine EEG, long-term monitoring studies including video-EEG, ambulatory EEG, and continuous EEG monitoring play important roles in various aspects of the diagnosis and treatment of epilepsy. Recent developments in computerized seizure detection and prediction algorithms, particularly those utilizing intracranial EEG electrodes, hold promise for future development of novel treatment strategies.     

Understanding dynamic state changes in temporal lobe epilepsy.

The authors review their work in applying nonlinear dynamics to predict onset of seizures in patients with medically refractory temporal lobe epilepsy. The underlying mathematical methodology is presented in some detail. To illustrate their approach, they present an extensive discussion of the analysis of preictal data from two seizures of one patient, and from one disease-free subject. They find similar behavior in some nonlinear measures across seizures, which suggests the possibility of forming a robust method of seizure prediction. However, despite clinical and electrographic preictal and ictal similarity, they have also found marked heterogeneity in other nonlinear measures of preictal activity across seizures arising out of stage 2 nonrapid eye movement sleep. The underlying basis for this variation remains uncertain and needs to be the subject of further intense study to gain a better understanding of the dynamic basis of epilepsy. The origin of these heterogeneities may or may not be related to the much larger differences in nonlinear measures between patients and disease-free subjects. To understand these differences, the authors think it is crucial to pay close attention to potentially confounding factors such as behavioral and other state changes, and to study and report in detail the ways in which relevant nonlinear measures behave in the presence of such changes, independent of seizure onset.     

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

ObjectivesIn patients with intractable epilepsy, predicting seizures above chance and with clinically acceptable performance has yet to be demonstrated. In this study, an intracranial EEG-based seizure prediction method using measures of similarity with a reference state is proposed.Methods1565 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.ResultsBetter 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%.ConclusionSeizures are predicted above chance in 41% of patients using measures of state similarity.SignificanceSensitivity and specificity levels are potentially interesting for closed-loop seizure control applications.     

Does interictal spiking change prior to seizures?

We studied 10 patients with intractable epilepsy being evaluated for epilepsy surgery for preictal changes in spiking. All patients were implanted with intracranial electrodes and underwent continuous EEG/audiovisual monitoring. Interictal spikes were detected and recorded continuously by a dedicated computerized system. Edited spikes were counted during 0-5, 5-10, and 0-60 min epochs before each seizure, during epochs of unvarying state of arousal (awake or sleep stage II). When comparing by repeated measures, 1-way ANOVA, total spiking (in all recording channels) did not differ among the different preictal epochs (0-5, 5-10, 0-60 min) in 45 seizures (F = 0.88, P = 0.40, using the Geisser-Greenhouse adjustment--GGA). Likewise, no significant differences were obtained during those same epochs when comparing spiking originating from the channel of seizure onset in 5 patients with 28 seizures of localized onset (F = 1.19, P = 0.38 using the GGA). Our findings indicate that in patients with intractable epilepsy, no changes in spiking occur in the 5 min prior to seizures, when compared to more distant preictal epochs.     

Seizure Prediction

There is mounting evidence that seizures are preceded by characteristic changes in the EEG that are detectable minutes before seizure onset. Using novel signal analysis techniques, researchers are beginning to characterize the transition from the interictal to the ictal state in quantitative terms. This research has led to the development of automated seizure prediction algorithms. Active debate persists regarding the interpretation of research results, methods of signal analysis, as well as experimental and statistical methods for testing seizure prediction algorithms. Developments in this field have led to new theories on the mechanism of seizure development and resolution. The ability to predict seizures could lead the way to novel diagnostic and therapeutic methods for the treatment of patients with epilepsy.The physiological characteristics of a seizure differ dramatically from that of the interictal state. During the interictal state, the EEG typically is lower in amplitude, less rhythmic, and more irregular in morphology. At the onset of a seizure, there is a sudden change in the amplitude, frequency, and morphology of the EEG signal, an increase in rhythmicity, and a synchronization of activity that takes place across widespread areas of the cerebral cortex. The clinical and EEG changes at the onset of a seizure are so dramatic that they give the impression of occurring without any warning or preceding buildup. Although patients sometimes report prodromal symptoms hours to minutes before seizures, the concept of a prodromal change in the EEG was rarely considered, until recently.The shift from the interictal condition, during which the patient is relatively asymptomatic, to the seizure, during which clinical symptoms may range from subtle sensory, cognitive, or emotional changes to complete loss of consciousness and motor control, is considered a state transition. A question of scientific and clinical interest is whether the transition between these physiological conditions is gradual or abrupt. The answer to this question will provide insight into the underlying mechanisms of seizure generation. From a clinical perspective, gradual transition offers the possibility of predicting an impending seizure, while an abrupt transition provides no hope of anticipating the seizure in time to intervene therapeutically.Evidence of a gradual transition was reported as early as the 1970s when investigators, using linear signal processing methods (1–3), reported changes in EEG characteristics beginning minutes prior to the onset of seizures. Some investigators found changes in interictal spike distribution or incidence approaching seizure onset (4,5), while others found no consistent changes in spike patterns (6–8). These observations were made through analysis of relatively brief EEG samples in a limited number of patients. Even at that time, the researchers realized that the presence of preictal changes in the EEG raised the possibility that seizures could be predicted.In the late 1980s, faster computers with larger storage capacity made it possible to systematically analyze longer segments of EEG preceding and following seizures from a larger number of patients and to use more sophisticated approaches to signal processing. Motivated by theories that seizures may result from spontaneous state transitions in a chaotic nonlinear system (9–18), some investigators began to apply mathematical techniques developed for the study of complex nonlinear systems to analyze EEGs for characteristics unique to the transitions into and out of seizures (15,16,18–34). As a result, researchers began to report measurable changes in EEG dynamics (temporal and spatiotemporal) that preceded seizures by periods ranging from seconds to hours. These changes were quantified in terms of signal order (vs chaoticity), signal complexity, time dependency, and similarity/synchronization indices—all estimated by constructing multidimensional phase space. Each measure was designed to provide a quantitative method for capturing a different aspect of signal property and, thus, the property of the underlying signal generator. The formal meaning of these measures was well understood when applied to computer-generated output from autonomous models of deterministic autonomous complex nonlinear systems. However, the interpretation of the same measures when applied to noisy, nonautonomous, nonstationary systems, like the human brain, continues to be debated. Nonetheless, the findings provided evidence from several different perspectives that changes occurred in the spatiotemporal properties of the EEG for minutes to hours before the onset of a seizure. These studies have been extensively reviewed (35–37).It is difficult, if not impossible, to prove whether EEG signals are linear or nonlinear. Yet, an advantage of mathematical techniques developed for analyzing nonlinear systems is that they do not require the assumption that the signal is linear. This feature is an important advantage if there are significant nonlinearities in the signal. However, two disadvantages of nonlinear techniques for EEG analysis are that the methods are novel to EEG research, thus opinions differ as to how the results are to be interpreted (38–43), and they are computationally demanding. The computational intensity of the methods was a major disadvantage before current central processing unit speeds were achieved. The limitations of nonlinear methods have led some researchers to renew efforts to investigate seizure generation with linear signal processing techniques (44–48). Linear methods, such as energy, the spectrogram, and coherence, require the assumption of a linear signal. In many instances, such as the epileptic brain, when the nature of the generator is not well understood, one cannot assume that the signal is linear. Yet, linear measures have been applied successfully to the analysis of a wide range of signals. Some investigators have found evidence for EEG signal changes preceding seizure onset, and some have questioned the necessity of using the more complicated nonlinear methods.Others have questioned nonlinear techniques on other grounds. Following the published successes in finding what became known as a preictal state, some scientists began to report negative results using the same nonlinear techniques previously described (41,42,49–52). These investigators raised questions about methods that employed the similarity index, the correlation dimension, the correlation integral, and the Lyapunov exponent. In most instances, the researchers did not precisely duplicate the methods they challenged. However, their reports have served to temper initial enthusiasm and confidence in finding clinically useful seizure prediction algorithms. In addition, they stimulated proposals for new experimental and statistical methods for testing the hypothesis of the existence of a preictal state (50,53,54). Recently, a statistically based evaluation of the ability of a number of linear/nonlinear and univariate/bivariate measures to distinguish significantly the preictal from the interictal state has provided further evidence of significant differences in EEG characteristics between the two periods (55). While several measures showed significance differences, bivariate measures were generally more effective.With growing evidence that seizures are preceded by measurable changes in EEG, some researchers began to develop and test automated seizure prediction algorithms (56–65). These algorithms are computer programs that read the raw EEG signal, calculate measures of specific signal characteristics, compare these measures to threshold values, and generate seizure warnings when pre-established criteria are met. The parameters of the algorithms can be set to alter the sensitivity/specificity ratio. In most cases, specificity is expressed as the false positive rate (i.e., number of false warnings per unit of time). In general, the higher the sensitivity obtained (i.e., the percentage of seizures predicted), the greater is the false positive rate of any prediction algorithm. Initial tests of these prediction algorithms involved EEG recordings from a small number of patients. In some instances, interictal data segments were preselected by the investigators (60). However, the algorithms were not subjected to rigorous statistical validation. Since there are no established seizure prediction algorithms that can serve as a standard, most investigators feel that a statistically based standard should be employed. In later reports, algorithm performance was compared to naïve statistically based prediction schemes that did not utilize information from the EEG (64,65). However, there remains debate as to what constitutes an appropriate experimental design, what statistical comparisons are optimal, and the standards for a good algorithm performance. Notwithstanding ongoing debate (and in some cases, skepticism), the results of these investigations were encouraging. Evaluations of the algorithms were based on recordings from a small number of patients. Most studies employed recordings performed with intracranial EEG electrodes. However, preliminary reports have indicated that it is possible to predict seizures from EEG recordings utilizing scalp electrodes (30,66,67). The ability to predict using intracranial electrode recordings will be important in the development of closed loop seizure control devices. However, prediction from scalp EEG recordings will make it possible to use the devices in a variety of monitoring applications. An obvious application would be monitoring laboratories for epilepsy patients undergoing diagnostic or presurgical evaluations. Other potential applications include intensive care units and emergency departments.A major barrier to this research is the limited availability of data to test the algorithms. Adequate statistical testing of performance requires high quality, continuous EEG datasets of ample duration and from a sufficient number of patients, with a wide enough variety of seizure types. Very few medical centers have adequate capacity to store and organize large numbers of such datasets. Data sharing among investigators will require a massive effort to generate and organize de-identified research datasets. To date, this objective has not been met. Establishment of a databank of training and test datasets is a challenge for the immediate future.One of the promises of seizure prediction is that of developing better closed-loop seizure control devices. It is anticipated that closed-loop devices, coupled with seizure prediction, will be more efficient and effective than open-loop devices or devices triggered by seizure detection. Preliminary reports in a rodent model of temporal lobe epilepsy suggest that closed-loop, state-dependent control devices utilizing automated seizure prediction algorithms are feasible (68–72). Early investigations found that the rodent model exhibited dynamic changes in the EEG during transitions to the ictal state that were similar to those reported in human temporal lobe epilepsy. In addition, they found that electrical stimulation to the hippocampus triggered by a preictal state detection consistently reversed the dynamics of the EEG signal, resetting it back to the values of the interictal state. In addition, the stimulation appeared to delay seizure onset.Investigations into seizure prediction have raised important questions about seizure control. Although there is debate as to whether seizures and transitions between physiological states can be better explained by the theory of linear or nonlinear dynamics, understanding the dynamics of the epileptic brain remains an important objective. While linear systems respond predictably to external forcing (e.g., electrical stimulation) or change in a control parameter (e.g., release of a GABAergic drug), this is not the case with nonlinear systems, particularly if they are chaotic. Even low-dimensional chaotic systems are highly sensitive to initial conditions, and their behavior cannot be predicted over long periods of time. For this reason, novel approaches to controlling both low- and high-dimensional chaotic systems have been developed (14,17,73,74). Proof of chaos in epilepsy appears to be beyond the capability of current research techniques. Yet, theoretical tenets that explain epilepsy as a dynamical disorder have been compelling to many (9–11,13–34). However, some of the early proponents of these theories have become less convinced of their accuracy, as alternate explanations for empirical experimental results have been put forward. It is likely that progress in understanding the dynamics of epilepsy and advancing the goals of seizure prediction and control will require experimental investigations in animal models of epilepsy.Research related to seizure prediction has raised new questions about the basic mechanisms underlying seizure generation. The finding that the transition from the interictal to the ictal state evolves over minutes to hours must be incorporated into existing theories of ictogenesis. Investigations into cellular, synaptic, and extracellular processes that influence neuronal excitability may be able to explain seizures. However, epilepsy is a disorder characterized by intermittent recurrence of seizures. Current mainstream approaches to epilepsy research have not yet explained the repeated transition into and out of seizures. Much of the evidence from seizure prediction research suggests the presence of deterministic mechanisms in the EEG generators of the epileptic brain. Yet, the “governing dynamics,” to borrow a phrase from A Beautiful Mind, are not understood. Perhaps, integrating known cellular, synaptic, and extracellular changes (a science based largely on neurochemistry, neuroanatomy, and neuropharmacology) with computationally based dynamical systems theory will provide an understanding of the phenomenon of epilepsy.     

Daily resting-state intracranial EEG connectivity for seizure risk forecasts

Forecasting seizure risk aims to detect proictal states in which seizures would be more likely to occur. Classical seizure prediction models are trained over long-term electroencephalographic (EEG) recordings to detect specific preictal changes for each seizure, independently of those induced by shifts in states of vigilance. A daily single measure—during a vigilance-controlled period—to estimate the risk of upcoming seizure(s) would be more convenient. Here, we evaluated whether intracranial EEG connectivity (phase-locking value), estimated from daily vigilance-controlled resting-state recordings, could allow distinguishing interictal (no seizure) from preictal (seizure within the next 24 h) states. We also assessed its relevance for daily forecasts of seizure risk using machine learning models. Connectivity in the theta band was found to provide the best prediction performances (area under the curve ≥ .7 in 80% of patients), with accurate daily and prospective probabilistic forecasts (mean Brier score and Brier skill score of .13 and .72, respectively). More efficient ambulatory clinical application could be considered using mobile EEG or chronic implanted devices.     

Seizure prediction in hippocampal and neocortical epilepsy using a model-based approach

ObjectivesThe aim of this study is to develop a model based seizure prediction method.MethodsA neural mass model was used to simulate the macro-scale dynamics of intracranial EEG data. The model was composed of pyramidal cells, excitatory and inhibitory interneurons described through state equations. Twelve model’s parameters were estimated by fitting the model to the power spectral density of intracranial EEG signals and then integrated based on information obtained by investigating changes in the parameters prior to seizures. Twenty-one patients with medically intractable hippocampal and neocortical focal epilepsy were studied.ResultsTuned to obtain maximum sensitivity, an average sensitivity of 87.07% and 92.6% with an average false prediction rate of 0.2 and 0.15/h were achieved using maximum seizure occurrence periods of 30 and 50 min and a minimum seizure prediction horizon of 10 s, respectively. Under maximum specificity conditions, the system sensitivity decreased to 82.9% and 90.05% and the false prediction rates were reduced to 0.16 and 0.12/h using maximum seizure occurrence periods of 30 and 50 min, respectively.ConclusionsThe spatio-temporal changes in the parameters demonstrated patient-specific preictal signatures that could be used for seizure prediction.SignificanceThe present findings suggest that the model-based approach may aid prediction of seizures.     

On the predictability of epileptic seizures.

OBJECTIVE: An important issue in epileptology is the question whether information extracted from the EEG of epilepsy patients can be used for the prediction of seizures. Several studies have claimed evidence for the existence of a pre-seizure state that can be detected using different characterizing measures. In this paper, we evaluate the predictability of seizures by comparing the predictive performance of a variety of univariate and bivariate measures comprising both linear and non-linear approaches. METHODS: We compared 30 measures in terms of their ability to distinguish between the interictal period and the pre-seizure period. After completely analyzing continuous inctracranial multi-channel recordings from five patients lasting over days, we used ROC curves to distinguish between the amplitude distributions of interictal and preictal time profiles calculated for the respective measures. We compared different evaluation schemes including channelwise and seizurewise analysis plus constant and adaptive reference levels. Particular emphasis was placed on statistical validity and significance. RESULTS: Univariate measures showed statistically significant performance only in a channelwise, seizurewise analysis using an adaptive baseline. Preictal changes for these measures occurred 5-30 min before seizures. Bivariate measures exhibited high performance values reaching statistical significance for a channelwise analysis using a constant baseline. Preictal changes were found at least 240 min before seizures. Linear measures were found to perform similar or better than non-linear measures. CONCLUSIONS: Results provide statistically significant evidence for the existence of a preictal state. Based on our findings, the most promising approach for prospective seizure anticipation could be a combination of bivariate and univariate measures. SIGNIFICANCE: Many measures reported capable of seizure prediction in earlier studies are found to be insignificant in performance, which underlines the need for statistical validation in this field.     

The seizure prediction characteristic: a general framework to assess and compare seizure prediction methods

The unpredictability of seizures is a central problem for all patients suffering from uncontrolled epilepsy. Recently, numerous methods have been suggested that claim to predict from the EEG the onset of epileptic seizures. In parallel, new therapeutic devices are in development that could control upcoming seizures provided that their onset is known in advance. A reliable clinical application controlling seizures, consisting of a seizure prediction method and an intervention system, would improve patient quality of life. The question therefore arises as to whether the performance of the seizure prediction methods is already sufficient for clinical applications. The answer requires assessment criteria to judge and compare these methods, but recognized criteria still do not exist. Based on clinical, behavioral, and statistical considerations, we suggest the "seizure prediction characteristic" to evaluate seizure prediction methods. Results of this approach are exemplified by its application to the "dynamical similarity index" seizure prediction method using 582 hours of intracranial EEG data, including 88 seizures.     

Seizure detection,seizure prediction,and closed-loop warning systems in epilepsy

Nearly one-third of patients with epilepsy continue to have seizures despite optimal medication management. Systems employed to detect seizures may have the potential to improve outcomes in these patients by allowing more tailored therapies and might, additionally, have a role in accident and SUDEP prevention. Automated seizure detection and prediction require algorithms which employ feature computation and subsequent classification. Over the last few decades, methods have been developed to detect seizures utilizing scalp and intracranial EEG, electrocardiography, accelerometry and motion sensors, electrodermal activity, and audio/video captures. To date, it is unclear which combination of detection technologies yields the best results, and approaches may ultimately need to be individualized. This review presents an overview of seizure detection and related prediction methods and discusses their potential uses in closed-loop warning systems in epilepsy.     

Seizure prediction in patients with focal hippocampal epilepsy

Objective

We evaluated the performance of our previously developed seizure prediction approach on thirty eight seizures from ten patients with focal hippocampal epilepsy.