Epileptic event forewarning from scalp EEG

Is Using Threshold-Crossing Method and Single Type of Features Sufficient to Achieve Realistic Application of Seizure Prediction?

Objective This study aims to verify whether the simple threshold-crossing method can work well enough to achieve the realistic application of seizure prediction on the basis of a large public database, and examines how a more complex classifier can improve prediction performance. It also verified whether the combination of multiple types of features with a complex classifier can improve prediction performance. Method Phase synchronization and spectral power features were extracted from electroencephalogram recordings. The threshold-crossing method and a support vector machine (SVM) were used to identify preictal and interictal samples. Based on the type of selected features and the manner of classification, 5 different methods were conducted on 19 patients. The performances of these methods were directly compared and tested using a random predictor. In-sample optimization problems were avoided in the feature and parameter selection procedure to obtain credible results. Results The threshold-crossing method could only obtain satisfying prediction results for approximately half of the selected patients. The SVM classifier could significantly improve prediction performance compared with the threshold-crossing method for both types of features. Although the average performance was further improved when both types of features were combined with the SVM classifier, the improvement was insignificant. Conclusion A complex classifier, such as the SVM, is recommended in a realistic prediction device, although it will increase the complexity of the device. Indeed, the simple threshold-crossing method performs well enough for some of the patients. The combination of phase synchronization and spectral power features is unnecessary because of the increased computation complexity.

Fast monitoring of epileptic seizures using recurrence time statistics of electroencephalography

Epilepsy is a relatively common brain disorder which may be very debilitating. Currently, determination of epileptic seizures often involves tedious, time-consuming visual inspection of electroencephalography (EEG) data by medical experts. To better monitor seizures and make medications more effective, we propose a recurrence time based approach to characterize brain electrical activity. Recurrence times have a number of distinguished properties that make it very effective for forewarning epileptic seizures as well as studying propagation of seizures: (1) recurrence times amount to periods of periodic signals, (2) recurrence times are closely related to information dimension, Lyapunov exponent, and Kolmogorov entropy of chaotic signals, (3) recurrence times embody Shannon and Renyi entropies of random fields, and (4) recurrence times can readily detect bifurcation-like transitions in dynamical systems. In particular, property (4) dictates that unlike many other non-linear methods, recurrence time method does not require the EEG data be chaotic and/or stationary. Moreover, the method only contains a few parameters that are largely signal-independent, and hence, is very easy to use. The method is also very fast—it is fast enough to on-line process multi-channel EEG data with a typical PC. Therefore, it has the potential to be an excellent candidate for real-time monitoring of epileptic seizures in a clinical setting.

Responsive neurostimulation for the treatment of medically intractable epilepsy

With an annual incidence of 50/100,000 people, nearly 1% of the population suffers from epilepsy. Treatment with antiepileptic medication fails to achieve seizure remission in 20-30% of patients. One treatment option for refractory epilepsy patients who would not otherwise be surgical candidates is electrical stimulation of the brain, which is a rapidly evolving and reversible adjunctive therapy. Therapeutic stimulation can involve direct stimulation of the brain nuclei or indirect stimulation of peripheral nerves. There are three stimulation modalities that have class I evidence supporting their uses: vagus nerve stimulation (VNS), stimulation of the anterior nuclei of the thalamus (ANT), and, the most recently developed, responsive neurostimulation (RNS). While the other treatment modalities outlined deliver stimulation regardless of neuronal activity, the RNS administers stimulation only if triggered by seizure activity. The lower doses of stimulation provided by such responsive devices can not only reduce power consumption, but also prevent adverse reactions caused by continuous stimulation, which include the possibility of habituation to long-term stimulation. RNS, as an investigational treatment for medically refractory epilepsy, is currently under review by the FDA. Eventually systems may be developed to enable activation by neurochemical triggers or to wirelessly transmit any information gathered. We review the mechanisms, the current status, the target options, and the prospects of RNS for the treatment of medically intractable epilepsy.

The role of high-quality EEG databases in the improvement and assessment of seizure prediction methods

Initially, seizure prediction was based on the analysis of brief EEG segments preceding clinically manifest seizures. Whereas such approaches suggested that the sensitivities of various EEG-derived features in predicting seizures were high, the inclusion of longer interictal periods and the combined assessment of sensitivity and specificity and the application of statistical validation methods have put into question the validity of such claims. We here show that the duration of EEG on which analyses are based and the number of seizures assessed negatively correlate with the reported sensitivities of prediction studies. Methodological aspects of seizure prediction are discussed in the framework of currently existing databases and of the newly established European Union database. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.

Seizure prediction and its applications

Epilepsy is characterized by intermittent, paroxysmal, hypersynchronous electrical activity that may remain localized and/or spread and severely disrupt the brain's normal multitask and multiprocessing function. Epileptic seizures are the hallmarks of such activity. The ability to issue warnings in real time of impending seizures may lead to novel diagnostic tools and treatments for epilepsy. Applications may range from a warning to the patient to avert seizure-associated injuries, to automatic timely administration of an appropriate stimulus. Seizure prediction could become an integral part of the treatment of epilepsy through neuromodulation, especially in the new generation of closed-loop seizure control systems.

Quantification of brain macrostates using dynamical nonstationarity of physiological time series

The brain shows complex, nonstationarity temporal dynamics, with abrupt micro- and macrostate transitions during its information processing. Detecting and characterizing these transitions in dynamical states of the brain is a critical issue in the field of neuroscience and psychiatry. In the current study, a novel method is proposed to quantify brain macrostates (e.g., sleep stages or cognitive states) from shifts of dynamical microstates or dynamical nonstationarity. A ``dynamical microstate'' is a temporal unit of the information processing in the brain with fixed dynamical parameters and specific spatial distribution. In this proposed approach, a phase-space-based dynamical dissimilarity map (DDM) is used to detect transitions between dynamically stationary microstates in the time series, and Tsallis time-dependent entropy is applied to quantify dynamical patterns of transitions in the DDM. We demonstrate that the DDM successfully detects transitions between microstates of different temporal dynamics in the simulated physiological time series against high levels of noise. Based on the assumption of nonlinear, deterministic brain dynamics, we also demonstrate that dynamical nonstationarity analysis is useful to quantify brain macrostates (sleep stages I, II, III, IV, and rapid eye movement (REM) sleep) from sleep EEGs with an overall accuracy of 77%. We suggest that dynamical nonstationarity is a useful tool to quantify macroscopic mental states (statistical integration) of the brain using dynamical transitions at the microscopic scale in physiological data.

Permutation entropy to detect vigilance changes and preictal states from scalp EEG in epileptic patients. A preliminary study

Permutation entropy (PE) was recently introduced as a very fast and robust algorithm to detect dynamic complexity changes in time series. It was also suggested as a useful screening algorithm for epileptic events in EEG data. In the present work, we tested its efficacy on scalp EEG data recorded from three epileptic patients. With a receiver operating characteristics (ROC) analysis, we evaluated the separability of amplitude distributions of PE resulting from preictal and interictal phases. Moreover, the dependency of PE on vigilance state was tested by correlation coefficients. A good separability of interictal and preictal phase was found, nevertheless PE was shown to be sensitive to changes in vigilance state. The changes of PE during the preictal phase and at seizure onset coincided with changes in vigilance state, restricting its possible use for seizure prediction on scalp EEG; this finding however suggests its possible usefulness for an automated classification of vigilance states.

Detection of spectral instability in EEG recordings during the preictal period

The study of EEG recordings during the interval prior to an epileptic seizure onset--the preictal period--is likely to detect changes in the ongoing brain activity consistent with seizure anticipation. A novel index of spectral instability (ISpI) based on multiple abrupt changes of EEG spectral features is presented here. Based on the analysis of control records, robust M-estimates are used to calculate the threshold and avoid false warnings. The results obtained with a small data set (three patients, ten preictal records per patient) have shown that the ISpI index provided a warning flag that anticipated the seizure onset by 13.1 (SD = 4.0) min on average.

Seizure anticipation: from algorithms to clinical practice

PURPOSE OF REVIEW

Our understanding of the mechanisms that lead to the occurrence of epileptic seizures is rather incomplete. If it were possible to identify preictal precursors from the EEG of epilepsy patients, therapeutic possibilities could improve dramatically. Studies on seizure prediction have advanced from preliminary descriptions of preictal phenomena via proof of principle studies and controlled studies to studies on continuous multi-day recordings.

RECENT FINDINGS

Following mostly promising early reports, recent years have witnessed a debate over the reproducibility of results and suitability of approaches. The current literature is inconclusive as to whether seizures are predictable by prospective algorithms. Prospective out-of-sample studies including a statistical validation are missing. Nevertheless, there are indications of a superior performance for approaches characterizing relations between different brain regions.

SUMMARY

Prediction algorithms must be proven to perform better than a random predictor before prospective clinical trials involving seizure intervention techniques in patients can be justified.

Seizure anticipation, states of consciousness and marginal predictability in temporal lobe epilepsy

PURPOSE

It has recently been shown that differences between the marginal predictability associated with scalp electrodes adjacent to and remote from the site of a seizure focus are able to distinguish between epochs temporally distant from and just prior to (within about 20 min) the onset of a seizure in patients with temporal lobe epilepsy. The purpose of this paper is to show that these differences of marginal predictability intervals are independent of the state of consciousness of the patient.

METHODS

We have studied a data set encompassing 33 preictal epochs (within 1 h prior to a seizure) and 61 interictal epochs (defined as at least 1 h away from any seizure) from 14 patients. Each 30 s interval of each epoch was categorized into one of seven different states of consciousness. Statistical models were used to search for relationships (in aggregated data) between the values of differences of marginal predictabilities and state of consciousness.

RESULTS

It was not possible to reject the null hypothesis of no relationship between differences of marginal predictabilities and state of consciousness.

CONCLUSIONS

The values of the differences between marginal predictabilities on aggregated data are apparently insensitive to the state of consciousness. This conclusion, coupled with the fact that the differences between marginal predictabilities do depend on time to seizure, suggests the potential utility of these measures as the basis for ambulatory, non-invasive methods of seizure anticipation. However, the development of a practical non-invasive method for seizure anticipation requires further extensive study on disaggregated data from individual patients.

Detecting dynamical changes in time series using the permutation entropy

Timely detection of unusual and/or unexpected events in natural and man-made systems has deep scientific and practical relevance. We show that the recently proposed conceptually simple and easily calculated measure of permutation entropy can be effectively used to detect qualitative and quantitative dynamical changes. We illustrate our results on two model systems as well as on clinically characterized brain wave data from epileptic patients.

Machine failure forewarning via phase-space dissimilarity measures

We present a model-independent, data-driven approach to quantify dynamical changes in nonlinear, possibly chaotic, processes with application to machine failure forewarning. From time-windowed data sets, we use time-delay phase-space reconstruction to obtain a discrete form of the invariant distribution function on the attractor. Condition change in the system's dynamic is quantified by dissimilarity measures of the difference between the test case and baseline distribution functions. We analyze time-serial mechanical (vibration) power data from several large motor-driven systems with accelerated failures and seeded faults. The phase-space dissimilarity measures show a higher consistency and discriminating power than traditional statistical and nonlinear measures, which warrants their use for timely forewarning of equipment failure.

Non-linear, non-invasive method for seizure anticipation in focal epilepsy

In this paper we discuss an approach, using methods of non-linear time series analysis applied to scalp electrode recordings, which is able to distinguish between epochs temporally distant from and just prior to, the onset of a seizure in patients with temporal lobe epilepsy. The method involves a comparison of recordings taken from electrodes adjacent to and remote from the site of ictal onset. In particular, we define a non-linear quantity which we call 'marginal predictability'. This quantity is computed using data from remote and from adjacent electrodes. We find that the difference between the marginal predictabilities computed for the remote and adjacent electrodes decreases several tens of minutes prior to seizure onset, compared to its value interictally.

Seizure anticipation in pediatric epilepsy: use of Kolmogorov entropy

The purpose of this paper is to demonstrate feasibility of using trends in Kolmogorov entropy to anticipate seizures in pediatric patients with intractable epilepsy. Surface and intracranial recordings of preseizure and seizure activity were obtained from five patients and subjected to time series analysis using Kolmogorov entropy. This metric was compared with correlation dimension and power indices, both known to predict seizures in some adult patients. We used alarm levels and introduced regression analysis as a quantitative approach to the analysis of trends. Surrogate time series evaluated data nonlinearity, as a precondition to the use of nonlinear measures. Seizures were anticipated before clinical or electrographic seizure onset for three of the five patients from the intracranial recordings, and in two of five patients from the scalp recordings. Anticipation times varied between 2 and 40 minutes. This is the first report in which simultaneous surface and intracranial recording are used for seizure prediction in children. We conclude that the Kolmogorov entropy and power indices were as effective as the more commonly used correlation dimension in anticipating seizures. Further, regression analysis of the Kolmogorov entropy time series is feasible, making the analysis of data trends more objective.

Channel-consistent forewarning of epileptic events from scalp EEG

Phase-space dissimilarity measures (PSDM) have been recently proposed to provide forewarning of impending epileptic events from scalp electroencephalographic (EEG) for eventual ambulatory settings. Despite high noise in scalp EEG, PSDM yield consistently superior performance over traditional nonlinear indicators, such as Kolmogorov entropy, Lyapunov exponents, and correlation dimension. However, blind application of PSDM may result in channel inconsistency, whereby multiple datasets from the same patient yield conflicting forewarning indications in the same channel. This paper presents a first attempt to solve this problem.

Epileptic seizure prediction and control

Epileptic seizures are manifestations of epilepsy, a serious brain dynamical disorder second only to strokes. Of the world's approximately 50 million people with epilepsy, fully 1/3 have seizures that are not controlled by anti-convulsant medication. The field of seizure prediction, in which engineering technologies are used to decode brain signals and search for precursors of impending epileptic seizures, holds great promise to elucidate the dynamical mechanisms underlying the disorder, as well as to enable implantable devices to intervene in time to treat epilepsy. There is currently an explosion of interest in this field in academic centers and medical industry with clinical trials underway to test potential prediction and intervention methodology and devices for Food and Drug Administration (FDA) approval. This invited paper presents an overview of the application of signal processing methodologies based upon the theory of nonlinear dynamics to the problem of seizure prediction. Broader application of these developments to a variety of systems requiring monitoring, forecasting and control is a natural outgrowth of this field.

New horizons in ambulatory electroencephalography

Since its inception 30 years ago, AEEG has continued to evolve--from four-channel tape recorders to 32-channel digital recorders with sophisticated automatic spike and seizure detection algorithms. AEEG remains an important tool in epilepsy evaluation. In the near future, smaller, faster, and more sophisticated AEEGs will be developed. Seizure detection/anticipation systems will allow the wearer to be forewarned of a seizure so that appropriate safety measures can be taken. With further refinement in our understanding of nonlinear dynamic analysis to define the pre-ictal state, AEEG will be coupled with an accurate seizure anticipation device in a closed-loop system, providing a time window during which therapeutic intervention can occur, to prevent a seizure. The therapeutic intervention will most likely involve vagus nerve or deep brain stimulation. An alternative is that the patient may learn to recognize early symptoms of the pre-ictal state and use behavioral biofeedback interventions to avoid a clinical seizure. In order to achieve convenient ambulatory recording and seizure detection that could realistically improve the lives of patients with refractory epilepsy, the process of miniaturization of such a device to a convenient size must be accomplished. One of the aspects of epilepsy that patients find most frustrating, and that most limits activities, is the vulnerability to sudden unexpected incapacitation due to the occurrence of a seizure. With miniaturization of AEEG and seizure anticipation technology, and advancements in our ability to identify the transition from pre-ictal to ictal state, there is realistic hope that patients with refractory epilepsy may gain control over their seizures and enjoy significantly improved quality of life.

Future aspects of the presurgical evaluation in epilepsy

Epilepsy surgery is a successful therapeutic approach in patients with medically intractable epilepsy. The presurgical evaluation aims to detect the epileptogenic brain area by use of different diagnostic techniques. In this review article the current diagnostic procedures applied for this purpose are described. The diagnostic armamentarium can be divided conceptually into three different groups: assessment of function/dysfunction, structural/morphologic imaging methods and functional neuroimaging techniques. Properties, diagnostic power and limits of all diagnostic tools used in the diagnostic evaluation are discussed. In addition, future perspectives and the diagnostic value of new technologies are mentioned. Some are increasingly gaining acceptance in the routine preoperative diagnostic procedure like MR volumetry or MR spectroscopy of the hippocampus in patients with temporal lobe epilepsy. Some, on the other hand, like MEG and 11C-flumazenil PET, still remain experimental diagnostic tools as they are technically demanding and cost intensive. Besides the refinement of established techniques, co-registration of different modalities like spike-triggered functional MRI will play an important role in the non-invasive detection of the epileptic seizure focus and may change the regimen of the preoperative diagnostic work up of epilepsy patients in the future.

Treatment strategies in Landau-Kleffner syndrome and paraictal psychiatric and cognitive disturbances

Psychiatric and cognitive disturbances of the peri-ictal period (i.e., the seizure prodrome and the postictal period) can be considered paraictal disturbances, as they are directly related to the ictal event. There are also certain interictal psychiatric and cognitive disturbances that become apparent concomitantly with the onset of a seizure disorder and remit and/or significantly improve upon its remission. Such disorders also fall under the classification paraictal disorders, and are exemplified by Landau-Kleffner syndrome (LKS), a disorder in which language and psychiatric disturbances begin with the onset of epileptic activity and improve upon its disappearance. In this article, we review the treatment of paraictal cognitive and psychiatric disorders presenting as preictal and postictal psychiatric disturbances and LKS.

Human EEG responses to controlled alterations of the Earth's magnetic field

OBJECTIVES

Examine the effects of controlled changes in the Earth's magnetic field on electroencephalogram (EEG) and subjective report.

METHODS

Fifty volunteers were exposed double-blind to changes in field magnitude, angle of inclination, and angle of deviation. Volunteers were also exposed to magnetic field conditions found near the North and South Pole. EEG recorded over temporal and occipital sites was compared across 4s baseline, field exposure, and no-change control trials.

RESULTS

No EEG spectral differences as a function of gender or recording site were found. Geomagnetic field alterations had no effect on total energy (0.5-42 Hz), energy within traditional EEG analysis bands, or on the 95% spectral edge. Most volunteers reported no sensations; others reported non-specific symptoms unrelated to type of field change.

DISCUSSION

Three hypothesized field detection mechanisms were not supported: (1) mechanical reception through torque exerted on the ferromagnetic material magnetite; (2) movement-induced induction of an electric field in the body; and (3) enhanced sensitivity due to alterations in the rates of chemical reactions involving electron spin states.

CONCLUSIONS

Humans have little ability to detect brief alterations in the geomagnetic field, even if these alteration are of a large magnitude.

Prediction of epileptic seizures

For almost 40 years, neuroscientists thought that epileptic seizures began abruptly, just a few seconds before clinical attacks. There is now mounting evidence that seizures develop minutes to hours before clinical onset. This change in thinking is based on quantitative studies of long digital intracranial electroencephalographic (EEG) recordings from patients being evaluated for epilepsy surgery. Evidence that seizures can be predicted is spread over diverse sources in medical, engineering, and patent publications. Techniques used to forecast seizures include frequency-based methods, statistical analysis of EEG signals, non-linear dynamics (chaos), and intelligent engineered systems. Advances in seizure prediction promise to give rise to implantable devices able to warn of impending seizures and to trigger therapy to prevent clinical epileptic attacks. Treatments such as electrical stimulation or focal drug infusion could be given on demand and might eliminate side-effects in some patients taking antiepileptic drugs long term. Whether closed-loop seizure-prediction and treatment devices will have the profound clinical effect of their cardiological predecessors will depend on our ability to perfect these techniques. Their clinical efficacy must be validated in large-scale, prospective, controlled trials.