Subcortical (thalamic) automated seizure detection: A new option for contingent therapy delivery.

The feasibility of automated detection of cortical-onset epileptic seizures from subcortical structures such as the thalamus was investigated via simultaneous recording of electroencephalography (EEG) and anterior and centromedian thalamic nuclei electrical signals (electrothalamography) in nine subjects with pharmacoresistant seizures admitted to an epilepsy monitoring unit after deep brain stimulating electrode implantation. Thalamic electrical signals were analyzed using a validated seizure detection algorithm, and times of seizure onset and termination were compared to those determined through visual analysis of video-EEG. Ictal activity was recorded from the scalp and thalamic nuclei in three subjects who had seizures during the 3-4-day recording period. In the majority of seizures, ictal activity in the thalamic nuclei preceded electrographic onset as determined from the EEG or clinical onset as determined from behavioral observations. Interictal epileptiform discharges were also recorded from the thalamus and in certain instances had no scalp representation. Subcortical/thalamic detection of cortical-onset seizures is feasible. This approach would enable contingent therapy delivery and may be particularly valuable for subjects with multiple or difficult-to-localize epileptogenic regions.

Alterations in skin temperature and sleep in the fear of harm phenotype of pediatric bipolar disorder.

In children diagnosed with pediatric bipolar disorder (PBD), disturbances in the quality of sleep and wakefulness are prominent. A novel phenotype of PBD called Fear of Harm (FOH) associated with separation anxiety and aggressive obsessions is associated with sleep onset insomnia, parasomnias (nightmares, night-terrors, enuresis), REM sleep-related problems, and morning sleep inertia. Children with FOH often experience thermal discomfort (e.g. feeling hot, excessive sweating) in neutral ambient temperature conditions, as well as no discomfort during exposure to the extreme cold, and alternate noticeably between being excessively hot in the evening and cold in the morning. We hypothesized that these sleep- and temperature-related symptoms were overt symptoms of an impaired ability to dissipate heat, particularly in the evening hours near the time of sleep onset. We measured sleep/wake variables using actigraphy, and nocturnal skin temperature variables using thermal patches and a wireless device, and compared these data between children with PBD/FOH and a control sample of healthy children. The results are suggestive of a thermoregulatory dysfunction that is associated with sleep onset difficulties. Further, they are consistent with our hypothesis that alterations in neural circuitry common to thermoregulation and emotion regulation underlie affective and behavioral symptoms of the FOH phenotype.

Patient and caregiver perspectives on seizure prediction.

One of the goals of the Fourth International Workshop on Seizure Prediction was to provide an opportunity for patients with epilepsy and their caregivers to voice their perspectives on seizure prediction and related matters toward the goal of influencing the design of solutions. In an attempt to fulfill this goal, a survey of patients and caregivers, who often make or influence patient choices, was conducted on issues pertaining to living with epilepsy, epilepsy treatments, seizure prediction, and the use of implantable devices for the control of seizures. The results of this survey are reported here.

Epileptic seizures: Quakes of the brain?

A dynamical analogy supported by five scale-free statistics (the Gutenberg-Richter distribution of event sizes, the distribution of interevent intervals, the Omori and inverse Omori laws, and the conditional waiting time until the next event) is shown to exist between two classes of seizures ("focal" in humans and generalized in animals) and earthquakes. Increments in excitatory interneuronal coupling in animals expose the system's dependence on this parameter and its dynamical transmutability: moderate increases lead to power-law behavior of seizure energy and interevent times, while marked ones to scale-free (power-law) coextensive with characteristic scales and events. The coextensivity of power law and characteristic size regimes is predicted by models of coupled heterogeneous threshold oscillators of relaxation and underscores the role of coupling strength in shaping the dynamics of these systems.

Seizure prediction: the Fourth International Workshop.

The recently convened Fourth International Workshop on Seizure Prediction (IWSP4) brought together a diverse international group of investigators, from academia and industry, including epileptologists, neurosurgeons, neuroscientists, computer scientists, engineers, physicists, and mathematicians who are conducting interdisciplinary research on the prediction and control of seizures. IWSP4 allowed the presentation and discussion of results, an exchange of ideas, an assessment of the status of seizure prediction, control, and related fields, and the fostering of collaborative projects.

Controversies in epilepsy: debates held during the Fourth International Workshop on Seizure Prediction.

Debates on six controversial topics were held during the Fourth International Workshop on Seizure Prediction (IWSP4) convened in Kansas City, KS, USA, July 4-7, 2009. The topics were (1) Ictogenesis: Focus versus Network? (2) Spikes and Seizures: Step-relatives or Siblings? (3) Ictogenesis: A Result of Hyposynchrony? (4) Can Focal Seizures Be Caused by Excessive Inhibition? (5) Do High-Frequency Oscillations Provide Relevant Independent Information? (6) Phase Synchronization: Is It Worthwhile as Measured? This article, written by the IWSP4 organizing committee and the debaters, summarizes the arguments presented during the debates.

Pharmaco-resistant seizures: self-triggering capacity, scale-free properties and predictability?

Relevant and timely questions such as regarding the predictability of seizures and their capacity to trigger more seizures remain the subject of debate in epileptology. The present study endeavors to gain insight into these dynamic issues by adopting a non-reductionist approach and via the use of mathematical tools. Probability distribution functions of seizure energies and inter-seizure intervals and the probability of seizure occurrence conditional upon the time elapsed from the previous seizure were estimated from prolonged recordings from subjects with pharmaco-resistant seizures, undergoing surgical evaluation, on reduced doses of or on no medications. The energy and inter-seizure interval distributions for pharmaco-resistant seizures, under the prevailing study conditions, are governed by power laws ('scale-free' behavior). Pharmaco-resistant seizures tend to occur in clusters and the time to the next seizure increases with the duration of the seizure-free interval since the last one. However, characteristic size energy probability density functions were found in a few subjects. These findings suggests that: (i) pharmaco-resistant seizures have an inherent self-triggering capacity; (ii) their time of occurrence and intensity may be predictable in light of the existence of power law distributions and of their self-triggering capacity; and (iii) their lack of typical size and duration (scale-free), features upon which their classification into ictal or interictal is largely based, may be inadequate/insufficient classifiers.

Detection of seizure rhythmicity by recurrences.

Epileptic seizures show a certain degree of rhythmicity, a feature of heuristic and practical interest. In this paper, we introduce a simple model of this type of behavior, and suggest a measure for detecting and quantifying it. To evaluate our method, we develop a set of test segments that incorporate rhythmicity features, and present results from the application of this measure to test segments. We then analyze electrocorticogram segments containing seizures, and present two examples. Finally, we discuss the similarity of our method to techniques for detecting unstable periodic orbits in chaotic time series.

Epileptic seizures are temporally interdependent under certain conditions.

PURPOSE: The possibility that seizures may be intercorrelated has not been sufficiently investigated. A handful of studies, the majority based on patient seizure diaries, provide disparate results: some claim that seizures are serially correlated and others that they are random events. This study investigates the effect that a seizure may have on the time of occurrence and severity of subsequent ones in subjects undergoing invasive surgical evaluation. METHODS: The Savit-Green statistic, a measure of time series lag dependency, was applied to seizure sequences derived from the ECoGs of 26 epilepsy surgery candidates. Seizure onset times, intensities and durations were obtained using a validated seizure detection algorithm, and from these, inter-seizure intervals (ISI) and severities were computed and their lag dependencies were compared to suitably randomized and amplitude-scaled linear surrogate sets. RESULTS: The null hypothesis (seizures are uncorrelated) was rejected (p<0.05) for ISI in 12/26 subjects and for seizure severity in 13/26. The temporal correlations spanned up to three preceding seizures and were nonlinear in 7/12 subjects for ISI and in 8/13 for severity. An important finding is that dependencies may be related to the frequency of seizures in the sample. CONCLUSIONS: This study demonstrates that under certain conditions, there are linear and nonlinear seizure dependencies of low order and at small time scales (minutes to hours), for ISI and seizure severity. This observation has important implications for studies of seizure predictability, which de facto treat seizures as independent occurrences. Given the study subjects' conditions, it is not clear if the dependencies reflect innate brain dynamics, drug withdrawal, local trauma or a combination of these.

Characterization of synchrony with applications to epileptic brain signals.

Measurement of synchrony in networks of complex or high-dimensional, nonstationary, and noisy systems such as the mammalian brain is technically difficult. We present a general method to analyze synchrony from multichannel time series. The idea is to calculate the phase-synchronization times and to construct a matrix. We develop a random-matrix-based criterion for proper choosing of the diagonal matrix elements. Monitoring of the eigenvalues and the determinant provides an effective way to assess changes in synchrony. The method is tested using a prototype nonstationary dynamical system, electroencephalogram (scalp) data from absence seizures for which enhanced synchrony is presumed, and electrocorticogram (intracranial) data from subjects having partial seizures with secondary generalization.

Strategies for adapting automated seizure detection algorithms.

The time-varying dynamics of epileptic seizures and the high inter-individual variability make their detection difficult. Osorio et al. [Osorio, I, Frei, MG, Wilkinson, SB. Real-time automated detection and quantitative analysis of seizures and short-term prediction of clinical onset. Epilepsia 1998;39(6):615-27] developed an algorithm that has had success in detecting seizures. We present a new strategy for adapting this algorithm or other algorithms to an individual's seizure fingerprint using both seizure and non-seizure training segments and a novel performance criterion that directly incorporates the non-linearity and lack of differentiability of the algorithm. The joint optimization of a linear filter chosen from a bank of candidate filters and of a percentile used in order statistic filtering provides an empirical solution that is both practical and useful, which should translate into improved sensitivity, specificity and detection speed. This premise is strongly supported by the results obtained in a large validation study and the examples illustrated in this article. This strategy is generalizable to other detection algorithms with modular architecture and spectral filters.

Detecting and characterizing phase synchronization in nonstationary dynamical systems.

We propose a general framework for detecting and characterizing phase synchronization from noisy, nonstationary time series. For detection, we propose to use the average phase-synchronization time and show that it is extremely sensitive to parameter changes near the onset of phase synchronization. To characterize the degree of temporal phase synchronization, we suggest to monitor the evolution of phase diffusion from a moving time window and argue that this measure is practically useful as it can be enhanced by increasing the size of the window. While desynchronization events can be caused by either a lack of sufficient deterministic coupling or noise, we demonstrate that the time scales associated with the two mechanisms are quite different. In particular, noise-induced desynchronization events tend to occur on much shorter time scales. This allows for the effect of noise on phase synchronization to be corrected in a practically doable manner. We perform a control study to substantiate these findings by constructing and investigating a prototype model of nonstationary dynamical system that consists of coupled chaotic oscillators with time-varying coupling parameter.

Analog seizure detection and performance evaluation.

Epilepsy is the most prevalent neurological disorder affecting both adults and children. Over two-and-one-half million individuals in the United States have epilepsy and 25% of them do not respond to drugs. A significant focus of current research efforts is the development of a fully implantable device for real-time seizure detection and automated warning and blockage of seizures. The purpose of this paper is to describe and demonstrate the feasibility of incorporating a novel tool, the percentile tracking filter into a successful, validated seizure detection algorithm to create an analog seizure detection device. We demonstrate, in a small-scale study, that the performance of this analog implementation is statistically similar to a digital implementation of a previously described and successfully validated seizure digital algorithm. This analog implementation can be realized into an application specific integrated circuit that is suitable for a fully implantable device for seizure monitoring, warning and treatment, which is likely to consume very little power, a feature of practical value.

Correlation dimension and integral do not predict epileptic seizures.

Reports in the literature have indicated potential value of the correlation integral and dimension for prediction of epileptic seizures up to several minutes before electrographic onset. We apply these measures to over 2000 total hours of continuous electrocortiogram, taken from 20 patients with epilepsy, examine their sensitivity to quantifiable properties such as the signal amplitude and autocorrelation, and investigate the influence of embedding and filtering strategies on their performance. The results are compared against those obtained from surrogate time series. Our conclusion is that neither the correlation dimension nor the correlation integral has predictive power for seizures.

Accumulated energy revisited.

OBJECTIVE: To examine the seizure prediction and detection abilities of the accumulated energy on multi-center data submitted to the First International Collaborative Workshop on Seizure Prediction. METHODS: The accumulated energy (AE), windowed average power, and FHS seizure detection algorithm were applied to a single channel of ECoG data taken from the data sets contributed to the workshop. The FHS seizure detection algorithm was used to perform automated scoring of the data in order to locate subclinical events not picked up by the centers where the data was collected. The results were analyzed retrospectively, comparing the behavior of the accumulated energy and windowed average power on segments containing seizures to interictal segments. RESULTS: Accumulated energy curves showed no divergence from interictal curves prior to seizure. Distinctive or clear increases in the AE slope occurred sometime at or after electrographic seizure onset for some seizures. Similarly, the windowed average power showed no consistent increases in broadband energy prior to seizures. However, both methods may have detection ability for some seizures. CONCLUSIONS: The accumulated energy did not appear to have predictive abilities for these data sets. Some detection ability was apparent. SIGNIFICANCE: In data unsorted by sleep/wake state, no seizure prediction was evident. The lack of prediction calls into question the existence of a preictal state as previously claimed in the literature using this method.

Automated seizure abatement in humans using electrical stimulation.

The need for novel, efficacious, antiseizure therapies is widely acknowledged. This study investigates in humans the feasibility, safety, and efficacy of high-frequency electrical stimulation (HFES; 100-500 Hz) triggered by automated seizure detections. Eight patients were enrolled in this study, which consisted of a control and an experimental phase. HFES was delivered directly to the epileptogenic zone (local closed-loop) in four patients and indirectly, through anterior thalami (remote closed-loop), to the other four patients for every other automated seizure detection made by a validated algorithm. Interphase (control vs experimental phase) and intraphase (stimulated vs nonstimulated) comparisons of clinical seizure rate and relative severity (clinical and electrographic) were performed, and differences were assessed using effect size. Patients were deemed "responders" if seizure rate was reduced by at least 50%; the remaining patients were deemed "nonresponders." All patients completed the study; rescue medications were not required. There were 1,491 HFESs (0.2% triggered after-discharges). Mean change in seizure rate in the local closed-loop group was -55.5% (-100 to +36.8%); three of four responders had a mean change of -86% (-100 to -58.8%). In the remote closed-loop, the mean change of seizure rate was -40.8% (-72.9 to +1.4%); two of four responders had a mean change of -74.3% (-75.6 to -72.9%). Mean effect size was zero in the local closed-loop (responders: beneficial and medium to large in magnitude) and negligible in the remote closed-loop group (responders: beneficial and medium to large). HFES effects on epileptogenic tissue were immediate and also outlasted the stimulation period. This study demonstrates the feasibility and short-term safety of automated HFES for seizure blockage, and also raises the possibility that it may be beneficial in pharmaco-resistant epilepsies.

Controlled test for predictive power of Lyapunov exponents: their inability to predict epileptic seizures.

Lyapunov exponents are a set of fundamental dynamical invariants characterizing a system's sensitive dependence on initial conditions. For more than a decade, it has been claimed that the exponents computed from electroencephalogram (EEG) or electrocorticogram (ECoG) signals can be used for prediction of epileptic seizures minutes or even tens of minutes in advance. The purpose of this paper is to examine the predictive power of Lyapunov exponents. Three approaches are employed. (1) We present qualitative arguments suggesting that the Lyapunov exponents generally are not useful for seizure prediction. (2) We construct a two-dimensional, nonstationary chaotic map with a parameter slowly varying in a range containing a crisis, and test whether this critical event can be predicted by monitoring the evolution of finite-time Lyapunov exponents. This can thus be regarded as a "control test" for the claimed predictive power of the exponents for seizure. We find that two major obstacles arise in this application: statistical fluctuations of the Lyapunov exponents due to finite time computation and noise from the time series. We show that increasing the amount of data in a moving window will not improve the exponents' detective power for characteristic system changes, and that the presence of small noise can ruin completely the predictive power of the exponents. (3) We report negative results obtained from ECoG signals recorded from patients with epilepsy. All these indicate firmly that, the use of Lyapunov exponents for seizure prediction is practically impossible as the brain dynamical system generating the ECoG signals is more complicated than low-dimensional chaotic systems, and is noisy.

Gaussian mixture models of ECoG signal features for improved detection of epileptic seizures.

PURPOSE: To investigate the potential for improving the performance of the Osorio-Frei seizure detection algorithm (OFA) by incorporating multiple FIR filters operating in parallel and Gaussian mixture models (GMM) for ECoG features distributions, thus creating "hybrid" system. METHODS: The "hybrid" algorithm decomposes the signal into four subbands, using wavelets, after which relevant features are extracted for each subband. Following these steps, multivariate GMM are developed for seizure and non-seizure states, using training segments. State classification is based on thresholding of the likelihood ratio of seizure vs. non-seizure data. Multiple comparisons are performed between this "hybrid" and a modified version of the OFA suitable for this purpose, using as indices false positives (FP), false negatives (FN) and speed of detection. RESULTS: GMM improved speed of detection over the modified OFA at negligible FP levels. The average detection delay from expert visually placed electrographic onset over all seizures was reduced from 4.8 s for modified OFA to 1.8 s for GMM (p < 0.002) Individualized training by subject proved superior to group-based training. CONCLUSIONS: This work introduces multi-feature extraction from ECoG signals together with use of Gaussian mixtures to model them, as tools to improve automated seizure detection. At the clinical level, this approach appears to increase warning time and with it the window during which safety measures and seizure blockage may be implemented, at an affordable computational cost and with negligible FP rate.

Inability of Lyapunov exponents to predict epileptic seizures.

It has been claimed that Lyapunov exponents computed from electroencephalogram or electrocorticogram (ECoG) time series are useful for early prediction of epileptic seizures. We show, by utilizing a paradigmatic chaotic system, that there are two major obstacles that can fundamentally hinder the predictive power of Lyapunov exponents computed from time series: finite-time statistical fluctuations and noise. A case study with an ECoG signal recorded from a patient with epilepsy is presented.