Seizure anticipation: from algorithms to clinical practice

The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives

Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus-a discrete cortical area from which seizures originate-to a widespread epileptic network-spanning lobes and hemispheres-considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.

The performance evaluation of the state-of-the-art EEG-based seizure prediction models

The recurrent and unpredictable nature of seizures can lead to unintentional injuries and even death. The rapid development of electroencephalogram (EEG) and Artificial Intelligence (AI) technologies has made it possible to predict seizures in real-time through brain-machine interfaces (BCI), allowing advanced intervention. To date, there is still much room for improvement in predictive seizure models constructed by EEG using machine learning (ML) and deep learning (DL). But, the most critical issue is how to improve the performance and generalization of the model, which involves some confusing conceptual and methodological issues. This review focuses on analyzing several factors affecting the performance of seizure prediction models, focusing on the aspects of post-processing, seizure occurrence period (SOP), seizure prediction horizon (SPH), and algorithms. Furthermore, this study presents some new directions and suggestions for building high-performance prediction models in the future. We aimed to clarify the concept for future research in related fields and improve the performance of prediction models to provide a theoretical basis for future applications of wearable seizure detection devices.

Wireless Closed-Loop Optical Regulation System for Seizure Detection and Suppression In Vivo

There are approximately 50 million people with epilepsy worldwide, even about 25% of whom cannot be effectively controlled by drugs or surgical treatment. A wireless closed-loop system for epilepsy detection and suppression is proposed in this study. The system is composed of an implantable optrode, wireless recording, wireless energy supply, and a control module. The system can monitor brain electrical activity in real time. When seizures are recognized, the optrode will be turned on. The preset photosensitive caged compounds are activated to inhibit the seizure. When seizures are inhibited or end, the optrode is turned off. The method demonstrates a practical wireless closed-loop epilepsy therapy system.

The potential of closed-loop endovascular neurostimulation as a viable therapeutic approach for drug-resistant epilepsy: A critical review

Over the last few decades, biomedical implants have successfully delivered therapeutic electrical stimulation to reduce the frequency and severity of seizures in people with drug-resistant epilepsy. However, neurostimulation approaches require invasive surgery to implant stimulating electrodes, and surgical, medical, and hardware complications are not uncommon. An endovascular approach provides a potentially safer and less invasive surgical alternative. This article critically evaluates the feasibility of endovascular closed-loop neuromodulation for the treatment of epilepsy. By reviewing literature that reported the impact of direct electrical stimulation to reduce the frequency of epileptic seizures, we identified clinically validated extracranial, cortical, and deep cortical neural targets. We identified veins in close proximity to these targets and evaluated the potential of delivering an endovascular implant to these veins based on their diameter. We then compared the risks and benefits of existing technology to describe a benchmark of clinical safety and efficacy that would need to be achieved for endovascular neuromodulation to provide therapeutic benefit. For the majority of brain regions that have been clinically demonstrated to reduce seizure occurrence in response to delivered electrical stimulation, vessels of appropriate diameter for delivery of an endovascular electrode to these regions could be achieved. This includes delivery to the vagus nerve via the 13.2 ± 0.9 mm diameter internal jugular vein, the motor cortex via the 6.5 ± 1.7 mm diameter superior sagittal sinus, and the cerebellum via the 7.7 ± 1.4 mm diameter sigmoid sinus or 6.2 ± 1.4 mm diameter transverse sinus. Deep cerebral targets can also be accessed with an endovascular approach, with the 1.9 ± 0.5 mm diameter internal cerebral vein and 1.2-mm-diameter thalamostriate vein lying in close proximity to the anterior and centromedian nuclei of the thalamus, respectively. This work identified numerous veins that are in close proximity to conventional stimulation targets that are of a diameter large enough for delivery and deployment of an endovascular electrode array, supporting future work to assess clinical efficacy and chronic safety of an endovascular approach to deliver therapeutic neurostimulation.

One-Step, Three-Factor Passthought Authentication With Custom-Fit, In-Ear EEG

In-ear EEG offers a promising path toward usable, discreet brain-computer interfaces (BCIs) for both healthy individuals and persons with disabilities. To test the promise of this modality, we produced a brain-based authentication system using custom-fit EEG earpieces. In a sample of N = 7 participants, we demonstrated that our system has high accuracy, higher than prior work using non-custom earpieces. We demonstrated that both inherence and knowledge factors contribute to authentication accuracy, and performed a simulated attack to show our system's robustness against impersonation. From an authentication standpoint, our system provides three factors of authentication in a single step. From a usability standpoint, our system does not require a cumbersome, head-worn device.

Transition of brain networks from an interictal to a preictal state preceding a seizure revealed by scalp EEG network analysis

Epilepsy is a neurological disorder in the brain that is characterized by unprovoked seizures. Epileptic seizures are attributed to abnormal synchronous neuronal activity in the brain. To detect the seizure as early as possible, the identification of specific electroencephalogram (EEG) dynamics is of great importance in investigating the transition of brain activity as the epileptic seizure approaches. In this study, we investigated the transition of brain activity from interictal to preictal states preceding a seizure by combining EEG network and clustering analyses together in different frequency bands. The findings of this study demonstrated the best clustering performance of k-medoids in the beta band; in addition, compared to the interictal state, the preictal state experienced increased synchronization of EEG network connectivity, characterized by relatively higher network properties. These findings can provide helpful insight into the mechanism of epilepsy, which can also be used in the prediction of epileptic seizures and subsequent intervention.

Predicting state transitions in brain dynamics through spectral difference of phase-space graphs

Networks are naturally occurring phenomena that are studied across many disciplines. The topological features of a network can provide insight into the dynamics of a system as it evolves, and can be used to predict changes in state. The brain is a complex network whose temporal and spatial behavior can be measured using electroencephalography (EEG). This data can be reconstructed to form a family of graphs that represent the state of the brain over time, and the evolution of these graphs can be used to predict changes in brain states, such as the transition from preictal to ictal in patients with epilepsy. This research proposes objective indications of seizure onset observed from minimally invasive scalp EEG. The approach considers the brain as a complex nonlinear dynamical system whose state can be derived through time-delay embedding of the EEG data and characterized to determine change in brain dynamics related to the preictal state. This method targets phase-space graph spectra as biomarkers for seizure prediction, correlates historical degrees of change in spectra, and makes accurate prediction of seizure onset. A significant trend of normalized dissimilarity over time indicates a departure from the norm, and thus a change in state. Our methods show high sensitivity (90-100%) and specificity (90%) on 241 h of scalp EEG training data, and sensitivity and specificity of 70%-90% on test data. Moreover, the algorithm was capable of processing 12.7 min of data per second on an Intel Core i3 CPU in Matlab, showing that real-time analysis is viable.

Wearable technology in epilepsy: The views of patients, caregivers, and healthcare professionals

PURPOSE

In recent years, digital technology and wearable devices applied to seizure detection have progressively become available. In this study, we investigated the perspectives of people with epilepsy (PWE), caregivers (CG), and healthcare professionals (HP). We were interested in their current use of digital technology as well as their willingness to use wearables to monitor seizures. We also explored the role of factors influencing engagement with technology, including demographic and clinical characteristics, data confidentiality, need for technical support, and concerns about strain or increased workload.

METHODS

An online survey drawing on previous data collected via focus groups was constructed and distributed via a web link. Using logistic regression analyses, demographic, clinical, and other factors identified to influence engagement with technology were correlated with reported use and willingness to use digital technology and wearables for seizure tracking.

RESULTS

Eighty-seven surveys were completed, fifty-two (59.7%) by PWE, 13 (14.4%) by CG, and 22 (25.3%) by HP. Responders were familiar with multiple digital technologies, including the Internet, smartphones, and personal computers, and the use of digital services was similar to the UK average. Moreover, age and disease-related factors did not influence access to digital technology. The majority of PWE were willing to use a wearable device for long-term seizure tracking. However, only a limited number of PWE reported current regular use of wearables, and nonusers attributed their choice to uncertainty about the usefulness of this technology in epilepsy care. People with epilepsy envisaged the possibility of understanding their condition better through wearables and considered, with caution, the option to send automatic emergency calls. Despite concerns around accuracy, data confidentiality, and technical support, these factors did not limit PWE's willingness to use digital technology. Caregivers appeared willing to provide support to PWE using wearables and perceived a reduction of their workload and anxiety. Healthcare professionals identified areas of application for digital technologies in their clinical practice, pending an appropriate reorganization of the clinical team to share the burden of data reviewing and handling.

CONCLUSIONS

Unlike people who have other chronic health conditions, PWE appeared not to be at risk of digital exclusion. This study highlighted a great interest in the use of wearable technology across epilepsy service users, carers, and healthcare professionals, which was independent of demographic and clinical factors and outpaced data security and technology usability concerns.

Bio-electrochemical microelectrode arrays for glutamate and electrophysiology detection in hippocampus of temporal lobe epileptic rats

Temporal Lobe Epilepsy (TLE) is a chronic neurological disorder, characterized by sudden, repeated and transient central nervous system dysfunction. For better understanding of TLE, bio-nanomodified microelectrode arrays (MEA) are designed, for the achievement of high-quality simultaneous detection of glutamate signals (Glu) and multi-channel electrophysiological signals including action potentials (spikes) and local field potentials (LFPs). The MEA was fabricated by Micro-Electro-Mechanical System fabrication technology and all recording sites were modified with platinum black nano-particles, the average impedance decreased by nearly 90 times. Additionally, glutamate oxidase was also modified for the detection of Glu. The average sensitivity of the electrode in Glu solution was 1.999 ± 0.032 × 10^-2pA/μM·μm²(n = 3) and linearity was R = 0.9986, with a good selectivity of 97.82% for glutamate and effective blocking of other interferents. In the in-vivo experiments, the MEA was subjected in hippocampus to electrophysiology and Glu concentration detection. During seizures, the fire rate of spikes increases, and the interspike interval is concentrated within 30 ms. The amplitude of LFPs increases by 3 times and the power increases. The Glu level (4.22 μM, n = 4) was obviously higher than normal rats (2.24 μM, n = 4). The MEA probe provides an advanced tool for the detection of dual-mode signals in the research of neurological diseases.

Epileptic Seizure Prediction Using Diffusion Distance and Bayesian Linear Discriminate Analysis on Intracranial EEG

Epilepsy is a chronic neurological disorder characterized by sudden and apparently unpredictable seizures. A system capable of forecasting the occurrence of seizures is crucial and could open new therapeutic possibilities for human health. This paper addresses an algorithm for seizure prediction using a novel feature — diffusion distance (DD) in intracranial Electroencephalograph (iEEG) recordings. Wavelet decomposition is conducted on segmented electroencephalograph (EEG) epochs and subband signals at scales 3, 4 and 5 are utilized to extract the diffusion distance. The features of all channels composing a feature vector are then fed into a Bayesian Linear Discriminant Analysis (BLDA) classifier. Finally, postprocessing procedure is applied to reduce false prediction alarms. The prediction method is evaluated on the public intracranial EEG dataset, which consists of 577.67[Formula: see text]h of intracranial EEG recordings from 21 patients with 87 seizures. We achieved a sensitivity of 85.11% for a seizure occurrence period of 30[Formula: see text]min and a sensitivity of 93.62% for a seizure occurrence period of 50[Formula: see text]min, both with the seizure prediction horizon of 10[Formula: see text]s. Our false prediction rate was 0.08/h. The proposed method yields a high sensitivity as well as a low false prediction rate, which demonstrates its potential for real-time prediction of seizures.

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.

METHODS

The seizure prediction system was developed based on the extraction of correlation dimension, correlation entropy, noise level, Lempel-Ziv complexity, largest Lyapunov exponent, and nonlinear interdependence from segments of intracranial EEG.

RESULTS

Our results showed an average sensitivity of 86.7% and 92.9%, an average false prediction rate of 0.126 and 0.096/h, and an average minimum prediction time of 14.3 and 33.3min, respectively, using seizure occurrence periods of 30 and 50min and a seizure prediction horizon of 10s. Two-third of the analyzed seizures showed significantly increased complexity in periods prior to the seizures in comparison with baseline. In four patients, strong bidirectional connectivities between epileptic contacts and the surrounding areas were observed. However, in five patients, unidirectional functional connectivities in preictal periods were observed from remote areas to epileptogenic zones.

CONCLUSIONS

Overall, preictal periods in patients with focal hippocampal epilepsy were characterized with patient-specific changes in univariate and bivariate nonlinear measures.

SIGNIFICANCE

The spatio-temporal characterization of preictal periods may help to better understand the mechanism underlying seizure generation in patients with focal hippocampal epilepsy.

Non-EEG seizure detection systems and potential SUDEP prevention: State of the art: Review and update

PURPOSE

Detection of, and alarming for epileptic seizures is increasingly demanded and researched. Our previous review article provided an overview of non-invasive, non-EEG (electro-encephalography) body signals that can be measured, along with corresponding methods, state of the art research, and commercially available systems. Three years later, many more studies and devices have emerged. Moreover, the boom of smart phones and tablets created a new market for seizure detection applications.

METHOD

We performed a thorough literature review and had contact with manufacturers of commercially available devices.

RESULTS

This review article gives an updated overview of body signals and methods for seizure detection, international research and (commercially) available systems and applications. Reported results of non-EEG based detection devices vary between 2.2% and 100% sensitivity and between 0 and 3.23 false detections per hour compared to the gold standard video-EEG, for seizures ranging from generalized to convulsive or non-convulsive focal seizures with or without loss of consciousness. It is particularly interesting to include monitoring of autonomic dysfunction, as this may be an important pathophysiological mechanism of SUDEP (sudden unexpected death in epilepsy), and of movement, as many seizures have a motor component.

CONCLUSION

Comparison of research results is difficult as studies focus on different seizure types, timing (night versus day) and patients (adult versus pediatric patients). Nevertheless, we are convinced that the most effective seizure detection systems are multimodal, combining for example detection methods for movement and heart rate, and that devices should especially take into account the user's seizure types and personal preferences.

Classifying normal and abnormal status based on video recordings of epileptic patients

Based on video recordings of the movement of the patients with epilepsy, this paper proposed a human action recognition scheme to detect distinct motion patterns and to distinguish the normal status from the abnormal status of epileptic patients. The scheme first extracts local features and holistic features, which are complementary to each other. Afterwards, a support vector machine is applied to classification. Based on the experimental results, this scheme obtains a satisfactory classification result and provides a fundamental analysis towards the human-robot interaction with socially assistive robots in caring the patients with epilepsy (or other patients with brain disorders) in order to protect them from injury.

Predicting epileptic seizures in advance

Epilepsy is the second most common neurological disorder, affecting 0.6-0.8% of the world's population. In this neurological disorder, abnormal activity of the brain causes seizures, the nature of which tend to be sudden. Antiepileptic Drugs (AEDs) are used as long-term therapeutic solutions that control the condition. Of those treated with AEDs, 35% become resistant to medication. The unpredictable nature of seizures poses risks for the individual with epilepsy. It is clearly desirable to find more effective ways of preventing seizures for such patients. The automatic detection of oncoming seizures, before their actual onset, can facilitate timely intervention and hence minimize these risks. In addition, advance prediction of seizures can enrich our understanding of the epileptic brain. In this study, drawing on the body of work behind automatic seizure detection and prediction from digitised Invasive Electroencephalography (EEG) data, a prediction algorithm, ASPPR (Advance Seizure Prediction via Pre-ictal Relabeling), is described. ASPPR facilitates the learning of predictive models targeted at recognizing patterns in EEG activity that are in a specific time window in advance of a seizure. It then exploits advanced machine learning coupled with the design and selection of appropriate features from EEG signals. Results, from evaluating ASPPR independently on 21 different patients, suggest that seizures for many patients can be predicted up to 20 minutes in advance of their onset. Compared to benchmark performance represented by a mean S1-Score (harmonic mean of Sensitivity and Specificity) of 90.6% for predicting seizure onset between 0 and 5 minutes in advance, ASPPR achieves mean S1-Scores of: 96.30% for prediction between 1 and 6 minutes in advance, 96.13% for prediction between 8 and 13 minutes in advance, 94.5% for prediction between 14 and 19 minutes in advance, and 94.2% for prediction between 20 and 25 minutes in advance.

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

OBJECTIVES

The aim of this study is to develop a model based seizure prediction method.

METHODS

A 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.

RESULTS

Tuned 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 10s, 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.

CONCLUSIONS

The spatio-temporal changes in the parameters demonstrated patient-specific preictal signatures that could be used for seizure prediction.

SIGNIFICANCE

The present findings suggest that the model-based approach may aid prediction of seizures.

Closed-loop neural stimulation for pentylenetetrazole-induced seizures in zebrafish

Neural stimulation can reduce the frequency of seizures in persons with epilepsy, but rates of seizure-free outcome are low. Vagus nerve stimulation prevents seizures by continuously activating noradrenergic projections from the brainstem to the cortex. Cortical norepinephrine then increases GABAergic transmission and increases seizure threshold. Another approach, responsive nervous stimulation, prevents seizures by reactively shocking the seizure onset zone in precise synchrony with seizure onset. The electrical shocks abort seizures before they can spread and manifest clinically. The goal of this study was to determine whether a hybrid platform in which brainstem activation triggered in response to impending seizure activity could prevent seizures. We chose the zebrafish as a model organism for this study because of its ability to recapitulate human disease, in conjunction with its innate capacity for tightly controlled high-throughput experimentation. We first set out to determine whether electrical stimulation of the zebrafish hindbrain could have an anticonvulsant effect. We found that pulse train electrical stimulation of the hindbrain significantly increased the latency to onset of pentylenetetrazole-induced seizures, and that this apparent anticonvulsant effect was blocked by noradrenergic antagonists, as is also the case with rodents and humans. We also found that the anticonvulsant effect of hindbrain stimulation could be potentiated by reactive triggering of single pulse electrical stimulations in response to impending seizure activity. Finally, we found that the rate of stimulation triggering was directly proportional to pentylenetetrazole concentration and that the stimulation rate was reduced by the anticonvulsant valproic acid and by larger stimulation currents. Taken as a whole, these results show that that the anticonvulsant effect of brainstem activation can be efficiently utilized by reactive triggering, which suggests that alternative stimulation paradigms for vagus nerve stimulation might be useful. Moreover, our results show that the zebrafish epilepsy model can be used to advance our understanding of neural stimulation in the treatment of epilepsy.

Dynamic characteristics of absence EEG recordings with multiscale permutation entropy analysis

Understanding the transition of brain activities towards an absence seizure, called pre-epileptic seizure, is a challenge. In this study, multiscale permutation entropy (MPE) is proposed to describe dynamical characteristics of electroencephalograph (EEG) recordings on different absence seizure states. The classification ability of the MPE measures using linear discriminant analysis is evaluated by a series of experiments. Compared to a traditional multiscale entropy method with 86.1% as its classification accuracy, the classification rate of MPE is 90.6%. Experimental results demonstrate there is a reduction of permutation entropy of EEG from the seizure-free state to the seizure state. Moreover, it is indicated that the dynamical characteristics of EEG data with MPE can identify the differences among seizure-free, pre-seizure and seizure states. This also supports the view that EEG has a detectable change prior to an absence seizure.

Seizure prediction in intracranial EEG: a patient-specific rule-based approach

In this study, we report our development of a patient-specific rule-based seizure prediction system. Five univariate and one bivariate nonlinear measures were extracted from non-overlapping 10-second segments of intracranial EEG (iEEG) data recorded using both depth electrodes in the brain and subdural electrodes over the cortical surface. Nonlinear features representing the specific characteristic properties of EEG signal were then integrated spatio-temporally in a way to predict to predict seizure with high sensitivity. The present system was tested on 58 hours of iEEG data containing ten seizures recorded in two patients with medically intractable focal epilepsy. Within a prediction horizon of 30 and 60 minutes, our method showed an average sensitivity of 90% and 96.5% with an average false prediction rate of 0.06/h and 0.055/h, respectively. The present results suggest that such a rule-based system can become potentially a useful approach for predicting seizures prior to onset.

A rule-based seizure prediction method for focal neocortical epilepsy

OBJECTIVE

In the present study, we have developed a novel patient-specific rule-based seizure prediction system for focal neocortical epilepsy.

METHODS

Five univariate measures including correlation dimension, correlation entropy, noise level, Lempel-Ziv complexity, and largest Lyapunov exponent as well as one bivariate measure, nonlinear interdependence, were extracted from non-overlapping 10-s segments of intracranial electroencephalogram (iEEG) data recorded using electrodes implanted deep in the brain and/or placed on the cortical surface. The spatio-temporal information was then integrated by using rules established based on patient-specific changes observed in the period prior to a seizure sample for each patient. The system was tested on 316 h of iEEG data containing 49 seizures recorded in 11 patients with medically intractable focal neocortical epilepsy.

RESULTS

For seizure occurrence periods of 30 and 50 min our method showed an average sensitivity of 79.9% and 90.2% with an average false prediction rate of 0.17 and 0.11/h, respectively. In terms of sensitivity and false prediction rate, the system showed superiority to random and periodical predictors.

CONCLUSIONS

The nonlinear analysis of iEEG in the period prior to seizures revealed patient-specific spatio-temporal changes that were significantly different from those observed within baselines in the majority of the seizures analyzed in this study.

SIGNIFICANCE

The present results suggest that the patient specific rule-based approach may become a potentially useful approach for predicting seizures prior to onset.

Seizure prediction: methods

Epilepsy, one of the most common neurological diseases, affects over 50 million people worldwide. Epilepsy can have a broad spectrum of debilitating medical and social consequences. Although antiepileptic drugs have helped treat millions of patients, roughly a third of all patients have seizures that are refractory to pharmacological intervention. The evolution of our understanding of this dynamic disease leads to new treatment possibilities. There is great interest in the development of devices that incorporate algorithms capable of detecting early onset of seizures or even predicting them hours before they occur. The lead time provided by these new technologies will allow for new types of interventional treatment. In the near future, seizures may be detected and aborted before physical manifestations begin. In this chapter we discuss the algorithms that make these devices possible and how they have been implemented to date. We also compare and contrast these measures, and review their individual strengths and weaknesses. Finally, we illustrate how these techniques can be combined in a closed-loop seizure prevention system. 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.

Challenges for emerging neurostimulation-based therapies for real-time seizure control

In step with the worthwhile aim of this special issue, two junior investigators impart their insights on the therapeutic challenges imposed by pharmacoresistant epilepsies and offer viable approaches to improvement of treatment outcomes. Sunderam's comprehensive perspective addresses issues of critical importance for the design of efficacious therapies. Talathi delves into the thorny roles of so-called "interictal" spikes in ictio- and epileptogenesis, roles that are central to understanding the dynamics of these phenomena and implicitly of how to prevent them or abort them. First, however, Osorio and co-workers illustrate the complex behavior of the epileptogenic network and point to the importance of real-time intraindividual adaptation and optimization of therapies for seizures originating from the same epileptogenic network.

A chronic generalized bi-directional brain-machine interface

A bi-directional neural interface (NI) system was designed and prototyped by incorporating a novel neural recording and processing subsystem into a commercial neural stimulator architecture. The NI system prototype leverages the system infrastructure from an existing neurostimulator to ensure reliable operation in a chronic implantation environment. In addition to providing predicate therapy capabilities, the device adds key elements to facilitate chronic research, such as four channels of electrocortigram/local field potential amplification and spectral analysis, a three-axis accelerometer, algorithm processing, event-based data logging, and wireless telemetry for data uploads and algorithm/configuration updates. The custom-integrated micropower sensor and interface circuits facilitate extended operation in a power-limited device. The prototype underwent significant verification testing to ensure reliability, and meets the requirements for a class CF instrument per IEC-60601 protocols. The ability of the device system to process and aid in classifying brain states was preclinically validated using an in vivo non-human primate model for brain control of a computer cursor (i.e. brain-machine interface or BMI). The primate BMI model was chosen for its ability to quantitatively measure signal decoding performance from brain activity that is similar in both amplitude and spectral content to other biomarkers used to detect disease states (e.g. Parkinson's disease). A key goal of this research prototype is to help broaden the clinical scope and acceptance of NI techniques, particularly real-time brain state detection. These techniques have the potential to be generalized beyond motor prosthesis, and are being explored for unmet needs in other neurological conditions such as movement disorders, stroke and epilepsy.

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.

Synchrony in normal and focal epileptic brain: the seizure onset zone is functionally disconnected

Synchronization of local and distributed neuronal assemblies is thought to underlie fundamental brain processes such as perception, learning, and cognition. In neurological disease, neuronal synchrony can be altered and in epilepsy may play an important role in the generation of seizures. Linear cross-correlation and mean phase coherence of local field potentials (LFPs) are commonly used measures of neuronal synchrony and have been studied extensively in epileptic brain. Multiple studies have reported that epileptic brain is characterized by increased neuronal synchrony except possibly prior to seizure onset when synchrony may decrease. Previous studies using intracranial electroencephalography (EEG), however, have been limited to patients with epilepsy. Here we investigate neuronal synchrony in epileptic and control brain using intracranial EEG recordings from patients with medically resistant partial epilepsy and control subjects with intractable facial pain. For both epilepsy and control patients, average LFP synchrony decreases with increasing interelectrode distance. Results in epilepsy patients show lower LFP synchrony between seizure-generating brain and other brain regions. This relative isolation of seizure-generating brain underlies the paradoxical finding that control patients without epilepsy have greater average LFP synchrony than patients with epilepsy. In conclusion, we show that in patients with focal epilepsy, the region of epileptic brain generating seizures is functionally isolated from surrounding brain regions. We further speculate that this functional isolation may contribute to spontaneous seizure generation and may represent a clinically useful electrophysiological signature for mapping epileptic brain.

Patient-specific early seizure detection from scalp electroencephalogram

The objective of this study is to develop a method for automatic detection of seizures before or immediately after clinical onset using features derived from scalp electroencephalogram. This detection method is patient specific. It uses recurrent neural networks and a variety of input features. For each patient, we trained and optimized the detection algorithm for two cases: (1) during the period immediately preceding seizure onset and (2) during the period immediately after seizure onset. Continuous scalp electroencephalogram recordings (duration 15-62 hours, median 25 hours) from 25 patients, including a total of 86 seizures, were used in this study. Preonset detection was successful in 14 of the 25 patients. For these 14 patients, all of the testing seizures were detected before seizure onset with a median preonset time of 51 seconds and false-positive (FP) rate was 0.06/hour. Postonset detection had 100% sensitivity, 0.023/hour FP rate, and median delay of 4 seconds after onset. The unique results of this study relate to preonset detection. Our results suggest that reliable preonset seizure detection may be achievable for a significant subset of patients with epilepsy without use of invasive electrodes.

Views of patients with epilepsy on seizure prediction devices

Patients views on the relevance, performance requirements, and implementation of seizure prediction devices have so far not been evaluated in a standardized form. We here report views of outpatients with uncontrolled epilepsy from the epilepsy centers at Freiburg, Germany, and Coimbra, Portugal, based on a questionnaire. Interest in the development of methods for seizure prediction both for warning and for closed-loop interventions is high. High sensitivity of prediction is regarded as more important than specificity. Short prediction time windows are preferred, but the indication of seizure-prone periods is also considered worthwhile. Only a few patients are, however, willing to wear EEG electrodes for signal acquisition on a long-term basis. These data support the view that seizure prediction is of high interest to patients with uncontrolled epilepsy. Improvements in the performance of presently available prediction algorithms and technical improvements in EEG recording will, however, be necessary to meet patients requirements.

Network dynamics during development of pharmacologically induced epileptic seizures in rats in vivo

In epilepsy, the cortical network fluctuates between the asymptomatic interictal state and the symptomatic ictal state of seizures. Despite their importance, the network dynamics responsible for the transition between the interictal and ictal states are largely unknown. Here we used multielectrode single-unit recordings from the hippocampus to investigate the network dynamics during the development of seizures evoked by various chemoconvulsants in vivo. In these experiments, we detected a typical network dynamics signature that preceded seizure initiation. The preictal state preceding pilocarpine-, kainate-, and picrotoxin-induced seizures was characterized by biphasic network dynamics composed of an early desynchronization phase in which the tendency of neurons to fire correlated action potentials decreased, followed by a late resynchronization phase in which the activity and synchronization of the network gradually increased. This biphasic network dynamics preceded the initiation both of the initial seizure and of recurrent spontaneous seizures that followed. During seizures, firing of individual neurons and interneuronal synchronization further increased. These findings advance our understanding of the network dynamics leading to seizure initiation and may in future help in the development of novel seizure prediction algorithms.

A decrease in EEG energy accompanies anti-epileptic drug taper during intracranial monitoring

OBJECTIVE

During intracranial EEG (icEEG) monitoring the likelihood of observing a seizure is increased by tapering anti-epileptic drugs (AEDs). Presumably AED taper results in an increase in cortical excitation which in turn promotes seizure emergence. We measured change in signal energy of icEEGs in response to AED taper to quantify changes in excitation which accompany the increased propensity for seizures.

METHODS

Twelve consecutive adult patients who completed intracranial monitoring were studied. Two icEEG epochs from before and after AED taper, each 1h in duration, during wake, matched by time-of-day and removed from seizures were selected for each patient. Teager energy, a frequency weighted measure of signal energy, was estimated for both the seizure onset region as well as all other brain areas monitored.

RESULTS

Considerable changes in Teager energy, evaluated at a 1-h time-resolution, occur during intracranial monitoring. The most dominant trend is a decrease to lower values than those when the patient is on AEDs. A decrease of 35% was observed for both all the brain areas monitored and the seizure onset region.

CONCLUSIONS

A decrease in signal energy occurs during intracranial EEG monitoring, possibly accompanying AED taper. If the decrease is due to AED taper this would suggest that AEDs prevent seizures in ways other than reduction of cortical excitation and seizure generation may be influenced by factors other than poorly regulated cortical excitation.

Epilepsy and nonlinear dynamics

This overview summarizes findings obtained from analyzing electroencephalographic (EEG) recordings from epilepsy patients with methods from the theory of nonlinear dynamical systems. The last two decades have shown that nonlinear time series analysis techniques allow an improved characterization of epileptic brain states and help to gain deeper insights into the spatial and temporal dynamics of the epileptic process. Nonlinear EEG analyses can help to improve the evaluation of patients prior to neurosurgery, and with an unequivocal identification of precursors of seizures, they can be of great value in the development of seizure warning and prevention techniques.

Prolonged preictal psychosis in refractory seizures: a report of three cases

Seizures and psychosis coexist in a large number of patients with epilepsy, and a significant amount of research on their relationship has been published. There are several reports and reviews on postictal and interictal psychosis in patients with epilepsy. We describe three patients with refractory temporal lobe epilepsy, each of whom presented with a history of episodic psychosis that preceded almost all habitual seizures and, thus, served as a useful warning symptom. All three patients had intractable left complex partial seizures; two had right mesial temporal sclerosis, and the third had a gliotic area in the right frontotemporal region on MRI. This is the first report of psychosis preceding seizures. The literature on seizure anticipation, as well as on the complex relationship between seizures and psychosis, is also reviewed.

Transition to seizures in the isolated immature mouse hippocampus: a switch from dominant phasic inhibition to dominant phasic excitation

The neural dynamics and mechanisms responsible for the transition from the interictal to the ictal state (seizures) are unresolved questions in epilepsy. It has been suggested that a shift from inhibitory to excitatory GABAergic drive can promote seizure generation. In this study, we utilized an experimental model of temporal lobe epilepsy which produces recurrent seizure-like events in the isolated immature mouse hippocampus (P8-16), perfused with low magnesium ACSF, to investigate the cellular dynamics of seizure transition. Whole-cell and perforated patch recordings from CA1 pyramidal cells and from fast- and non-fast-spiking interneurons in the CA1 stratum oriens hippocampal region showed a change in intracellular signal integration during the transition period, starting with dominant phasic inhibitory synaptic input, followed by dominant phasic excitation prior to a seizure. Efflux of bicarbonate ions through the GABA A receptor did not fully account for this excitation and GABAergic excitation via reversed IPSPs was also excluded as the prime mechanism generating the dominant excitation, since somatic and dendritic GABA A responses to externally applied muscimol remained hyperpolarizing throughout the transition period. In addition, abolishing EPSPs in a single neuron by intracellularly injected QX222, revealed that inhibitory synaptic drive was maintained throughout the entire transition period. We suggest that rather than a major shift from inhibitory to excitatory GABAergic drive prior to seizure onset, there is a change in the interaction between afferent synaptic inhibition, and afferent and intrinsic excitatory processes in pyramidal neurons and interneurons, with maintained inhibition and increasing, entrained 'overpowering' excitation during the transition to seizure.

Etiology and management of refractory epilepsies

The epilepsies are an important, common and diverse group of symptom complexes characterized by recurrent spontaneous seizures. Although many patients with epilepsy have their seizures controlled effectively by antiepileptic drugs (AEDs), about one-third of patients continue to have seizures, despite trying a range of AEDs. Such patients bear the heaviest burden of epilepsy, with increased morbidity and risk of premature mortality. Our current understanding of the refractory epilepsies--the most common of which are focal--is limited; even their definition is problematic. Standard treatments for refractory epilepsies include optimization of existing AED regimens, trials of further AEDs, and, for some patients, therapeutic resective neurosurgery. Recent basic research has explored possible underlying causes of refractory epilepsy, and two main hypotheses have emerged to account for the failure of AED treatment. According to one hypothesis, AEDs might fail because of alterations in the properties of their usual targets. Alternatively, they might fail because multidrug transporter mechanisms limit concentrations of the drugs at their targets. The refractory epilepsies can be viewed as offering remarkable insights into biological processes in the epilepsies, and their effective treatment remains an important aim; treatment would potentially bring much-needed relief to hundreds of thousands of patients across the world.

State-of-the-Art of Seizure Prediction

SUMMARY

Although there are numerous studies exploring basic neuronal mechanisms that are likely to be associated with seizures, to date no definite information is available as to how, when, or why a seizure occurs in humans. 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 identify preictal precursors from the EEG of epilepsy patients, therapeutic possibilities and quality of life could improve dramatically. The last three decades have witnessed a rapid increase in the development of new EEG analysis techniques that appear to be capable of defining seizure precursors. Since the 1970s, studies on seizure prediction have advanced from preliminary descriptions of preictal phenomena and proof of principle studies via controlled studies to studies on continuous multiday recordings. At present, it is unclear whether prospective algorithms can predict seizures. If prediction algorithms are to be used in invasive seizure intervention techniques in humans, they must be proven to perform considerably better than a random predictor. The authors present an overview of the field of seizure prediction, its history, accomplishments, recent controversies, and potential for future development.

Seizure prediction: The impact of long prediction horizons

Several procedures have been proposed to be capable of predicting the occurrence of epileptic seizures. Up to now, all proposed algorithms are far from being sufficient for a clinical application. This is, however, often not obvious when results of seizure prediction performance are reported. Here, we discuss impacts of long prediction horizons with respect to clinical needs and the strain on patients by analyzing long-term continuous intracranial electroencephalography data.

Modeling the contribution of lamina 5 neuronal and network dynamics to low frequency EEG phenomena

The Electroencephalogram (EEG) is an important clinical and research tool in neurophysiology. With the advent of recording techniques, new evidence is emerging on the neuronal populations and wiring in the neocortex. A main challenge is to relate the EEG generation mechanisms to the underlying circuitry of the neocortex. In this paper, we look at the principal intrinsic properties of neocortical cells in layer 5 and their network behavior in simplified simulation models to explain the emergence of several important EEG phenomena such as the alpha rhythms, slow-wave sleep oscillations, and a form of cortical seizure. The models also predict the ability of layer 5 cells to produce a resonance-like neuronal recruitment known as the augmenting response. While previous models point to deeper brain structures, such as the thalamus, as the origin of many EEG rhythms (spindles), the current model suggests that the cortical circuitry itself has intrinsic oscillatory dynamics which could account for a wide variety of EEG phenomena.

Fast oscillations trigger bursts of action potentials in neocortical neurons in vitro: A quasi-white-noise analysis study

PURPOSE

Recent evidence supports the importance of action potential bursts in physiological neural coding, as well as in pathological epileptogenesis. To better understand the temporal dynamics of neuronal input currents that trigger burst firing, we characterized spectral patterns of stimulation current that generate bursts of action potentials from regularly spiking neocortical neurons in vitro.

METHODS

Sharp microelectrodes were used for intracellular recording and stimulation of cortical neurons in rat brain slices. Quasi-white-noise (0-2 kHz) and "chirp" sine wave currents of decreasing wavelength were applied to represent a broad spectrum of stimulation frequencies. Action potential-related averaging of the stimulation current variations preceding bursting was used to characterize stimulation current patterns more likely to result in a burst rather than a single-spike response.

RESULTS

Bursts of action potentials were most reliably generated by a preceding series of > or = 2 positive current transients at 164+/-37 Hz of the quasi-white-noise, and to sine wave currents with frequencies greater than 90 Hz. The intraburst action potential rate was linearly related to the frequency of the input sine wave current.

CONCLUSIONS

This study demonstrates that regularly spiking cortical neurons in vitro burst in response to fast oscillations of input currents. In the presence of positive cortical feedback loops, encoding input frequency in the intraburst action potential rate may be safer than producing a high-frequency regular output spike train. This leads to the experimentally testable and therapeutically important hypothesis that burst firing could be an antiepileptogenic and/or anti-ictogenic mechanism.