Current epileptic seizure "prediction" algorithms are generally based on the knowledge of seizure occurring time and analyze the electroencephalogram (EEG) recordings retrospectively. It is then obvious that, although these analyses provide evidence of brain activity changes prior to epileptic seizures, they cannot be applied to develop implantable devices for diagnostic and therapeutic purposes. In this paper, we describe an adaptive procedure to prospectively analyze continuous, long-term EEG recordings when only the occurring time of the first seizure is known. The algorithm is based on the convergence and divergence of short-term maximum Lyapunov exponents (STLmax) among critical electrode sites selected adaptively. A warning of an impending seizure is then issued. Global optimization techniques are applied for selecting the critical groups of electrode sites. The adaptive seizure prediction algorithm (ASPA) was tested in continuous 0.76 to 5.84 days intracranial EEG recordings from a group of five patients with refractory temporal lobe epilepsy. A fixed parameter setting applied to all cases predicted 82% of seizures with a false prediction rate of 0.16/h. Seizure warnings occurred an average of 71.7 min before ictal onset. Similar results were produced by dividing the available EEG recordings into half training and testing portions. Optimizing the parameters for individual patients improved sensitivity (84% overall) and reduced false prediction rate (0.12/h overall). These results indicate that ASPA can be applied to implantable devices for diagnostic and therapeutic purposes.

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Epileptic seizures occur intermittently as a result of complex dynamical interactions among many regions of the brain. By applying signal processing techniques from the theory of nonlinear dynamics and global optimization to the analysis of long-term (3.6 to 12 days) continuous multichannel electroencephalographic recordings from four epileptic patients, we present evidence that epileptic seizures appear to serve as dynamical resetting mechanisms of the brain, that is the dynamically entrained brain areas before seizures disentrain faster and more frequently (p < 0.05) at epileptic seizures than any other periods. We expect these results to shed light into the mechanisms of epileptogenesis, seizure intervention and control, as well as into investigations of intermittent spatiotemporal state transitions in other complex biological and physical systems.

Objective-The purpose of this study was to evaluate and validate an offline, automated scalp EEGbased seizure detection system and to compare its performance to commercially available seizure detection software.Methods-The test seizure detection system, IdentEvent™, was developed to enhance the efficiency of post-hoc long-term EEG review in epilepsy monitoring units. It translates multi-channel scalp EEG signals into multiple EEG descriptors and recognizes ictal EEG patterns. Detection criteria and thresholds were optimized in 47 long-term scalp EEG recordings selected for training (47 subjects, ~3653 hours with 141 seizures). The detection performance of IdentEvent was evaluated using a separate test dataset consisting of 436 EEG segments obtained from 55 subjects (~1200 hours with 146 seizures). Each of the test EEG segments was reviewed by three independent epileptologists and the presence or absence of seizures in each epoch was determined by majority rule. Seizure detection sensitivity and false detection rate were calculated for IdentEvent as well as for the comparable detection software (Persyst's Reveal ® , version 2008.03.13, with three parameter settings). Bootstrap re-sampling was applied to establish the 95% confidence intervals of the estimates and for the performance comparison between two detection algorithms.Results-The overall detection sensitivity of IdentEvent was 79.5% with a false detection rate (FDR) of 2 per 24 hours, whereas the comparison system had 80.8%, 76%, and 74% sensitivity using its three detection thresholds (perception score) with FDRs of 13, 8, and 6 per 24 hours, respectively. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Bootstrap 95% confidence intervals of the performance difference revealed that the two detection systems had comparable detection sensitivity, but IdentEvent generated a significantly (p < 0.05) smaller FDR. NIH Public AccessConclusions-The study validates the performance of the IdentEvent™ .seizure detection system.Significance-With comparable detection sensitivity, an improved false detection rate makes the automated seizure detection software more useful in clinical practice.

Objective This study investigated inter-rater agreement (IRA) among EEG experts for the identification of electrographic seizures and periodic discharges (PDs) in continuous ICU EEG recordings. Methods Eight board-certified EEG experts independently identified seizures and PDs in thirty 1-hour EEG segments which were selected from ICU EEG recordings collected from three medical centers. IRA was compared between seizure and PD identifications, as well as among rater groups that have passed an ICU EEG Certification Test, developed by the Critical Care EEG Monitoring Research Consortium (CCEMRC). Results Both kappa and event-based IRA statistics showed higher mean values in identification of seizures compared to PDs (k = 0.58 vs. 0.38; p < 0.001). The group of rater pairs who had both passed the ICU EEG Certification Test had a significantly higher mean IRA in comparison to rater pairs in which neither had passed the test. Conclusions IRA among experts is significantly higher for identification of electrographic seizures compared to PDs. Additional instruction, such as the training module and certification test developed by the CCEMRC, could enhance this IRA. Significance This study demonstrates more disagreement in the labeling of PDs in comparison to seizures. This may be improved by education about standard EEG nomenclature.

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Temporal lobe epilepsy is characterized by episodic paroxysmal electrical discharges (ictal activity) originating in mesial structures of the temporal lobe. These discharges consist of organized synchronous activity of mesial temporal neurons, particularly those of the hippocampus. This activity is seen as rhythmic medium to high amplitude slow waves or spike and slow wave discharges on the electroencephalogram (EEG). The ictal discharges (seizures) often spread to involve widespread regions of the ipsilateral then the contralateral cerebral hemispheres. These diffuse discharges often persist for approximately 1 to 5 minutes and are followed by a postictal pattern of asynchronous low amplitude slow waves in the EEG. It is a widely held view that seizures arise from mesial temporal structures because of damage to hippocampal circuitry. The characteristic circuit abnormalities include drop out of neurons, simplification of the dendritic tree (reduced synaptic input), sprouting of dentate granule cell axons (increasing the number of excitatory-excitatory feedback connections), and increase in glial cell elements (sclerosis). There is a concomitant loss in neurotransmitter receptors in the hippocampus. Physiologic studies in epileptogenic hippocampi have demonstrated loss of neuronal inhibition. It is generally believed that loss of inhibition is, at least in part, responsible for the occurrence of epileptic seizures. The central questions as to why seizures occur intermittently, and begin and end when they do, remain unanswered. The structural abnormalities of the temporal lobe are relatively stable, yet they exhibit dramatically variable behavior, as characterized by the EEG. For example, during the interictal state, the EEG pattern is described by electroencephalographers as low to medium voltage, "irregular" and "arrhythmic". This contrasts with the "organized", "rhythmic", and self-sustained characteristics of ictal EEG pattern. We have postulated that epileptic brains, being chaotic nonlinear systems, repeatedly make the abrupt transitions into and out of the ictal state because the epileptogenic focus drives them into selforganizing phase transitions from chaos to order. Further, we postulated that the seizure serves to reset the system. Our hypotheses have been supported by the following findings: (1) positive In: Chaos in the brain? Eds. K. Lehnertz & C.E. Elger, World Scientific, Singapore, in press EpilepsyChaos2.doc submitted to World Scientific 01/03/00 : 2:16 PM 2 Lyapunov exponent in EEG signal (2) nonlinearities in interictal EEG generated by the epileptogenic focus, (3) existence of a spatiotemporal transition in EEG dynamics (from chaos to order) preceding seizures by minutes to hours to days and (4) resetting of spatiotemporal dynamics by the seizure (from order to chaos), leading to the more favorable interictal condition. These observations were made in scalp and intracranial EEG recordings from patients with epileptic seizures of frontal as well as mesial temporal origin. Thus, although an epile...

Due to increased awareness of the high prevalence of nonconvulsive seizures (NCSs) in critically ill patients, continuous EEG monitoring (cEEG) in ICUs is rapidly increasing in use. However, cEEG monitoring is labor intensive; manual review and interpretation of the EEG are impractical in most ICUs. Effective methods to assist in rapid and accurate detection of NCSs would greatly reduce the cost of cEEG and enhance the quality of patient care. In this study, we report a preliminary investigation of a novel ICU EEG analysis and seizure detection algorithm. Twenty-four prolonged cEEG recordings were included in this study. Seizure detection sensitivity and specificity were assessed for the new algorithm and for the two commercial seizure detection software systems. The new algorithm performed a mean sensitivity of 90.4% and a mean false detection rate of 0.066/h. The two commercial detection products performed with low sensitivities (12.9% and 10.1%) and false detection rates of 1.036/h and 0.013/h, respectively. These findings suggest that the novel algorithm has potential to be the basis of clinically useful software that can assist ICU staff in timely identification of NCSs. This study also suggests that currently available seizure detection software does not have sufficient performance for the detection of NCSs in critically ill patients.