Quantitative EEG analysis for automated detection of nonconvulsive seizures in intensive care units.

Because of increased awareness of the high prevalence of nonconvulsive seizures in critically ill patients, use of continuous EEG (cEEG) monitoring is rapidly increasing in ICUs. However, cEEG monitoring is labor intensive, and manual review and interpretation of the EEG are impractical in most ICUs. Effective methods to assist in rapid and accurate detection of nonconvulsive seizures would greatly reduce the cost of cEEG monitoring 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 with a mean sensitivity of 90.4% and a mean false detection rate of 0.066/hour. The two commercial detection products performed with low sensitivities (12.9 and 10.1%) and false detection rates of 1.036/hour and 0.013/hour, 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 nonconvulsive seizures. This study also suggests that currently available seizure detection software does not perform sufficiently in detection of nonconvulsive seizures in critically ill patients. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.

Effects of age and cortical infarction on EEG dynamic changes associated with spike wave discharges in F344 rats.

Rodent models of absence seizures are used to investigate the network properties and regulatory mechanisms of the seizure's generalized spike and wave discharge (SWD). As rats age, SWDs occur more frequently, suggesting aging-related changes in the regulation of the corticothalamic mechanisms generating the SWD. We hypothesized that brain resetting mechanisms - how the brain "resets" itself to a more normal functional state following a transient period of abnormal function, e.g., a SWD - are impaired in aged animals and that brain infarction would further affect these resetting mechanisms. The main objective of this study was to determine the effects of aging, infarction, and their potential interaction on the resetting of EEG dynamics assessed by quantitative EEG (qEEG) measures of linear (signal energy measured by amplitude variation; signal frequency measured by mean zero-crossings) and nonlinear (signal complexity measured by the pattern match regularity statistic and the short-term maximum Lyapunov exponent) brain EEG dynamics in 4- and 20-month-old F344 rats with and without brain infarction. The main findings of the study were: 1) dynamic resetting of both linear and nonlinear EEG characteristics occurred following SWDs; 2) animal age significantly affected the degree of dynamic resetting in all four qEEG measures: SWDs in older rats exhibited a lower degree of dynamic resetting; 3) infarction significantly affected the degree of dynamic resetting only in terms of EEG signal complexity: SWDs in infarcted rats exhibited a lower degree of dynamic resetting; and 4) in all four qEEG measures, there was no significant interaction effect between age and infarction on dynamic resetting. We conclude that recovery of the brain to its interictal state following SWDs was better in young adult animals compared with aged animals, and to a lesser degree, in age-matched controls compared with infarction-injured animal groups, suggesting possible effects of brain resetting mechanisms and/or the disruption of the epileptogenic network that triggers SWDs.

Visualization and modelling of STLmax topographic brain activity maps.

This paper evaluates the descriptive power of brain topography based on a dynamical parameter, the Short-Term Maximum Lyapunov Exponent (STLmax), estimated from EEG, for finding out a relationship of STLmax spatial distribution with the onset zone and with the mechanisms leading to epileptic seizures. Our preliminary work showed that visual assessment of STLmax topography exhibited a link with the location of seizure onset zone. The objective of the present work is to model the spatial distribution of STLmax in order to automatically extract these features from the maps. One-hour preictal segments from four long-term continuous EEG recordings (two scalp and two intracranial) were processed and the corresponding STLmax profiles were estimated. The spatial STLmax maps were modelled by a combination of two Gaussians functions. The parameters of the fitted model allow automatic extraction of quantitative information about the spatial distribution of STLmax: the EEG signal recorded from the brain region where seizures originate exhibited low-STLmax levels, long before the seizure onset, in 3 out of 4 patients (1 out of 2 of scalp patients and 2 out of 2 in intracranial patients). Topographic maps extracted directly from the EEG power did not provide useful information about the location, therefore we conclude that the analysis so far carried out suggests the possibility of using a model of STLmax topography as a tool for monitoring the evolution of epileptic brain dynamics. In the future, a more elaborate approach will be investigated in order to improve the specificity of the method.

Seizure Detection Software Used to Complement the Visual Screening Process for Long-Term EEG Monitoring.

It is widely recognized that visual screening of long-term EEG recordings can be time-consuming and labor-intensive due to the large volume of patient data produced daily in most Epilepsy Monitoring Units (EMUs). As a result, seizures, especially those with only electrographic changes, are sometimes overlooked, which for some patients could result in missed information for diagnosis, an unnecessarily prolonged hospital stay, and unavailable EMU beds for others. In this report, we propose that a better solution for identifying seizures in long-term EEG recording is to combine detection results from a reliable (high sensitivity and low false detection rate) automated detection system with EEG technologists' visual screening process. Using commercially available detection software, we present case studies that demonstrate potential benefits of this method that could help improve detection rates and bring greater efficiency to the seizure identification process in long-term EEG monitoring.

An investigation of EEG dynamics in an animal model of temporal lobe epilepsy using the maximum Lyapunov exponent.

Analysis of intracranial electroencephalographic (iEEG) recordings in patients with temporal lobe epilepsy (TLE) has revealed characteristic dynamical features that distinguish the interictal, ictal, and postictal states and inter-state transitions. Experimental investigations into the mechanisms underlying these observations require the use of an animal model. A rat TLE model was used to test for differences in iEEG dynamics between well-defined states and to test specific hypotheses: 1) the short-term maximum Lyapunov exponent (STL(max)), a measure of signal order, is lowest and closest in value among cortical sites during the ictal state, and highest and most divergent during the postictal state; 2) STL(max) values estimated from the stimulated hippocampus are the lowest among all cortical sites; and 3) the transition from the interictal to ictal state is associated with a convergence in STL(max) values among cortical sites. iEEGs were recorded from bilateral frontal cortices and hippocampi. STL(max) and T-index (a measure of convergence/divergence of STL(max) between recorded brain areas) were compared among the four different periods. Statistical tests (ANOVA and multiple comparisons) revealed that ictal STL(max) was lower (p<0.05) than other periods, STL(max) values corresponding to the stimulated hippocampus were lower than those estimated from other cortical regions, and T-index values were highest during the postictal period and lowest during the ictal period. Also, the T-index values corresponding to the preictal period were lower than those during the interictal period (p<0.05). These results indicate that a rat TLE model demonstrates several important dynamical signal characteristics similar to those found in human TLE and support future use of the model to study epileptic state transitions.

Quantitative complexity analysis in multi-channel intracranial EEG recordings form epilepsy brains.

Epilepsy is a brain disorder characterized clinically by temporary but recurrent disturbances of brain function that may or may not be associated with destruction or loss of consciousness and abnormal behavior. Human brain is composed of more than 10 to the power 10 neurons, each of which receives electrical impulses known as action potentials from others neurons via synapses and sends electrical impulses via a sing output line to a similar (the axon) number of neurons. When neuronal networks are active, they produced a change in voltage potential, which can be captured by an electroencephalogram (EEG). The EEG recordings represent the time series that match up to neurological activity as a function of time. By analyzing the EEG recordings, we sought to evaluate the degree of underlining dynamical complexity prior to progression of seizure onset. Through the utilization of the dynamical measurements, it is possible to classify the state of the brain according to the underlying dynamical properties of EEG recordings. The results from two patients with temporal lobe epilepsy (TLE), the degree of complexity start converging to lower value prior to the epileptic seizures was observed from epileptic regions as well as non-epileptic regions. The dynamical measurements appear to reflect the changes of EEG's dynamical structure. We suggest that the nonlinear dynamical analysis can provide a useful information for detecting relative changes in brain dynamics, which cannot be detected by conventional linear analysis.

Seizure prediction.

There is mounting evidence that seizures are preceded by characteristic changes in the EEG that are detectable minutes before seizure onset. Using novel signal analysis techniques, researchers are beginning to characterize the transition from the interictal to the ictal state in quantitative terms. This research has led to the development of automated seizure prediction algorithms. Active debate persists regarding the interpretation of research results, methods of signal analysis, as well as experimental and statistical methods for testing seizure prediction algorithms. Developments in this field have led to new theories on the mechanism of seizure development and resolution. The ability to predict seizures could lead the way to novel diagnostic and therapeutic methods for the treatment of patients with epilepsy.

Predictability analysis for an automated seizure prediction algorithm.

Epileptic seizures of mesial temporal origin are preceded by changes in signal properties detectable in the intracranial EEG. A series of computer algorithms designed to detect the changes in spatiotemporal dynamics of the EEG signals and to warn of impending seizures have been developed. In this study, we evaluated the performance of a novel adaptive threshold seizure warning algorithm (ATSWA), which detects the convergence in Short-Term Maximum Lyapunov Exponent (STLmax) values among critical intracranial EEG electrode sites, as a function of different seizure warning horizons (SWHs). The ATSWA algorithm was compared to two statistical based naïve prediction algorithms (periodic and random) that do not employ EEG information. For comparison purposes, three performance indices "area above ROC curve" (AAC), "predictability power" (PP) and "fraction of time under false warnings" (FTF) were defined and the effect of SWHs on these indices was evaluated. The results demonstrate that this EEG based seizure warning method performed significantly better (P < 0.05) than both naïve prediction schemes. Our results also show that the performance indexes are dependent on the length of the SWH. These results suggest that the EEG based analysis has the potential to be a useful tool for seizure warning.

Affect in patients with epilepsy undergoing video/EEG monitoring: retrospective versus momentary assessment and temporal relationship to seizures.

This study was designed to (1) compare retrospective and momentary assessments of mood/affect, and (2) examine the temporal relationship between affect and seizure occurrence. Patients with epilepsy undergoing long-term video/EEG monitoring (LTM) completed an affect rating of how they felt "at that moment" each time a programmed watch beeped (momentary assessment); these ratings were averaged across each patient's hospital stay. Prior to discharge, patients were asked to think back and rate how they felt "during their hospital stay" using the same rating scale (retrospective assessment). Results indicated that patients retrospectively recalled feeling significantly more positive during their LTM than they reported feeling when they were actually undergoing LTM. Among patients who had EEG-verified seizures, momentary assessments were used to compare affect during the interictal periods with affect during the prodromal and postictal periods. The latter two periods were characterized by significantly less activated positive affect than were the interictal periods.

Effects of acute hippocampal stimulation on EEG dynamics.

Progressive preictal dynamical convergence and postictal divergence of dynamical EEG descriptors among brain regions has been reported in human temporal lobe epilepsy (TLE) and in a rodent model of TLE. There are also reports of anticonvulsant effects of high frequency stimulation of the hippocampus in humans. We postulate that this anticonvulsant effect is due to dynamical resetting by the electrical stimulation. The following study investigated the effects of acute hippocampal electrical stimulation on dynamical transitions in the brain of a spontaneously seizing animal model of TLE to test the hypothesis of divergence in dynamical values by electrical stimulation of the hippocampus.

Randomized, controlled clinical trial of zonisamide as adjunctive treatment for refractory partial seizures.

PURPOSE: This study was designed to evaluate efficacy and safety of zonisamide (ZNS) as adjunctive treatment for patients with refractory partial seizures. METHODS: This randomized, double-blind, placebo-controlled study was conducted at four epilepsy treatment centers. It included a baseline phase (8 to 12 weeks) and a double-blind treatment phase (12 weeks). Initially, patients randomized to ZNS treatment were given a 7-mg/kg/d dosage. When investigators found that adverse effects could be reduced by gradually introducing ZNS, patients were allowed to begin treatment at lower doses (100 mg or approximately 1.5 mg/kg/d) titrated over several weeks to a maximum of 400 to 600 mg/d. Primary and secondary efficacy measures were the median percentage reduction from baseline in seizure frequency and the proportion of patients achieving a > or =50% reduction from baseline (responder rate). Patient and physician global assessments also served as indicators of efficacy. Safety was assessed primarily by treatment-emergent adverse events. RESULTS: ZNS-treated patients had a 28.9% reduction in seizure frequency, which differed significantly from the 4.7% increase in placebo-treated patients. The responder rate for ZNS-treated patients was 26.9%, compared with 16.2% for placebo-treated patients. At study's end, 66.2% of ZNS-treated patients and 12.3% of placebo-treated patients considered their condition improved; similarly, physicians assessed 63.6% of ZNS-treated patients and 10.8% of placebo-treated patients as improved. The most frequently reported adverse events with ZNS treatment included somnolence, irritability, dizziness, nausea, and fatigue. CONCLUSIONS: As adjunctive treatment, ZNS was generally well tolerated and significantly improved seizure control among patients with refractory partial seizures.

Dynamical resetting of the human brain at epileptic seizures: application of nonlinear dynamics and global optimization techniques.

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.

Adaptive epileptic seizure prediction system.

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.

Occurrence of psychosis in patients with epilepsy randomized to tiagabine or placebo treatment.

PURPOSE: Patients with drug-resistant epilepsy have a higher incidence of psychiatric problems and possibly greater intolerance to antiepileptic drugs (AEDs) than do other patients with epilepsy. Concern has been raised that gamma-aminobutyric acid (GABA)ergic drugs may be associated with treatment-emergent psychosis. Tiagabine (TGB; Gabitril), a new AED that blocks synaptic GABA uptake, was developed in trials of drug-resistant patients with epilepsy. We conducted ad hoc analyses of adverse events, drug intolerance, and treatment response to evaluate the association between TGB treatment and psychosis and whether psychiatric history might be predictive of tolerance or effectiveness of this GABAergic drug. METHODS: Data were analyzed from two multicenter, randomized, double-blind, placebo-controlled trials of add-on TGB therapy (32 or 56 mg daily) in 554 adolescents and adults with complex partial seizures (CPSs). After an 8- or 12-week baseline phase, double-blind treatment consisted of a 4-week titration period (with TGB dose gradually increased to 32 or 56 mg daily) and an 8- or 12-week fixed-dose period. Adverse events commonly associated with psychosis were evaluated. Treatment intolerance and effectiveness (> or =50% reduction in CPS rate) were compared among patients with and without psychiatric histories. RESULTS: Psychotic symptoms (hallucinations) were observed in three (0.8%) of 356 TGB-treated patients and none of 198 placebo-treated patients (p = 0.556, NS). Statistical analysis showed no interaction between psychiatric history and drug intolerance or treatment outcome. CONCLUSIONS: TGB administration appears to carry no significant increased risk of treatment-emergent psychosis. Psychiatric history was not predictive of the tolerance or effectiveness of the drug.

Improvement in depression associated with partial epilepsy in patients treated with lamotrigine.

Interictal depression is common in patients with epilepsy and it significantly impacts quality of life. Previous studies indicate that lamotrigine may have antidepressant properties. Thirteen adults with uncontrolled partial seizures and concomitant depression were evaluated using measures of depression [Montgomery and Asberg Depression Rating Scale (MADRS) and the MMPI Depression Scale] and anxiety [Spielberger's State-Trait Anxiety Inventory (STAI)] to test the effects of lamotrigine on mood. Evaluations after 5 weeks and again after 3 months of lamotrigine treatment demonstrated significant improvement in depression and anxiety. Mean MADRS overall scores were significantly lower than pretreatment baseline at the 5-week and 3-month evaluations. The mean MMPI Depression score was significantly lower than baseline at the 3-month evaluation. State anxiety scores were significantly reduced from baseline after 5 weeks, but not at 3 months, whereas Trait anxiety scores were reduced from baseline at the 5-week and 3-month evaluations.

Lamotrigine Monotherapy Improves Depressive Symptoms in Epilepsy: A Double-Blind Comparison with Valproate.

Depressive symptoms are highly prevalent in patients with epilepsy. The antiepileptic drug lamotrigine has been shown to be an effective treatment for the depressive phase of bipolar disorder and to enhance mood and well-being in epilepsy patients. The effects of lamotrigine monotherapy on depressive symptoms in epilepsy have not been evaluated to date in a controlled clinical trial. A recently completed double-blind epilepsy trial comparing the effects of lamotrigine monotherapy and valproate monotherapy on weight change incorporated a battery of standard mood assessments. Mean screening Beck Depression Inventory scores showed that both lamotrigine and valproate groups suffered from mild depression at baseline. Lamotrigine monotherapy was reliably associated with earlier and larger improvements than valproate in mood assessed with the Beck Depression Inventory, the Cornell Dysthymia Rating Scale, and the Profile of Mood States. Considered in the context of other data showing lamotrigine's antidepressant efficacy in bipolar depression, these results suggest that lamotrigine improves mood in mildly depressed patients with epilepsy. Lamotrigine may be particularly useful in treating epilepsy patients with comorbid depression, the most common psychiatric illness in epilepsy.