Topographic analysis of dimension estimates of EEG and filtered rhythms in epileptic patients with complex partial seizures

Nonlinear dynamic properties were analyzed on the EEG and filtered rhythms recorded from healthy subjects and epileptic patients with complex partial seizures. Estimates of correlation dimensions of control EEG, interictal EEG and ictal EEG were calculated. The values were demonstrated on topograms. The delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz) and gamma (30–40 Hz) components were obtained and considered as signals from the cortex. Corresponding surrogate data was produced. Firstly, the influence of sampling parameters on the calculation was tested. The dimension estimates of the signals from the frontal, temporal, parietal and occipital regions were computed and compared with the results of surrogate data. In the control subjects, the estimates between the EEG and surrogate data did not differ (P > 0.05). The interictal EEG from the frontal region and occipital region, as well as its theta component from the frontal region, and temporal region, showed obviously low dimensions (P < 0.01). The ictal EEG exhibited significantly low-dimension estimates across the scalp. All filtered rhythms from the temporal region yielded lower results than those of the surrogate data (P < 0.01). The dimension estimates of the EEG and filtered components markedly changed when the neurological state varied. For each neurological state, the dimension estimates were not uniform among the EEG and frequency components. The signal with a different frequency range and in a different neurological state showed a different dimension estimate. Furthermore, the theta and alpha components demonstrated the same estimates not only within each neurological state, but also among the different states. These results indicate that the theta and alpha components may be caused by similar dynamic processes. We conclude that the brain function underlying the ictal EEG has a simple mechanism. Several heterogeneous dynamic systems play important roles in the generation of EEG. Received: 10 December 1999 / Accepted in revised form: 8 May 2000     

Behavioral management of epileptic seizures following EEG biofeedback training of the sensorimotor rhythm

Eight severely epileptic patients, four males and four females, ranging in age from 10 to 29 years, were trained to increase 12–14 Hz EEG activity from the regions overlying the Rolandic area. This activity, the sensorimotor rhythm(SMR), has been hypothesized to be related to motor inhibitory processes(Sterman, 1974). The patients represented a crosssection of several different types of epilepsy, including grand mal, myoclonic, akinetic, focal, and psychomotor types. Three of them had varying degrees of mental retardation. SMR was detected by a combination of an analog filtering system and digital processing. Feedback, both auditory and/or visual, was provided whenever one-half second of 12–14-Hz activity was detected in the EEG. Patients were provided with additional feedback keyed by the output of a 4–7-Hz filter which indicated the presence of epileptiform spike activity, slow waves, or movement. Feedback for SMR was inhibited whenever slow-wave activity spikes or movement was also present. During the treatment period most of the patients showed varying degrees of improvement. Two of the patients who had been severely epileptic, having multiple seizures per week, have been seizure free for periods of up to 1 month. Other patients have developed the ability to block many of their seizures. Seizure intensity and duration have also decreased. Furthermore, the successful patients demonstrated an increase in the amount of SMR and an increase in amplitude of SMR during the training period. Spectral analyses for the EEGs were performed periodically. The effectiveness of SMR conditioning for the control of epileptic seizures is evaluated in terms of patient characteristics and type of seizures.     

Application of a dissimilarity index of EEG and its sub-bands on prediction of induced epileptic seizures from rat's EEG signals

ObjectiveEpileptic seizures are defined as manifest of excessive and hyper-synchronous activity of neurons in the cerebral cortex that cause frequent malfunction of the human central nervous system. Therefore, finding precursors and predictors of epileptic seizure is of utmost clinical relevance to reduce the epileptic seizure induced nervous system malfunction consequences. Researchers for this purpose may even guide us to a deep understanding of the seizure generating mechanisms. The goal of this paper is to predict epileptic seizures in epileptic rats.MethodsSeizures were induced in rats using pentylenetetrazole (PTZ) model. EEG signals in interictal, preictal, ictal and postictal periods were then recorded and analyzed to predict epileptic seizures. Epileptic seizures were predicted by calculating an index in consecutive windows of EEG signal and comparing the index with a threshold. In this work, a newly proposed dissimilarity index called Bhattacharyya Based Dissimilarity Index (BBDI), dynamical similarity index and fuzzy similarity index were investigated.ResultsBBDI, dynamical similarity index and fuzzy similarity index were examined on case and control groups and compared to each other. The results show that BBDI outperforms dynamical and fuzzy similarity indices. In order to improve the results, EEG sub-bands were also analyzed. The best result achieved when the proposed dissimilarity index was applied on Delta sub-band that predicts epileptic seizures in all rats with a mean of 299.5 s.ConclusionThe dissimilarity of neural network activity between reference window and present window of EEG signal has a significant increase prior to an epileptic seizure and the proposed dissimilarity index (BBDI) can reveal this variation to predict epileptic seizures. In addition, analyzing EEG sub-bands results in more accurate information about constituent neuronal activities underlying the EEG since certain changes in EEG signal may be amplified when each sub-band is analyzed separately.SignificanceThis paper presents application of a dissimilarity index (BBDI) on EEG signals and its sub-bands to predict PTZ-induced epileptic seizures in rats. Based on the results of this work, BBDI will predict epileptic seizures more accurately and more reliably compared to current indices that increases epileptic patient comfort and improves patient outcomes.     

The effect of PTZ-induced epileptic seizures on hippocampal expression of PSA-NCAM in offspring born to kindled rats

Background

Maternal epileptic seizures during pregnancy can affect the hippocampal neurons in the offspring. The polysialylated neural cell adhesion molecule (PSA-NCAM), which is expressed in the developing central nervous system, may play important roles in neuronal migration, synaptogenesis, and axonal outgrowth. This study was designed to assess the effects of kindling either with or without maternal seizures on hippocampal PSA-NCAM expression in rat offspring.

Methods

Forty timed-pregnant Wistar rats were divided into four groups: A) Kind⁺/Seiz⁺, pregnant kindled (induced two weeks prior to pregnancy) rats that received repeated intraperitoneal (i.p.) pentylenetetrazol, PTZ injections on gestational days (GD) 14-19; B) Kind^-/Seiz⁺, pregnant non-kindled rats that received PTZ injections on GD14-GD19; C) Kind⁺/Seiz^-, pregnant kindled rats that did not receive any PTZ injections; and D) Kind^-/Seiz^-, the sham controls. Following birth, the pups were sacrificed on PD1 and PD14, and PSA-NCAM expression and localization in neonates’ hippocampi were analyzed by Western blots and immunohistochemistry.

Results

Our data show a significant down regulation of hippocampal PSA-NCAM expression in the offspring of Kind⁺/Seiz⁺ (p = 0.001) and Kind^-/Seiz⁺ (p = 0.001) groups compared to the sham control group. The PSA-NCAM immunoreactivity was markedly decreased in all parts of the hippocampus, especially in the CA3 region, in Kind⁺/Seiz⁺ (p = 0.007) and Kind^-/Seiz⁺ (p = 0.007) group’s newborns on both PD1 and 14.

Conclusion

Our findings demonstrate that maternal seizures but not kindling influence the expression of PSA-NCAM in the offspring’s hippocampi, which may be considered as a factor for learning/memory and cognitive impairments reported in children born to epileptic mothers.     

On the prediction of epileptic seizures

In 12 epileptic patients suffering from absences 8-channel EEG was recorded by telemetry. The autoregressive model was applied to the signal and the prediction coefficients being the basis for calculation of the poles of the predictor. The location of the poles in thez- ands-planes was described as a function of time for 0.1 s steps along the pre-seizure EEG. In 10 of the 12 patients, and in 25 of the 28 recorded seizures this presentation of the poles of the predictor showed specific pattern linked with the occurrence of the siezure. The trajectory of the most mobile pole during the pre-seizure period could aid in the prediction of the seizure by several seconds.Dr. Isak Gath c/o Prof. Lehmann Neurologische Universitätsklinik CH-8091 Zürich Switzerland     

Models of epileptic seizures in immature rats

Models of basic types of epileptic seizures are elaborated not only in adult but also in immature rodents. It is important because at least half of human epilepsies starts during infancy and childhood. This paper presents a review of chemically and electrically induced models of generalized convulsive and nonconvulsive (absence) seizures as well as models of partial simple (neocortical) and complex (limbic) seizures in immature rats. These models can also serve as a tool for study the development of central nervous system and motor abilities because the level of maturation is reflected in seizure semiology. Age-dependent models of epileptic seizures (absences and flexion seizures) are discussed. Models of seizures in immature animals should be used for testing of potential antiepileptic drugs.     

Chromosomal abnormalities among offspring of childhood-cancer survivors in Denmark: a population-based study

Ionizing radiation and many cancer drugs have the potential to produce germ-cell mutations that might lead to genetic disease in the next generation. In a population-based study, we identified, from records in the Danish Cancer Registry, 4,676 children treated for cancer. Their 6,441 siblings provided a comparison cohort. The results of a search of the Central Population Register identified 2,630 live-born offspring of the survivors and 5,504 live-born offspring of their siblings. The occurrence of abnormal karyotypes diagnosed in these offspring and also in any pregnancies terminated following prenatal diagnosis of a chromosome abnormality was determined from the Danish Cytogenetic Registry. After exclusion of hereditary cases and inclusion of the prenatal cases, after correction for expected viability, the adjusted proportion of live-born children in survivor families with abnormal karyotypes (5.5/2,631.5 [0.21%]) was the same as that among the comparison sibling families (11.8/5,505.8 [0.21%]). There were no significant differences in the occurrence of Down syndrome (relative risk [RR]=1.07; 95% CI 0.16-5.47) or Turner syndrome (RR=1.32; 95% CI 0.17-7.96) among the children of cancer survivors, compared with the children of their siblings. These reassuring results are of importance to the survivors, to their families, and to genetic counselors.     

Role of oxidative stress in epileptic seizures

Oxidative stress resulting from excessive free-radical release is likely implicated in the initiation and progression of epilepsy. Therefore, antioxidant therapies aimed at reducing oxidative stress have received considerable attention in epilepsy treatment. However, much evidence suggests that oxidative stress does not always have the same pattern in all seizures models. Thus, this review provides an overview aimed at achieving a better understanding of this issue. We summarize work regarding seizure models (i.e., genetic rat models, kainic acid, pilocarpine, pentylenetetrazol, and trimethyltin), oxidative stress as an etiologic factor in epileptic seizures (i.e., impairment of antioxidant systems, mitochondrial dysfunction, involvement of redox-active metals, arachidonic acid pathway activation, and aging), and antioxidant strategies for seizure treatment. Combined, this review highlights pharmacological mechanisms associated with oxidative stress in epileptic seizures and the potential for neuroprotection in epilepsy that targets oxidative stress and is supported by effective antioxidant treatment.     

Excitatory aminoacids and epileptic seizures in immature brain

Data on convulsant and anticonvulsant action of drugs influencing excitatory amino acid receptors in developing rats are reviewed. Agonists of NMDA type of receptors NMDA and homocysteic acid, elicited an age-related seizure pattern--flexion, emprosthotonic seizures--in the first three postnatal weeks of rats. Generalized clonic-tonic seizures appeared only after a longer latency. Kainic acid administration resulted in epileptic automatisms and later in minimal, clonic seizures followed by generalized tonic-clonic seizures. A decrease of sensitivity to convulsant action with age is a general rule for all agonists tested. Different anticonvulsant action of NMDA and nonNMDA antagonists was demonstrated in a model of generalized tonic-clonic seizures induced by pentetrazol, whereas their action against epileptic afterdischarges elicited by electrical stimulation of cerebral cortex was similar. Again, higher efficacy in younger animals was a rule. As far as metabotropic glutamate receptors are concerned, agonists of groups II and III were shown to protect against convulsant action of homocysteic acid in immature rats and an antagonist of group I receptors MPEP suppressed the tonic phase of generalized tonic-clonic seizures induced by pentetrazol more efficiently in younger than in more mature rat pups. Unfortunately, a higher sensitivity to the action of antagonists of ionotropic glutamate receptors was demonstrated also for unwanted side effects (motor functions were compromized). In contrast, glutamate metabotropic receptor antagonist MPEP did not exhibit any serious side effects in rat pups.     

Comparative study of nonlinear properties of EEG signals of normal persons and epileptic patients

Background  

Investigation of the functioning of the brain in living systems has been a major effort amongst scientists and medical practitioners. Amongst the various disorder of the brain, epilepsy has drawn the most attention because this disorder can affect the quality of life of a person. In this paper we have reinvestigated the EEGs for normal and epileptic patients using surrogate analysis, probability distribution function and Hurst exponent.     

Proton magnetic resonance spectroscopy in patients with solitary and sporadic temporal lobe epileptic seizures

Proton magnetic resonance spectroscopy (¹H MRS) is an optional diagnostic method for potential epilepsy surgery candidates. The aim of this study was to determine the credibility of ¹H MRS examination in a group of patients suffering from solitary and sporadic epileptic seizures generated in temporal lobe. We recorded a 100% sensitivity of ¹H MRS in a group of ten patients in terms of detection of a pathological process in the temporal lobe. ¹H MRS also enabled determination of lateralization of the pathological process in three patients with bilateral epileptiform abnormalities on electroencephalography. Based on these results we suggest new perspectives on ¹H MRS as a part of standard diagnostic algorithm for solitary and sporadic temporal lobe epileptic seizures, particularly in cases with normal electroencephalography and magnetic resonance imaging findings.     

Automated diagnosis of epileptic EEG using entropies

Epilepsy is a neurological disorder characterized by the presence of recurring seizures. Like many other neurological disorders, epilepsy can be assessed by the electroencephalogram (EEG). The EEG signal is highly non-linear and non-stationary, and hence, it is difficult to characterize and interpret it. However, it is a well-established clinical technique with low associated costs. In this work, we propose a methodology for the automatic detection of normal, pre-ictal, and ictal conditions from recorded EEG signals. Four entropy features namely Approximate Entropy (ApEn), Sample Entropy (SampEn), Phase Entropy 1 (S1), and Phase Entropy 2 (S2) were extracted from the collected EEG signals. These features were fed to seven different classifiers: Fuzzy Sugeno Classifier (FSC), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Probabilistic Neural Network (PNN), Decision Tree (DT), Gaussian Mixture Model (GMM), and Naive Bayes Classifier (NBC). Our results show that the Fuzzy classifier was able to differentiate the three classes with a high accuracy of 98.1%. Overall, compared to previous techniques, our proposed strategy is more suitable for diagnosis of epilepsy with higher accuracy.     

Adaptive prediction of epileptic seizures from intracranial recordings

Previous work has demonstrated that some dynamic properties of intracranial EEG signals are indicative of epileptic seizures and hence could be used for prediction in order to realize counter measures. However, most previous studies only investigated predictability via offline analysis of EEG signals as compared to actually predicting seizures in a setting applicable to implantable devices. Here we address this problem, which calls for simple and fast online methods, and based on previous offline analyses we hypothesize that prediction can be further improved when using multiple features to detect the preictal patterns. We propose a simple adaptive online method (an evolving neuro-fuzzy model) to adaptively learn such combined features. The classifier starts out with a simple structure and patient-independent parameters and then grows into a personal seizure predictor as recursive methods tune the model structure and parameters. We apply the adaptive classifier to a publicly available database of intracranial recordings from 21 patients and demonstrate that seizure prediction is improved with our online method as compared to offline non-adaptive techniques. We show that our method is robust with respect to those few model parameters, which are not adapted. Moreover, as we report the performance on data from a publicly available seizure database, our results can serve as a yardstick for future method developments.     

Scaling effects and spatio-temporal multilevel dynamics in epileptic seizures

Epileptic seizures are one of the most well-known dysfunctions of the nervous system. During a seizure, a highly synchronized behavior of neural activity is observed that can cause symptoms ranging from mild sensual malfunctions to the complete loss of body control. In this paper, we aim to contribute towards a better understanding of the dynamical systems phenomena that cause seizures. Based on data analysis and modelling, seizure dynamics can be identified to possess multiple spatial scales and on each spatial scale also multiple time scales. At each scale, we reach several novel insights. On the smallest spatial scale we consider single model neurons and investigate early-warning signs of spiking. This introduces the theory of critical transitions to excitable systems. For clusters of neurons (or neuronal regions) we use patient data and find oscillatory behavior and new scaling laws near the seizure onset. These scalings lead to substantiate the conjecture obtained from mean-field models that a Hopf bifurcation could be involved near seizure onset. On the largest spatial scale we introduce a measure based on phase-locking intervals and wavelets into seizure modelling. It is used to resolve synchronization between different regions in the brain and identifies time-shifted scaling laws at different wavelet scales. We also compare our wavelet-based multiscale approach with maximum linear cross-correlation and mean-phase coherence measures.     

The methods of automatic analysis of epileptic EEG

The methods of automatic evaluation of epileptic EEG are reviewed. The aims of the computer analysis of seizure activity and different approaches to this problem are presented.