Progress in predicting seizure episodes with nonlinear methods

The genetic absence epilepsy rat from Strasbourg as a model to decipher the neuronal and network mechanisms of generalized idiopathic epilepsies

First characterized in 1982, the genetic absence epilepsy rat from Strasbourg (GAERS) has emerged as an animal model highly reminiscent of a specific form of idiopathic generalized epilepsy. Both its electrophysiological (spike-and-wave discharges) and behavioral (behavioral arrest) features fit well with those observed in human patients with typical absence epilepsy and required by clinicians for diagnostic purposes. In addition, its sensitivity to antiepileptic drugs closely matches what has been described in the clinic, making this model one of the most predictive. Here, we report how the GAERS, thanks to its spontaneous, highly recurrent and easily recognizable seizures on electroencephalographic recordings, allows to address several key-questions about the pathophysiology and genetics of absence epilepsy. In particular, it offers the unique possibility to explore simultaneously the neural circuits involved in the generation of seizures at different levels of integration, using multiscale methodologies, from intracellular recording to functional magnetic resonance imaging. In addition, it has recently allowed to perform proofs of concept for innovative therapeutic strategies such as responsive deep brain stimulation or synchrotron-generated irradiation based radiosurgery.

On the proper selection of preictal period for seizure prediction

Supervised machine learning-based seizure prediction methods consider preictal period as an important prerequisite parameter during training. However, the exact length of the preictal state is unclear and varies from seizure to seizure. We propose a novel statistical approach for proper selection of the preictal period, which can also be considered either as a measure of predictability of a seizure or as the prediction capability of an understudy feature. The optimal preictal periods (OPPs) obtained from the training samples can be used for building a more accurate classifier model. The proposed method uses amplitude distribution histograms of features extracted from electroencephalogram (EEG) recordings. To evaluate this method, we extract spectral power features in different frequency bands from monopolar and space-differential EEG signals of 18 patients suffering from pharmacoresistant epilepsy. Furthermore, comparisons among monopolar channels with space-differential channels, as well as intracranial EEG (iEEG) and surface EEG (sEEG) signals, indicate that while monopolar signals perform better in iEEG recordings, no significant difference is noticeable in sEEG recordings.

Preictal dynamics of EEG complexity in intracranially recorded epileptic seizure: a case report

Recent findings suggest that neural complexity reflecting a number of independent processes in the brain may characterize typical changes during epileptic seizures and may enable to describe preictal dynamics. With respect to previously reported findings suggesting specific changes in neural complexity during preictal period, we have used measure of pointwise correlation dimension (PD2) as a sensitive indicator of nonstationary changes in complexity of the electroencephalogram (EEG) signal. Although this measure of complexity in epileptic patients was previously reported by Feucht et al (Applications of correlation dimension and pointwise dimension for non-linear topographical analysis of focal onset seizures. Med Biol Comput. 1999;37:208-217), it was not used to study changes in preictal dynamics. With this aim to study preictal changes of EEG complexity, we have examined signals from 11 multicontact depth (intracerebral) EEG electrodes located in 108 cortical and subcortical brain sites, and from 3 scalp EEG electrodes in a patient with intractable epilepsy, who underwent preoperative evaluation before epilepsy surgery. From those 108 EEG contacts, records related to 44 electrode contacts implanted into lesional structures and white matter were not included into the experimental analysis.The results show that in comparison to interictal period (at about 8-6 minutes before seizure onset), there was a statistically significant decrease in PD2 complexity in the preictal period at about 2 minutes before seizure onset in all 64 intracranial channels localized in various brain sites that were included into the analysis and in 3 scalp EEG channels as well. Presented results suggest that using PD2 in EEG analysis may have significant implications for research of preictal dynamics and prediction of epileptic seizures.

NONLINEAR ANALYSIS OF PHYSIOLOGICAL SIGNALS: A REVIEW

This paper reviews various nonlinear analysis methods for physiological signals. The assessment is based on a discussion of chaos-inspired methods, such as fractal dimension (FD), correlation dimension (D2), largest Lyapunov exponet (LLE), Renyi's entropy (REN), Shannon spectral entropy (SEN), and approximate entropy (ApEn). We document that these methods are used to extract discriminative features from electroencephalograph (EEG) and heart rate variability (HRV) signals by reviewing the relevant scientific literature. EEG features can be used to support the diagnosis of epilepsy and HRV features can be used to support the diagnosis of cardiovascular diseases as well as diabetes. Documenting the widespread use of these and other nonlinear methods supports our thesis that the study of feature extraction methods, based on the chaos theory, is an important subject which has been gaining more and significance in biomedical engineering. We adopt the position that pursuing research in the field of biomedical engineering is ultimately a progmatic activity, where it is necessary to engage in features that work. In this case, the nonlinear features are working well, even if we do not have conclusive evidence that the underlying physiological phenomena are indeed chaotic.

Clinical features of the pre-ictal state: mood changes and premonitory symptoms

Identifying the pre-ictal state clinically would improve our understanding of seizure onset and suggest opportunities for new treatments. In our previous paper-diary study, increased stress and less sleep predicted seizures. Utilizing electronic diaries, we expanded this investigation. Variables were identified by their association with subsequent seizure using logit-normal random effects models fit by maximum likelihood. Nineteen subjects with localization-related epilepsy kept e-diaries for 12-14 weeks and reported 244 eligible seizures. In univariate models, several mood items and ten premonitory features were associated with increased odds of seizure over 12h. In multivariate models, a 10-point improvement in total mood decreased seizure risk by 25% (OR 0.75, CI 0.61-0.91, p=004) while each additional significant premonitory feature increased seizure risk by nearly 25% (OR 1.24, CI 1.13-1.35, p<001) over 12h. Pre-ictal changes in mood and premonitory features may predict seizure occurrence and suggest a role for behavioral intervention and pre-emptive therapy in epilepsy.

AUTOMATIC DETECTION OF EPILEPTIC EEG SIGNALS USING HIGHER ORDER CUMULANT FEATURES

The unpredictability of the occurrence of epileptic seizures makes it difficult to detect and treat this condition effectively. An automatic system that characterizes epileptic activities in EEG signals would allow patients or the people near them to take appropriate precautions, would allow clinicians to better manage the condition, and could provide more insight into these phenomena thereby revealing important clinical information. Various methods have been proposed to detect epileptic activity in EEG recordings. Because of the nonlinear and dynamic nature of EEG signals, the use of nonlinear Higher Order Spectra (HOS) features is a seemingly promising approach. This paper presents the methodology employed to extract HOS features (specifically, cumulants) from normal, interictal, and epileptic EEG segments and to use significant features in classifiers for the detection of these three classes. In this work, 300 sets of EEG data belonging to the three classes were used for feature extraction and classifier development and evaluation. The results show that the HOS based measures have unique ranges for the different classes with high confidence level (p-value < 0.0001). On evaluating several classifiers with the significant features, it was observed that the Support Vector Machine (SVM) presented a high detection accuracy of 98.5% thereby establishing the possibility of effective EEG segment classification using the proposed technique.

Concepts of connectivity and human epileptic activity

This review attempts to place the concept of connectivity from increasingly sophisticated neuroimaging data analysis methodologies within the field of epilepsy research. We introduce the more principled connectivity terminology developed recently in neuroimaging and review some of the key concepts related to the characterization of propagation of epileptic activity using what may be called traditional correlation-based studies based on EEG. We then show how essentially similar methodologies, and more recently models addressing causality, have been used to characterize whole-brain and regional networks using functional MRI data. Following a discussion of our current understanding of the neuronal system aspects of the onset and propagation of epileptic discharges and seizures, we discuss the most advanced and ambitious framework to attempt to fully characterize epileptic networks based on neuroimaging data.

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.

Initial symptoms, precipitant factors, and techniques to control epileptic seizures: the carer's perspective

Subjective experiences of seizures and events that occur prior to seizures may be useful in assisting health professionals to devise treatment plans tailored to the individual. The aim of this study was to investigate carers' knowledge of their patients' preseizure activity. Of 240 questionnaires mailed out to registrants on an epilepsy research database, 78 were anonymously returned (32.5%). Participants were aged between 18 and 89, with a mean age of 50.94 years (SD=17.23), and 82.1% were female. Of 78 participants, 74.4% reported that their patients experienced at least one symptom prior to a seizure, 88.5% reported that their patients experienced at least one seizure as a result of a specific event, and 56.4% reported that their patients had tried at least one technique to stop a seizure. The rates reported are comparable to those reported in other studies measuring responses from people with epilepsy.

The lived experience of initial symptoms of and factors triggering epileptic seizures

The aim of this study was to document the self-perception of initial symptoms of and factors triggering epileptic seizures in a sample of people with epilepsy (PWE) and their carers. Among 600 participants, questionnaires were returned by 309 (51.5%), of whom 72.8% were PWE and 27.2% were carers and others. Experiencing at least one symptom prior to a seizure was reported by 86.9% of PWE and 74% of carers. The most common symptoms were a funny feeling, confusion, and anxiety. Experiencing one trigger that resulted in a seizure was reported by 89.8% of PWE and 85.5% of carers. The most common triggers were tiredness, stress, and sleep deprivation. Among PWE and their carers, 63.6% and 51.3%, respectively, indicated that they can tell when a seizure is about to occur, and 26.7% and 15.4%, respectively, indicated that they felt they could stop a seizure. The most common techniques were resting, medication, and relaxation.

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.

RESULTS

Using random shuffled surrogate analysis, we have obtained some of the nonlinear features that was obtained by Andrzejak et al. [Phys Rev E 2001, 64:061907], for the epileptic patients during seizure. Probability distribution function shows that the activity of an epileptic brain is nongaussian in nature. Hurst exponent has been shown to be useful to characterize a normal and an epileptic brain and it shows that the epileptic brain is long term anticorrelated whereas, the normal brain is more or less stochastic. Among all the techniques, used here, Hurst exponent is found very useful for characterization different cases.

CONCLUSION

In this article, differences in characteristics for normal subjects with eyes open and closed, epileptic subjects during seizure and seizure free intervals have been shown mainly using Hurst exponent. The H shows that the brain activity of a normal man is uncorrelated in nature whereas, epileptic brain activity shows long range anticorrelation.

Zwanzig-Mori projection operators and EEG dynamics: deriving a simple equation of motion

We present a macroscopic theory of electroencephalogram (EEG) dynamics based on the laws of motion that govern atomic and molecular motion. The theory is an application of Zwanzig-Mori projection operators. The result is a simple equation of motion that has the form of a generalized Langevin equation (GLE), which requires knowledge only of macroscopic properties. The macroscopic properties can be extracted from experimental data by one of two possible variational principles. These variational principles are our principal contribution to the formalism. Potential applications are discussed, including applications to the theory of critical phenomena in the brain, Granger causality and Kalman filters.

PACS code: 87.19.lj