Short EEG Research Articles

Systems, Subjects, Sessions: To What Extent Do These Factors Influence EEG Data?

Lab-based electroencephalography (EEG) techniques have matured over decades of research and can produce high-quality scientific data. It is often assumed that the specific choice of EEG system has limited impact on the data and does not add variance to the results. However, many low cost and mobile EEG systems are now available, and there is some doubt as to the how EEG data vary across these newer systems. We sought to determine how variance across systems compares to variance across subjects or repeated sessions. We tested four EEG systems: two standard research-grade systems, one system designed for mobile use with dry electrodes, and an affordable mobile system with a lower channel count. We recorded four subjects three times with each of the four EEG systems. This setup allowed us to assess the influence of all three factors on the variance of data. Subjects performed a battery of six short standard EEG paradigms based on event-related potentials (ERPs) and steady-state visually evoked potential (SSVEP). Results demonstrated that subjects account for 32% of the variance, systems for 9% of the variance, and repeated sessions for each subject-system combination for 1% of the variance. In most lab-based EEG research, the number of subjects per study typically ranges from 10 to 20, and error of uncertainty in estimates of the mean (like ERP) will improve by the square root of the number of subjects. As a result, the variance due to EEG system (9%) is of the same order of magnitude as variance due to subjects (32%/sqrt(16) = 8%) with a pool of 16 subjects. The two standard research-grade EEG systems had no significantly different means from each other across all paradigms. However, the two other EEG systems demonstrated different mean values from one or both of the two standard research-grade EEG systems in at least half of the paradigms. In addition to providing specific estimates of the variability across EEG systems, subjects, and repeated sessions, we also propose a benchmark to evaluate new mobile EEG systems by means of ERP responses.

Various modifications of the intrahippocampal kainate model of mesial temporal lobe epilepsy in rats fail to resolve the marked rat-to-mouse differences in type and frequency of spontaneous seizures in this model.

Temporal lobe epilepsy (TLE) is the most common type of acquired epilepsy in adults. TLE can develop after diverse brain insults, including traumatic brain injury, infections, stroke, or prolonged status epilepticus (SE). Post-SE rodent models of TLE are widely used to understand mechanisms of epileptogenesis and develop treatments for epilepsy prevention. In this respect, the intrahippocampal kainate model of TLE in mice is of interest, because highly frequent spontaneous electrographic seizures develop in the kainate focus, allowing evaluation of both anti-seizure and anti-epileptogenic effects of novel drugs with only short EEG recording periods, which is not possible in any other model of TLE, including the intrahippocampal kainate model in rats. In the present study, we investigated whether the marked mouse-to-rat difference in occurrence and frequency of spontaneous seizures is due to a species difference or to technical variables, such as anesthesia during kainate injection, kainate dose, or location of kainate injection and EEG electrode in the hippocampus. When, as in the mouse model, anesthesia was used during kainate injection, only few rats developed epilepsy, although severity or duration of SE was not affected by isoflurane. In contrast, most rats developed epilepsy when kainate was injected without anesthesia. However, frequent electrographic seizures as observed in mice did not occur in rats, irrespective of location of kainate injection (CA1, CA3) or EEG recording electrode (CA1, CA3, dentate gyrus) or dose of kainate injected. These data indicate marked phenotypic differences between mice and rats in this model. Further studies should explore the mechanisms underlying this species difference.

Assessing multiscale permutation entropy for short electroencephalogram recordings

Electroencephalogram (EEG) has been a standard tool to monitor the status of the brain. For a quantification of EEG recordings, permutation entropy (PE) has been of interest due to simplicity and robustness to noise. A multiscale extension of PE, called multiscale PE (MPE), has been promising for describing the dynamical characteristics of EEG over multiple temporal scales. However, an imprecise estimation of MPE at large scales limits its application for analyzing of short EEG recordings. Here, with the aim of estimating MPE accurately, a modified MPE (MMPE) measure is presented. The proposed MMPE consists of two processes: (1) computation of PE values of all possible coarse-grained EEG time-series, (2) averaging of PE values at each scale. Through simulations with two synthetic signals, i.e., white and 1 / f noises, MMPE proves its capability over MPE in terms of accuracy. Experimental results using the actual EEG recordings indicate that MMPE is an improved quantifier in the sense that MMPE reduces variance of entropy estimation in comparison with MPE.

5. Sleep deprivation in the activation of interictal spikes and epileptic seizures

Objective To describe and review the use of sleep deprived EEG in the activation of interictal spikes and epileptic seizures. Background Sleep deprivation (SD) has been used to enhance EEG sensitivity in the diagnosis of epilepsy. However, standardized guidelines for the use of SD-EEGs are lacking. Here, we provide an update in the literature of sleep SD-EEG and epilepsy as well as presenting data from a retrospective study. Design/methods A literature search using PubMed was carried out to identify papers focusing on sleep deprivation EEG and interictal discharges. A total of 39 abstracts were obtained, 13 were screened and included in this review. We also present data from a retrospective study of 156 patients that were assessed with a short and long-monitoring EEG as well as SD-EEG at Son Espases Hospital (Spain) and evaluated their sensitivity in the diagnosis of interictal discharges in patients with suspected epilepsy. Results Further studies are needed to evaluate the role of SD-EEG in patients with suspected epilepsy. In our retrospective study 21% of patients with a previous normal awake EEG showed interictal discharges in the SD-EEG. Conclusions Some studies have shown that SD-EEG increases sensitivity and specificity diagnosing interictal activity. However, the duration of sleep deprivation and length of the test varies according to the different studies. The role of SD-EEG should be further evaluated. The use of subtemporal electrodes may contribute to the diagnosis of temporal lobe epileptiform focus.

Length matters: Improved high field EEG–fMRI recordings using shorter EEG cables

BackgroundThe use of concurrent EEG–fMRI recordings has increased in recent years, allowing new avenues of medical and cognitive neuroscience research; however, currently used setups present problems with data quality and reproducibility. New methodWe propose a compact experimental setup for concurrent EEG–fMRI at 4T and compare it to a more standard reference setup. The compact setup uses short EEG cables connecting to the amplifiers, which are placed right at the back of the head RF coil on a form-fitting extension force-locked to the patient MR bed. We compare the two setups in terms of sensitivity to MR-room environmental noise, interferences between measuring devices (EEG or fMRI), and sensitivity to functional responses in a visual stimulation paradigm. ResultsThe compact setup reduces the system sensitivity to both external noise and MR-induced artefacts by at least 60%, with negligible EEG noise induced from the mechanical vibrations of the cryogenic cooling compression pump. Comparison with existing methodsThe compact setup improved EEG data quality and the overall performance of MR-artifact correction techniques. Both setups were similar in terms of the fMRI data, with higher reproducibility for cable placement within the scanner in the compact setup. ConclusionsThis improved compact setup may be relevant to MR laboratories interested in reducing the sensitivity of their EEG–fMRI experimental setup to external noise sources, setting up an EEG–fMRI workplace for the first time, or for creating a more reproducible configuration of equipment and cables. Implications for safety and ergonomics are discussed.

Experience with short video EEG in small town (yield and cost effectiveness)

Experience with short video EEG in small town (yield and cost effectiveness)

Classification of obsessive compulsive disorder by EEG complexity and hemispheric dependency measurements.

In the present study, both single channel electroencephalography (EEG) complexity and two channel interhemispheric dependency measurements have newly been examined for classification of patients with obsessive-compulsive disorder (OCD) and controls by using support vector machine classifiers. Three embedding entropy measurements (approximate entropy, sample entropy, permutation entropy (PermEn)) are used to estimate single channel EEG complexity for 19-channel eyes closed cortical measurements. Mean coherence and mutual information are examined to measure the level of interhemispheric dependency in frequency and statistical domain, respectively for eight distinct electrode pairs placed on the scalp with respect to the international 10-20 electrode placement system. All methods are applied to short EEG segments of 2 s. The classification performance is measured 20 times with different 2-fold cross-validation data for both single channel complexity features (19 features) and interhemispheric dependency features (eight features). The highest classification accuracy of 85 ±5.2% is provided by PermEn at prefrontal regions of the brain. Even if the classification success do not provided by other methods as high as PermEn, the clear differences between patients and controls at prefrontal regions can also be obtained by using other methods except coherence. In conclusion, OCD, defined as illness of orbitofronto-striatal structures [Beucke et al., JAMA Psychiatry70 (2013) 619-629; Cavedini et al., Psychiatry Res.78 (1998) 21-28; Menzies et al., Neurosci. Biobehav. Rev.32(3) (2008) 525-549], is caused by functional abnormalities in the pre-frontal regions. Particularly, patients are characterized by lower EEG complexity at both pre-frontal regions and right fronto-temporal locations. Our results are compatible with imaging studies that define OCD as a sub group of anxiety disorders exhibited a decreased complexity (such as anorexia nervosa [Toth et al., Int. J. Psychophysiol.51(3) (2004) 253-260] and panic disorder [Bob et al., Physiol. Res.55 (2006) S113-S119]).

26. A newborn with transient giant sep revealed during a seizure by an integrated neurophysiological monitoring

The role of ElectroEncephaloGraphy (EEG) and Somatosensory-Evoked-Potentials (SEP) in Neonatal as well as Adult-ICU is validated. To evaluate brain-functions in newborns with high risk of encephalic damage we also proposed an Integrated Multiparametric-Neurophysiological-Assessment (IMNA) simultaneously recording VEEG, CFM, Density-Spectral-Analisys and SEPs. We describe the case of a term-newborn (Apgar- Index 6–9, pH 7,03) in which an 1-h-IMNA was performed at 24 h from birth, to best define the encephalic damage. Results:An early encephalic ultrasound study showed only poor evidences. During IMNA baby incidentally presented short episodes of tonic–clonic motions of left arm. These clinical signs were associated with short right emispheric EEG hypovoltages while the cascade-SEPs monitoring showed a sudden transient change in voltage: right giant SEPs traces simultaneous to seizures. After phenobarbital administration seizures stopped and the right SEPs traces return to normality. An early encephalic MRI showed a right parietal ischemic lesion and also a right extracerebral hemorrhagic injury. The latest one appeared reduced in a further MRI. No further seizures were reported. 1 h-IMNA monitoring, especially cascade-SEPs, is a useful tool to perform an early evaluation and assess prognosis of neonatal hypoxic brain damage; it also allows to correlate neurophysiological parameters? trends to clinical events.

Electroencephalographic aspects in dogs with congenital hydrocephalus

Introduction: Congenital hydrocephalus (CH) is a common neurologic disorder in dogs defined as an increased cerebrospinal fluid volume within the cranial cavity with no any specific causes. The diagnosis of this disease is based on the clinical features and imagistic examination (ultrasonography, CT, MRI) of the brain. Electroencephalography (EEG) is the best electrodiagnostic test in trying to assess the impact of hydrocephalus on electric activity of the brain . T he aim of this study is to describe short time EEG changes in dogs with CH. Materials and Methods: EEG was performed on 6 dogs with congenital hydrocephalus under general anaesthesia with medetomidine hydrochloride 0.05 mg/kg inj. EEGs were obtained via five subdermal stainless steel needle electrodes as described by Redding (1978). In EEG recoding the parameters used were: sensitivity = 70I¼V/cm; time constant = 0.3 seconds; Hf = 70 Hz; Lf = 0.5 Hz; notch filter inserted; impedance of all electrodes < 10 kI©. The results of the present study revealed abnormalities in EEG background activity in all dogs. Focal abnormalities were seen in 5 patients (slow waves in 2 dogs, spikes and sharp waves in 2 dogs, focal attenuation in 2 cases) and generalized abnormal activities in 4 dogs (generalized slow waves in 4 dogs and generalised spikes and sharp waves in 1 dog). The percentage of EEG interictal epileptiform discharges (66.67% of dogs had spikes, sharp waves, spikes and sharp waves complexes) was greater than seizures clinical seen (33.33% of dogs). In conclusion , EEG gived us important information about the severity of changes induced by hydrocephalus. Furthermore, this test is useful in the diagnosis of seizures associated with hydrocephalus and allows monitoring the therapeutic effects of antiepileptic drugs.

Hyperactivity and sensation seeking as autoregulatory attempts to stabilize brain arousal in ADHD and mania?

Hypoarousal as indicated by skin conductance and electroencephalography (EEG) has been discussed as a pathogenetic factor in attention-deficit/hyperactivity disorder (ADHD). The aim of this paper was to review these arousal-related pathogenetic concepts and to present the more recently proposed vigilance regulation model of affective disorders and ADHD. The latter builds on methodological advances in classifying short EEG segments into vigilance stages (Vigilance Algorithm Leipzig, VIGALL), indicating different states of global brain function ("brain arousal"). VIGALL allows the objective assessment of vigilance regulation under defined conditions, e.g. how fast vigilance declines to lower vigilance stages associated with drowsiness during 15-20-min EEG recordings under resting conditions with eyes closed. According to the vigilance regulation model, the hyperactivity and sensation seeking observed in overtired children, ADHD and mania may be interpreted as an autoregulatory attempt to create a stimulating environment in order to stabilize vigilance. The unstable regulation of vigilance observed in both mania and ADHD may thus explain the attention deficits, which become especially prominent in monotonous sustained attention tasks. Among the arguments supporting the vigilance regulation model are the facts that destabilizing vigilance (e.g., via sleep deprivation) can trigger or exacerbate symptoms of ADHD or mania, whereas stabilizing vigilance (e.g., via psychostimulants, reducing sleep deficits) alleviates these symptoms. The potential antimanic effects of methylphenidate are presently being studied in an international randomized controlled trial. We propose vigilance regulation as a converging biomarker, which could be useful for identifying treatment responders to psychostimulants and forming pathophysiologically more homogeneous ADHD subgroups for research purposes.

4. Classification of EEG graphoelements with supervised and unsupervised learning algorithms

The electroencephalogram (EEG) provides markers of brain disturbances in the field of epilepsy. In short duration EEG data recordings, the epileptic graphoelements may not manifest. The visual analysis of lengthy signals is a tedious task. It is necessary to track the EEG activity on the computer screen and to detect the epileptiform graphoelements and the other pathological activity. The automation of the process is needed. We will compare the EEG wave classification both by supervised and unsupervised learning algorithms. The feasibility to detect the changes in the microstructure of epileptic activity will be verified. The procedure is based on multichannel adaptive segmentation, feature extraction and classification of graphoelements. To take into account the non-stationary behavior of the signal, the features were extracted from segments detected by adaptive segmentation. The features included amplitude variance, parameters describing duration, number of segments, power in the frequency bands, signal entropy, number of zero crossings, nonlinear operator and others. The classification was performed by (fuzzy) cluster analysis and by artificial neural network using genetic algorithm. Adaptive segmentation extracts the discriminative features much better, than segmentation with fixed segment boundaries. The temporal profiles were plotted for EEG channels to reflect the macro-structure of the signal. The graphoelements of relevant class were highlighted by a color. Learning with a teacher was more successful than unsupervised algorithms.

Poster #T201 BEHAVIOURAL AND FMRI EVIDENCE OF SEMANTIC CATEGORISATION DEFICITS IN SCHIZOPHRENIA

There are two inter-related categories of EEG measurement: 1, EEG currents or power and; 2, EEG network properties such as coherence and phase delays. The purpose of this study was to compare the ability of these two different categories of EEG measurement to predict performance on the Weschler Intelligence test (WISC-R).Resting eyes closed EEG was recorded from 19 scalp locations with a linked ears reference from 442 subjects aged 5–52 years. The Weschler Intelligence test was administered to the same subjects but not while the EEG was recorded. Subjects were divided into high IQ (≥120) and low IQ (≤90) groups. EEG variables at P<.05 were entered into a factor analysis and then the single highest loading variable on each factor was entered into a discriminant analysis where groups were high IQ vs. low.Q.Discriminant analysis of high vs. low IQ was 92.81–97.14% accurate. Discriminant scores of intermediate IQ subjects (i.e. 90<IQ<120) were intermediate between the high and low IQ groups. Linear regression predictions of IQ significantly correlated with the discriminant scores (r=0.818–0.825, P<10−6). The ranking of effect size was EEG phase>EEG coherence>EEG amplitude asymmetry>absolute power>relative power and power ratios. The strongest correlations to IQ were short EEG phase delays in the frontal lobes and long phase delays in the posterior cortical regions, reduced coherence and increased absolute power.The findings are consistent with increased neural efficiency and increased brain complexity as positively related to intelligence, and with frontal lobe synchronization of neural resources as a significant contributing factor to EEG and intelligence correlations.Quantitative EEG predictions of intelligence provide medium to strong effect size estimates of cognitive functioning while simultaneously revealing a deeper understanding of the neurophysiological substrates of intelligence.

Comparative analysis of changes in short EEG segments during music perception based on event-related synchronization/desynchronization and wavelet synchronicity

The goal of the pilot study was to analyze the characteristics of changes in short EEG segments recorded from 32 sites during perception of musical melodies by healthy subjects depending on logical (recognition) and emotional (pleasant/unpleasant) estimation of the melody. For this purpose, the changes in event-related synchronization/desynchronization and the indices of wavelet synchrony of EEG responses were compared in 31 healthy subjects (18 to 60 years old). It has been shown that melody recognition during logical estimation of music is accompanied by event-related desynchronization in the left frontal-parietal-temporal area. Emotional estimation of a melody is characterized by event-related synchronization in the left frontal-temporal area for pleasant melodies, desynchronization in the temporal area for unpleasant melodies, and desynchronization in the occipital area for melodies inducing no emotional response. The analysis of EEG wavelet synchronization characterizing reactive changes in the interaction between cortical areas shows that the most distinct topographic differences are associated with the type of music processing: logical (familiar/unfamiliar) or emotional (pleasant/unpleasant). The changes in interhemispheric connections between the associative cortical areas (central, frontal, temporal) are greater during emotional estimation, while the changes in inter- and intrahemispheric connections between the projection areas of the acoustic analyzer (temporal area) are greater during logical estimation. It is assumed that the revealed event-related synchronization/desynchronization is most likely to reflect the activation component of musical fragment estimation, whereas wavelet analysis provides insight into the character of musical stimulus processing.

Electroencephalographic recordings in dogs suffering from idiopathic and symptomatic epilepsy: Diagnostic value of interictal short time EEG protocols supplemented by two activation techniques

The diagnostic value of interictal short time electroencephalographic (EEG) recordings in epileptic dogs under general anaesthesia with propofol and the muscle relaxant rocuronium bromide was investigated. Two activation techniques, namely photic stimulation and hyperventilation, were evaluated for their potential to enhance the diagnostic validity of these recordings. Sixty-one dogs suffering from idiopathic epilepsy and 28 dogs suffering from symptomatic epilepsy were included. Electroencephalograms were recorded using five subdermal EEG electrodes (F3, F4, Cz, O1 and O2). All 89 EEGs were analysed visually and 61 were also evaluated quantitatively with fast Fourier transformation. Interictal paroxysmal epileptiform activity was found in 25% of idiopathic and in 29% of symptomatic epileptic dogs. Quantitative analysis of the EEGs (qEEGs) detected significant differences of frequency analysis in single reading points without any continuous changes of frequency bands. A comparison between healthy and affected brain hemispheres in seven dogs with focal lesions of one hemisphere did not show any significant differences in qEEG analysis. qEEG was not more sensitive than visual evaluation. Despite the use of activation techniques, the results showed that short time EEG recordings in epileptic dogs can detect interictal epileptic activity in less than one third of all seizuring dogs and is not a useful screening method.

Newly diagnosed psychogenic nonepileptic seizures: Health care demand prior to and following diagnosis at a first seizure clinic

Patients with psychogenic nonepileptic seizures (PNES) are heavy users of emergency and nonemergency health care. We performed a 1-year prospective audit of use of a group of PNES-related health care items in patients with newly diagnosed (mean duration: 7.3months) PNES from PNES onset to diagnosis and from diagnosis to 6months postdiagnosis. Twenty-eight patients (20 women, age: 34±16years) were responsible for 14 general practitioner home visits, 31 ambulance calls, 34 emergency department visits, 21 hospital admissions (66 inpatient days), 8 MRI scans, 24 CT scans, 2 standard EEGs, 28 short video EEG recordings, and 5 ambulatory EEG recordings. In the 6months following diagnosis, there were 2 emergency department visits (94.1% reduction), no hospital admissions (100% reduction), 2 ambulance calls, no general practitioner visits, 1 MRI scan, and no CT scans or EEGs. The immediacy of this marked health care demand reduction suggests that the relationship between presentation of diagnosis and health care demand reduction is causal.

A Quantitative EEG Method for Detecting Post Clamp Changes During Carotid Endarterectomy

To apply univariate and multivariate analyses of EEG spectral features in order to detect subtle post-clamp changes during carotid endarterectomy, and to devise a data-driven, multivariate classification method for rapid, real-time detection of small post-clamp EEG changes. We used intraoperative EEG records of 27 patients who underwent carotid endarterectomy (CEA) and did not undergo a shunt per surgeon's and technologist's visual inspection of the electro-encephalogram (EEG). Furthermore, these records could not be flagged with r-sBSI (revised spatial brain symmetry index) or r-tBSI (revised temporal brain symmetry index) qEEG (quantitative EEG) metrics. Spectral energies of 10s EEG snippets were recorded at 5min intervals from 10min pre-clamp to 20min post-clamp. Using receiver operating characteristic curves, the power of standard EEG spectral bands in detection of post-clamp changes was determined. Next, we employed Fisher Linear Discriminant Analysis for classifier-guided multivariate feature selection and classification. In univariate analysis, delta waves in non-frontal areas, irrespective of the laterality of the affected hemisphere, were found to be generally more powerful indicators of clamp-induced EEG changes. In multivariate analysis, cross-validation results for Fisher LDA produced subject-independent equal error rates as low as 32% when separating post-clamp signals from pre-clamp controls. Within our subject group, the intricate clamp-induced EEG signatures were predominantly anterior, bilateral, and had a strong delta rhythm presence. The overall post-clamp saliencies, both in univariate and multivariate analyses, were time-dependent. By applying automatic data-driven feature extraction and classification to short EEG records, it is possible to construct subject-independent computational models that can detect subtle post clamp changes possibly caused by small perturbations in cerebral blood flow. These subtle changes are missed by visual inspection of the EEG and by other quantitative EEG techniques such as r-sBSI and r-tBSI. Within our subject group, the intricate post-clamp EEG signatures were predominantly anterior, bilateral, and had a strong delta rhythm presence.

Web-Based Collection of Expert Opinion on Routine Scalp EEG: Software Development and Interrater Reliability

Computerized detection of epileptiform transients (ETs), characterized by interictal spikes and sharp waves in the EEG, has been a research goal for the last 40 years. A reliable method for detecting ETs would assist physicians in interpretation and improve efficiency in reviewing long-term EEG recordings. Computer algorithms developed thus far for detecting ETs are not as reliable as human experts, primarily due to the large number of false-positive detections. Comparing the performance of different algorithms is difficult because each study uses individual EEG test datasets. In this article, we present EEGnet, a distributed web-based platform for the acquisition and analysis of large-scale training datasets for comparison of different EEG ET detection algorithms. This software allows EEG scorers to log in through the web, mark EEG segments of interest, and categorize segments of interest using a conventional clinical EEG user interface. This software platform was used by seven board-certified academic epileptologists to score 40 short 30-second EEG segments from 40 patients, half containing ETs and half containing artifacts and normal variants. The software performance was adequate. Interrater reliability for marking the location of paroxysmal activity was low. Interrater reliability of marking artifacts and ETs was high and moderate, respectively.

Neonatal seizure recognition – Comparative study of continuous-amplitude integrated EEG versus short conventional EEG recordings

Objectives This study aims to detect seizures by amplitude-integrated electroencephalography (EEG) (aEEG) as compared with conventional EEG (cEEG) by clinicians with different levels of expertise. Methods Simultaneous 10 min aEEG/cEEG recordings were time-locked and assessed for seizure activity. aEEG was assessed by a neonatologist, a fellow and a medical student and cEEG by two child neurologists and a neonatologist. Results A total of 265 paired epochs from 38 simultaneous recording were assessed. Forty-one seizure episodes were diagnosed in 31 epochs in the cEEG recordings of 10 infants. Sensitivity and specificity ranged from 68% to 84% and from 71% to 84%, respectively, per detection of epochs with seizures and from 71% to 84% and from 36% to 96% per detection of individual seizures. No agreement was found between the observations of the student, and those of the fellow or neonatologist. Substantial agreement was found between the fellow and neonatologist. Before cEEG was commenced, seizures were detected by aEEG in 22 infants. Conclusions aEEG has high sensitivity and specificity in the hands of experienced users. Inexperienced new users may have a high rate of misdiagnosed seizures. Early recording of high-risk infants can help in the early diagnosis and treatment of seizures. Significance Diagnosis and treatment of seizures in aEEG should be carried out by experienced users and should be supplemented with cEEG when available.

Interictal EEG spikes identify the region of electrographic seizure onset in some, but not all, pediatric epilepsy patients

The role of sharps and spikes, interictal epileptiform discharges (IEDs), in guiding epilepsy surgery in children remains controversial, particularly with intracranial electroencephalography (IEEG). Although ictal recording is the mainstay of localizing epileptic networks for surgical resection, current practice dictates removing regions generating frequent IEDs if they are near the ictal onset zone. Indeed, past studies suggest an inconsistent relationship between IED and seizure-onset location, although these studies were based upon relatively short EEG epochs. We employ a previously validated, computerized spike detector to measure and localize IED activity over prolonged, representative segments of IEEG recorded from 19 children with intractable, mostly extratemporal lobe epilepsy. Approximately 8 h of IEEG, randomly selected 30-min segments of continuous interictal IEEG per patient, were analyzed over all intracranial electrode contacts. When spike frequency was averaged over the 16-time segments, electrodes with the highest mean spike frequency were found to be within the seizure-onset region in 11 of 19 patients. There was significant variability between individual 30-min segments in these patients, indicating that large statistical samples of interictal activity were required for improved localization. Low-voltage fast EEG at seizure onset was the only clinical factor predicting IED localization to the seizure-onset region. Our data suggest that automated IED detection over multiple representative samples of IEEG may be of utility in planning epilepsy surgery for children with intractable epilepsy. Further research is required to better determine which patients may benefit from this technique a priori.

A Novel Method for Detection of Epilepsy in Short and Noisy EEG Signals Using Ordinal Pattern Analysis

Introduction: In this paper, a novel complexity measure is proposed to detect dynamical changes in nonlinear systems using ordinal pattern analysis of time series data taken from the system. Epilepsy is considered as a dynamical change in nonlinear and complex brain system. The ability of the proposed measure for characterizing the normal and epileptic EEG signals when the signal is short or is contaminated with noise is investigated and compared with some traditional chaos-based measures. Materials and Methods: In the proposed method, the phase space of the time series is reconstructed and then partitioned using ordinal patterns. The partitions can be labeled using a set of symbols. Therefore, the state trajectory is converted to a symbol sequence. A finite state machine is then constructed to model the sequence. A new complexity measure is proposed to detect dynamical changes using the state transition matrix of the state machine. The proposed complexity measure was applied to detect epilepsy in short and noisy EEG signals and the results were compared with some chaotic measures. Results: The results indicate that this complexity measure can distinguish normal and epileptic EEG signals with an accuracy of more than 97% for clean EEG and more than 75% for highly noised EEG signals. Discussion and Conclusion: The complexity measure can be computed in a very fast and easy way and, unlike traditional chaotic measures, is robust with respect to noise corrupting the data. This measure is also capable of dynamical change detection in short time series data.