Feature extraction and recognition of epileptiform activity in EEG by combining PCA with ApEn

This paper proposes a new method for feature extraction and recognition of epileptiform activity in EEG signals. The method improves feature extraction speed of epileptiform activity without reducing recognition rate. Firstly, Principal component analysis (PCA) is applied to the original EEG for dimension reduction and to the decorrelation of epileptic EEG and normal EEG. Then discrete wavelet transform (DWT) combined with approximate entropy (ApEn) is performed on epileptic EEG and normal EEG, respectively. At last, Neyman–Pearson criteria are applied to classify epileptic EEG and normal ones. The main procedure is that the principle component of EEG after PCA is decomposed into several sub-band signals using DWT, and ApEn algorithm is applied to the sub-band signals at different wavelet scales. Distinct difference is found between the ApEn values of epileptic and normal EEG. The method allows recognition of epileptiform activities and discriminates them from the normal EEG. The algorithm performs well at epileptiform activity recognition in the clinic EEG data and offers a flexible tool that is intended to be generalized to the simultaneous recognition of many waveforms in EEG.     

Complicated relationship between autism with regression and epilepsy

We retrospectively evaluated a set of 205 children with autism and compared it to the partial sub-set of 71 (34.6%) children with a history of regression. From 71 children with regression, signs of epileptic processes were present in 43 (60.6%), 28 (65.12%) suffered clinical epileptic seizures, and 15 (34.9%) just had an epileptiform abnormality on the EEG. In our analysis, autistic regression is substantially more associated with epileptic process symptoms than in children with autism and no history of regression. More than 90% of children with a history of regression also show IQ < 70 and reduced functionality. Functionality and IQ further worsens with the occurrence of epileptic seizures (98% of children with regression and epilepsy have IQ < 70). We proved that low IQ and reduced functionality significantly correlate rather with epileptic seizures than just sub-clinical epileptiform abnormality on EEG. Clinical epileptic seizures associated with regression significantly influence the age of regression and its clinical type. The age of regression is higher compared to children with regression without epileptic seizures (in median: 35 months of age in patients with seizures while only 24 months in other patients). Patients with seizures revealed regression after 24th months of age in 68% of cases, while patients without seizures only in 27%. However, coincidence with epilepsy also increased the occurrence of regression before the 18th month of age (23% of patients), while only 4% of patients without epilepsy revealed regression before the 18th month. Epileptic seizures are significantly associated especially with behaviour regression rather than speech regression or regression in both behaviour and speech. Also epileptic seizures diagnosed before correct diagnosis of autism were significantly associated with delayed regression (both behavioural and speech regression).     

Electroencephalographic study of iron-induced chronic focal cortical epilepsy in rat: propagation of cortical epileptic activity to substantia nigra and thalamus.

Cortical epileptic focus was produced by an intracortical injection of FeCl3 in rat cerebral cortex using standard techniques. How after its onset in the cortical focus, the epileptiform activity evolved with time in the thalamus and substantia nigra has been determined. To study the propagation of the epileptiform activity, the local EEG and multiple unit action potentials were recorded from these structures simultaneously with the cortical epileptiform EEG. The results showed that in thalamus and substantia nigra epileptiform activity appeared simultaneously with that in the cortical focus. Intensity of epileptic activity in thalamus and substantia nigra on the whole increased in parallel with that in the cortical focus. The results suggest that the thalamic and nigral epileptiform activity may reinforce the cortical epileptiform activity.     

Spike-induced interference in auditory sensory processing in Landau–Kleffner syndrome

Objectives: Landau–Kleffner Syndrome (LKS) is an epileptic syndrome characterised by a deficit in language comprehension and production, paroxysmal epileptiform activity in the posterior temporal leads, and by the inconsistent presence of epileptic fits. Its interest lies in the fact that it stands as a model for the study of interference of epileptiform activity on cognitive function, although the pathophysiology of the decline in language skills that follows its onset has not yet been clarified.Methods: We have recorded spike-triggered auditory evoked responses in a group of 6 children with LKS, to investigate whether the occurrence of individual EEG paroxysms is able per se to induce a decline in the response of the auditory cortex.Results: Results have indicated that left hemisphere spikes are associated with a greater reduction in amplitude and an increase in latency of the N1, than spikes occurring in the right hemisphere. No stable change in the evoked response has been detected outside of the EEG paroxysm.Conclusions: We postulate EEG interictal activity is able to induce impairment in processing auditory information and that this may play a role in the pathogenesis of language deficit (deficiency?) in LKS.     

Epileptic seizures may begin hours in advance of clinical onset: a report of five patients

Mechanisms underlying seizure generation are traditionally thought to act over seconds to minutes before clinical seizure onset. We analyzed continuous 3- to 14-day intracranial EEG recordings from five patients with mesial temporal lobe epilepsy obtained during evaluation for epilepsy surgery. We found localized quantitative EEG changes identifying prolonged bursts of complex epileptiform discharges that became more prevalent 7 hr before seizures and highly localized subclinical seizure-like activity that became more frequent 2 hr prior to seizure onset. Accumulated energy increased in the 50 min before seizure onset, compared to baseline. These observations, from a small number of patients, suggest that epileptic seizures may begin as a cascade of electrophysiological events that evolve over hours and that quantitative measures of preseizure electrical activity could possibly be used to predict seizures far in advance of clinical onset.     

In vivo optical mapping of epileptic foci and surround inhibition in ferret cerebral cortex

The population of neurons participating in an epileptiform event varies from moment to moment. Most techniques currently used to localize epileptiform events in vivo have spatial and/or temporal sampling limitations. Here we show in an animal model that optical imaging based on intrinsic signals is an excellent method for in vivo mapping of clinically relevant epileptiform events, such as interictal spikes, ictal onsets, ictal spread and secondary homotopic foci. In addition, a decrease in the optical signal correlates spatially with a decrease in neuronal activity recorded from cortex surrounding an epileptic focus. Optical mapping of epilepsy might be a useful adjunct in the surgical treatment of neocortical epilepsy, which critically depends on the precise localization of intrinsically epileptogenic neurons.     

Analysis of the electroencephalographic activity associated with thalamic tumors

A physiologically based model of corticothalamic dynamics is used to investigate the electroencephalographic (EEG) activity associated with tumors of the thalamus. Tumor activity is modeled by introducing localized two-dimensional spatial non-uniformities into the model parameters, and calculating the resulting activity via the coupling of spatial eigenmodes. The model is able to reproduce various qualitative features typical of waking eyes-closed EEGs in the presence of a thalamic tumor, such as the appearance of abnormal peaks at theta ( approximately 3Hz) and spindle ( approximately 12Hz) frequencies, the attenuation of normal eyes-closed background rhythms, and the onset of epileptic activity, as well as the relatively normal EEGs often observed. The results indicate that the abnormal activity at theta and spindle frequencies arises when a small portion of the brain is forced into an over-inhibited state due to the tumor, in which there is an increase in the firing of (inhibitory) thalamic reticular neurons. The effect is heightened when there is a concurrent decrease in the firing of (excitatory) thalamic relay neurons, which are in any case inhibited by the reticular ones. This is likely due to a decrease in the responsiveness of the peritumoral region to cholinergic inputs from the brainstem, and a corresponding depolarization of thalamic reticular neurons, and hyperpolarization of thalamic relay neurons, similar to the mechanism active during slow-wave sleep. The results indicate that disruption of normal thalamic activity is essential to generate these spectral peaks. Furthermore, the present work indicates that high-voltage and epileptiform EEGs are caused by a tumor-induced local over-excitation of the thalamus, which propagates to the cortex. Experimental findings relating to local over-inhibition and over-excitation are discussed. It is also confirmed that increasing the size of the tumor leads to greater abnormalities in the observable EEG. The usefulness of EEG for localizing the tumor is investigated.     

Localizational concepts in epilepsy: past, present and future

The clinical seizure pattern, particularly the initial phenomena, plus the EEG, when satisfactory recording of the seizure onset can be achieved, determine the primary localization of epileptic phenomena. The EEG has also demonstrated, by the presence of interictal epileptiform spike discharges, the presence of a second-order localization of epileptic phenomena, namely, the location and extent of cortex adjacent to the site of origin of the neuronal seizure discharge that is recruited into action in a clinical epileptic seizure. Experience with cortical resection in the treatment of focal epilepsy has demonstrated the importance of a third-order localization of epileptic phenomena, namely, how much of the potentially epileptogenic cortex must be excised in order to produce a satisfactory reduction of the seizure tendency.     

Improved Diagnosis in Children with Partial Epilepsy Using a Multivariable Prediction Model Based on EEG Network Characteristics

Background

Electroencephalogram (EEG) acquisition is routinely performed to support an epileptic origin of paroxysmal events in patients referred with a possible diagnosis of epilepsy. However, in children with partial epilepsies the interictal EEGs are often normal. We aimed to develop a multivariable diagnostic prediction model based on electroencephalogram functional network characteristics.

Methodology/Principal Findings

Routinely performed interictal EEG recordings at first presentation of 35 children diagnosed with partial epilepsies, and of 35 children in whom the diagnosis epilepsy was excluded (control group), were used to develop the prediction model. Children with partial epilepsy were individually matched on age and gender with children from the control group. Periods of resting-state EEG, free of abnormal slowing or epileptiform activity, were selected to construct functional networks of correlated activity. We calculated multiple network characteristics previously used in functional network epilepsy studies and used these measures to build a robust, decision tree based, prediction model. Based on epileptiform EEG activity only, EEG results supported the diagnosis of with a sensitivity and specificity of 0.77 and 0.91 respectively. In contrast, the prediction model had a sensitivity of 0.96 [95% confidence interval: 0.78–1.00] and specificity of 0.95 [95% confidence interval: 0.76–1.00] in correctly differentiating patients from controls. The overall discriminative power, quantified as the area under the receiver operating characteristic curve, was 0.89, defined as an excellent model performance. The need of a multivariable network analysis to improve diagnostic accuracy was emphasized by the lack of discriminatory power using single network characteristics or EEG''s power spectral density.

Conclusions/Significance

Diagnostic accuracy in children with partial epilepsy is substantially improved with a model combining functional network characteristics derived from multi-channel electroencephalogram recordings. Early and accurate diagnosis is important to start necessary treatment as soon as possible and inform patients and parents on possible risks and psychosocial aspects in relation to the diagnosis.     

Electroencephalographic study of the effect of neurotoxin DSP-4 in iron model of chronic focal epilepsy.

The effect of the noradrenergic neurotoxin N-(2-chloroethyl)-N-ethyl-2-bromobenzylamine (DSP-4) on electroencephalographic activity (EEG) was studied in the model of chronic focal epilepsy induced by intracortical injection of FeCl3 in the rat. EEG activity was recorded from the epileptogenic focus (ipsilateral and contralateral) in chronic experiments before and after DSP-4 treatment. In some experiments EEG activity was also simultaneously recorded from the cortical epileptogenic focus and locus coeruleus before and after DSP-4 treatment to study the effect of iron-induced seizure activity and of DSP-4 on the locus coeruleus electrical activity. The results showed that DSP-4 aggravated the iron-induced epileptiform activity as well as the locus-coeruleus electrical activity. The data also showed that, induction of epilepsy by FeCl3 is accompanied by enhancement of the locus coeruleus electrical activity. Our study demonstrates that DSP-4 intensifies and modifies the epileptic activity in the iron-induced chronic epilepsy model and that the effects of toxin persist for a longer duration.     

Calcium oscillations in neocortical astrocytes under epileptiform conditions.

Morphological and functional alterations in astrocytic glia are often found in epileptic syndromes, although the exact role of astrocytes in epilepsy is poorly understood. During calcium imaging of epileptiform events in juvenile neocortical slices we previously discovered cells with spontaneous oscillations in their intracellular free calcium concentration ([Ca(2+)](i)). We have now characterized these oscillations using two in vitro models of epilepsy and find that they are produced by astrocytes. Astrocytic oscillations are widespread throughout the imaged territories, are remarkably regular and have long periods, averaging 100 s, which become shorter during development. Astrocytic oscillations are uncorrelated among themselves and with epileptiform events, are blocked by internal release antagonists and are stimulated by caffeine. Astrocytic calcium oscillations could mediate reactive astrogliosis, contribute to the pathogenesis of chronic epileptic syndromes, and be used as a diagnostic test for epileptic tissue.     

Automatic EEG arousal detection for sleep apnea syndrome

Electroencephalographic (EEG) arousals are seen in EEG recordings as an awakening response of the human brain. Sleep apnea is a serious sleep disorder. Severe sleep apnea brings about EEG arousals and sleep for patients with sleep apnea syndrome (SAS) is thus frequently interrupted. The number of respiratory-related arousals during the whole night on PSG recordings is directly related to the quality of sleep. Detecting EEG arousals in the PSG record is thus a significant task for clinical diagnosis in sleep medicine. In this paper, a method for automatic detection of EEG arousals in SAS patients was proposed. To effectively detect respiratory-related arousals, threshold values were determined according to pathological events as sleep apnea and electromyogram (EMG). If resumption of ventilation (end of the apnea interval) was detected, much lower thresholds were adopted for detecting EEG arousals, including relatively doubtful arousals. Conversely, threshold was maintained high when pathological events were undetected. The proposed method was applied to polysomnographic (PSG) records of eight patients with SAS and accuracy of EEG arousal detection was verified by comparative visual inspection. Effectiveness of the proposed method in clinical diagnosis was also investigated.     

Calcium oscillations in neocortical astrocytes under epileptiform conditions

Morphological and functional alterations in astrocytic glia are often found in epileptic syndromes, although the exact role of astrocytes in epilepsy is poorly understood. During calcium imaging of epileptiform events in juvenile neocortical slices we previously discovered cells with spontaneous oscillations in their intracellular free calcium concentration ([Ca²⁺]_i). We have now characterized these oscillations using two in vitro models of epilepsy and find that they are produced by astrocytes. Astrocytic oscillations are widespread throughout the imaged territories, are remarkably regular and have long periods, averaging 100 s, which become shorter during development. Astrocytic oscillations are uncorrelated among themselves and with epileptiform events, are blocked by internal release antagonists and are stimulated by caffeine. Astrocytic calcium oscillations could mediate reactive astrogliosis, contribute to the pathogenesis of chronic epileptic syndromes, and be used as a diagnostic test for epileptic tissue. © 2002 Wiley Periodicals, Inc. J Neurobiol 50: 45–55, 2002     

A New Method for Nonlinear Dynamic Analysis of the Multi-kinetics Neural Mass Model

ObjectiveThe aim was to use a new method to analyze the nonlinear dynamic characteristics of the multi-kinetics neural mass model. We hope that this new method can be as an auxiliary judgment tool for the diagnosis of brain diseases and the identification of brain activity states.MethodsWe apply the Lorenz plot to analyze the nonlinear dynamic characteristics of electroencephalogram (EEG) signals from the multi-kinetics neural mass models. The standard deviations in two orthogonal directions of the Lorenz plot are further used to quantify the nonlinear dynamic characteristics of EEG signals.ResultsThe results show that the normalized signal frequency power spectrum may not be able to distinguish normal EEG signals and epileptiform spikes, but the Lorenz plot can distinguish the normal EEG signals and epileptiform spikes effectively. For EEG signals with multi-rhythms, the Lorenz plot of all the simulated signals are oval, but the value of SD1/SD2 increases monotonically when the multi-rhythm EEG signals change from low frequency to high frequency.ConclusionThe Lorenz plot of EEG signals with different rhythms presents different distribution. It is an effective nonlinear analysis method for EEG signals.     

三种抗癫痫药对癫痫患者脑电图的背景影响比较研究

目的：研究对比三种抗癫痫药（苯妥因钠、丙戊酸钠、卡马西平）对癫痫患者脑电图的背景影响。方法：选取我院于2009年3月至2011年2月收治的60例癫痫患者,随机分为苯妥因钠（PHT）、卡马西平（CBZ）和丙戊酸钠（SVP）组各20例,动态观察各组患者于治疗期间痫样波放电的频度和EEG背景的变化。结果：EEG痫样波放电的抑制率以SVP最为明显,而CBZ在EEG背景活动影响方面均比其他两组显著。结论：三种药物对癫痫波放电的抑制顺序是SVP〉PHT〉CBZ,SVP组明显优于其他两组。     

Estimating short-run and long-run interaction mechanisms in interictal state

We address the issue of analyzing electroencephalogram (EEG) from seizure patients in order to test, model and determine the statistical properties that distinguish between EEG states (interictal, pre-ictal, ictal) by introducing a new class of time series analysis methods. In the present study: firstly, we employ statistical methods to determine the non-stationary behavior of focal interictal epileptiform series within very short time intervals; secondly, for such intervals that are deemed non-stationary we suggest the concept of Autoregressive Integrated Moving Average (ARIMA) process modelling, well known in time series analysis. We finally address the queries of causal relationships between epileptic states and between brain areas during epileptiform activity. We estimate the interaction between different EEG series (channels) in short time intervals by performing Granger-causality analysis and also estimate such interaction in long time intervals by employing Cointegration analysis, both analysis methods are well-known in econometrics. Here we find: first, that the causal relationship between neuronal assemblies can be identified according to the duration and the direction of their possible mutual influences; second, that although the estimated bidirectional causality in short time intervals yields that the neuronal ensembles positively affect each other, in long time intervals neither of them is affected (increasing amplitudes) from this relationship. Moreover, Cointegration analysis of the EEG series enables us to identify whether there is a causal link from the interictal state to ictal state.     

视频脑电图鉴别诊断癫痫患者睡眠障碍、认知障碍的临床价值研究

目的:探讨视频脑电图诊断癫痫患者睡眠障碍、认知障碍的临床价值。方法:选取2014年1月~2016年12月在我院神经内科进行诊治的癫痫患者236例作为癫痫组,另选取同期的健康患者家属或者其他健康体检者236例作为正常对照组,对两组进行视频脑电图联合睡眠参数分析;并对癫痫组视频脑电图联合认知参数进行分析。结果:癫痫组睡眠Ⅰ~Ⅱ期时间显著长于正常对照组且具有统计学差异(P=0.000),睡眠Ⅲ~Ⅳ期时间显著短于正常对照组且具有统计学差异(P=0.000),睡眠时相转换频率、觉醒指数均显著高于正常对照组且均具有统计学差异(P=0.000);清醒期、睡眠期不同痫样放电指数(IED)的WAIS-RC IQ和WMS-RC MQ均具有统计学差异(P0.05),10%IED≤50%者的WAIS-RC IQ和WMS-RC MQ均显著低于1%IED≤10%者且均具有统计学差异(P0.05),IED 10%可能是痫样放电影响患者认知功能的最低阈值。结论:视频脑电图在癫痫患者睡眠障碍、认知障碍识别中具有重要的临床价值。     

Localizing confined epileptic foci in patients with an unclear focus or presumed multifocality using a component-based EEG-fMRI method

Precise localization of epileptic foci is an unavoidable prerequisite in epilepsy surgery. Simultaneous EEG-fMRI recording has recently created new horizons to locate foci in patients with epilepsy and, in comparison with single-modality methods, has yielded more promising results although it is still subject to limitations such as lack of access to information between interictal events. This study assesses its potential added value in the presurgical evaluation of patients with complex source localization. Adult candidates considered ineligible for surgery on account of an unclear focus and/or presumed multifocality on the basis of EEG underwent EEG-fMRI. Adopting a component-based approach, this study attempts to identify the neural behavior of the epileptic generators and detect the components-of-interest which will later be used as input in the GLM model, substituting the classical linear regressor. Twenty-eight sets interictal epileptiform discharges (IED) from nine patients were analyzed. In eight patients, at least one BOLD response was significant, positive and topographically related to the IEDs. These patients were rejected for surgery because of an unclear focus in four, presumed multifocality in three, and a combination of the two conditions in two. Component-based EEG-fMRI improved localization in five out of six patients with unclear foci. In patients with presumed multifocality, component-based EEG-fMRI advocated one of the foci in five patients and confirmed multifocality in one of the patients. In seven patients, component-based EEG-fMRI opened new prospects for surgery and in two of these patients, intracranial EEG supported the EEG-fMRI results. In these complex cases, component-based EEG-fMRI either improved source localization or corroborated a negative decision regarding surgical candidacy. As supported by the statistical findings, the developed EEG-fMRI method leads to a more realistic estimation of localization compared to the conventional EEG-fMRI approach, making it a tool of high value in pre-surgical evaluation of patients with refractory epilepsy. To ensure proper implementation, we have included guidelines for the application of component-based EEG-fMRI in clinical practice.     

Changes in the activity of gamma-amino butyric acid transaminase and succinic semialdehyde dehydrogenase in the cobalt and iron experimental epileptogenic foci in the rat brain

GABA-transaminase (GABA-T) and succinic semialdehyde dehydrogenase (SSA-DH) activities were measured in the mitochondrial fractions from the cobalt- and FeCl3-induced chronic epileptogenic foci in the rat brain. Electroencephalographically, the FeCl3 epileptogenic focus remained active for a duration longer than that of the cobalt focus. In both the foci SSA-DH activity showed significant increases which were concomitant with the EEG epileptiform activity. In cobalt focus, the GABA-T activity fell whereas, in the FeCl3 focus it was unchanged. In cobalt focus fall in GABA-T activity seemed to be concomitant with EEG epileptiform discharge. The measurements of the enzyme activities in the mirror (secondary) foci showed that, except for a brief stimulation of SSA-DH activity in the mirror focus in FeCl3 epileptic animals, the enzyme activities remained unchanged. Possible significance of the observed enzymatic changes in the physiology of epileptogenic focus is discussed.     

EEGgui: a program used to detect electroencephalogram anomalies after traumatic brain injury

Background

Identifying and quantifying pathological changes in brain electrical activity is important for investigations of brain injury and neurological disease. An example is the development of epilepsy, a secondary consequence of traumatic brain injury. While certain epileptiform events can be identified visually from electroencephalographic (EEG) or electrocorticographic (ECoG) records, quantification of these pathological events has proved to be more difficult. In this study we developed MATLAB-based software that would assist detection of pathological brain electrical activity following traumatic brain injury (TBI) and present our MATLAB code used for the analysis of the ECoG.

Methods

Software was developed using MATLAB(?) and features of the open access EEGLAB. EEGgui is a graphical user interface in the MATLAB programming platform that allows scientists who are not proficient in computer programming to perform a number of elaborate analyses on ECoG signals. The different analyses include Power Spectral Density (PSD), Short Time Fourier analysis and Spectral Entropy (SE). ECoG records used for demonstration of this software were derived from rats that had undergone traumatic brain injury one year earlier.

Results

The software provided in this report provides a graphical user interface for displaying ECoG activity and calculating normalized power density using fast fourier transform of the major brain wave frequencies (Delta, Theta, Alpha, Beta1, Beta2 and Gamma). The software further detects events in which power density for these frequency bands exceeds normal ECoG by more than 4 standard deviations. We found that epileptic events could be identified and distinguished from a variety of ECoG phenomena associated with normal changes in behavior. We further found that analysis of spectral entropy was less effective in distinguishing epileptic from normal changes in ECoG activity.

Conclusion

The software presented here was a successful modification of EEGLAB in the Matlab environment that allows detection of epileptiform ECoG signals in animals after TBI. The code allows import of large EEG or ECoG data records as standard text files and uses fast fourier transform as a basis for detection of abnormal events. The software can also be used to monitor injury-induced changes in spectral entropy if required. We hope that the software will be useful for other investigators in the field of traumatic brain injury and will stimulate future advances of quantitative analysis of brain electrical activity after neurological injury or disease.