癫痫病人脑电信号的奇异谱

癫痫是一种常见的神经系统疾患,其唯一客观证据为脑电图的癫痫样发放。在癫痫发作间期,仅有偶发的很难辨别的癫痫样放电,为了正确诊断癫痫病,往往需要医生长时间监测病人的脑电信号,在对脑电信号进行相空间重构,进而对其进行奇异系统分析,发现癫痫病人无论在癫痫发作前、发作中、发作后,其脑电信号的奇异谱曲线不存在噪声平台,明显区别于正常人。是否可以认为脑电信号的奇异谱正代表着大脑的一种基本状态,癫痫患者在未发作时,大脑的基本状态已经处于异常。无论如休,奇异系统分析方法使得可以利用很短的一段脑电数据诊断癫痫。无疑为癫痫病人的临床诊断提供了一条简单、有效的途径。     

基于二阶C0复杂度的癫痫发作预测

癫痫发作的预测是近年来在临床医学和神经系统科学研究领域中备受关注的问题。如果癫痫发作能够被可靠地预测,则可以提前采取有效的临床预防措施,从而能较大程度地改善癫痫患者的生活质量。文章提出了一种基于二阶C0复杂度的预测算法用于预测癫痫发作。该算法通过分析癫痫患者颅内脑电信号的二阶C0复杂度,利用发作前期复杂度曲线的变化特征预测癫痫发作。作者运用该算法对21组癫痫病人87次发作的临床颅内脑电数据和4组大鼠4次发作的颅内脑电数据进行分析计算,预测准确率分别为94.3%和100%。实验结果表明该算法可以有效地预测癫痫发作,具有潜在的重要临床应用价值。     

癫痫发作间期alpha波的窄带相位同步分析

神经元同步化放电是癫痫发作的一个重要特征,作者提出并运用窄带相位同步技术对比分析了54个癫痫病人和10个正常成年人的脑电信号(EEG)数据.结果表明,相对于对照组,癫痫组的Alpha波的平均窄带相位同步值有显著下降(P=0.02058).为了更准确地刻画和衡量癫痫组和其对照组在同步模式上的差异,提出了一个新的具体的量化指标,即alpha波的窄带相位同步发散率.分析结果显示,对照组的窄带相位同步发散率明显低于癫痫组(P=0.003060),说明对照组的alpha波振子间互诱导强度更高.这可能反映了癫痫组窄带相位同步发散率的升高及所代表的alpha振子间互诱导强度的减弱与患者在癫痫发作间期的状态有密切的联系.     

基于时间聚类分析和独立成分分析的癫痫fMRI盲分析方法

提出了一种基于时间聚类分析和独立成分分析的癫痫fMRI数据盲分析方法,并将两种方法有效联合,提取发作间期的癫痫fMRI激活时空信息.该方法首先由时间聚类分析得到与激活相关的时间峰度特征曲线,以此特征作为时间参考信息;再由空间独立成分分析分解fMRI信号得到空间独立成分;最后将每个独立成分所对应的时间曲线与参考曲线做相关分析提取相应脑激活图.提出的方法无需任何关于癫痫fMRI的先验假设信息,有效解决了独立成分的排序问题,实现了对数据的盲分析.仿真试验结果阐明了这一方法的有效性及可靠性,对癫痫数据的试验结果显示空间定位准确性优于统计参数图方法.     

基于EEG信号分析处理的癫痫预测研究

癫痫是神经系统的常见病,特点是大脑神经细胞群反复超同步放电,从而引起突发性的脑功能紊乱.由于癫痫发作具有突然性,发作时患者常伴有意识丧失,癫痫的发作常造成患者意外的伤害.如能对癫痫发作进行有效的预测,就可在发作前对患者采取必要的保护措施,从而减少患者受到伤害的风险.癫痫预测的研究也对认识癫痫的发病机制及开发新的治疗方法提供依据.本文介绍了癫痫发作预测常用的方法,研究现状和存在的问题.     

功率谱熵在痫性发作大鼠脑电检测中的应用研究

目的:研究功率谱熵在痫性发作大鼠脑电检测中的应用。方法:采用青霉素在大鼠海马微注射制备急性痫性发作模型,以深部电极记录大鼠原始脑电信号,将24只SD大鼠随机分成四组,即正常组(A),对照组(B),单电极组(C),多电极组(D)。C、D组大鼠经致痫后观察未发作期、发作前期、发作期和发作后期四期脑电信号的变化,运用谱熵对四期脑电信号进行分析,并与A、B组进行对比。结果:C组和D组脑电功率谱熵显示两组发作期与未发作期、发作前期、发作后期比较有显著差异(P0.05),发作期明显低于其它各期;未发作期和发作前期相比有明显差异(P0.05),发作前期较未发作期降低;将D组大鼠海马致痫灶(a)及其同侧附近(b)、对侧(c)三点发作各期脑电功率谱熵进行对比分析,发作前期和发作期a、b、c三点比较有明显差异(P0.05),a点最低,c点的功率谱熵值最高。结论:功率谱熵可以预报痫性发作并可对癫痫病灶的定位提供一定的帮助。     

基于ReHo方法的颞叶癫痫功能磁共振成像研究

癫痫是一种以神经活动同步性异常增高为特征的中枢神经系统疾病。作者利用基于局域一致性(regional homogeneity,ReHo)分析方法的功能磁共振成像技术对内侧颞叶癫痫(medial temporal lobe epilepsy,mTLE)进行了研究。观察颞叶癫痫病人相比正常对照组局域一致性的改变情况。结果表明:在静息状态下,病人大脑的局域一致性在某些脑区较正常人高,主要集中在海马、丘脑、顶叶;另外在某些脑区的局域一致性较正常人低,主要集中在小脑后叶。提示该方法可检出癫痫活动造成的局部脑组织血氧水平信号同步性的改变,进而达到对癫痫活动的定位检测。     

脑电信号的高阶奇异谱分析

奇异谱分析是脑电信号分析的一种新方法,脑电信号的奇异谱可以反映脑电的特征,它有助于研究大脑的动力学行为。奇异谱分析方法是基于二阶统计的方法,反映的是信号时间上和空间上的一种线性相关关系。而脑电信号属于非线性信号,其内在的非线性关系很难通过奇异谱得到真实的反映,从而会丢失某些有用的信息。提出一种新的基于高阶统计的脑电奇异谱分析方法,并将其运用于正常脑电和癫痫患者的脑电分析中。大量的实测信号样本仿真实验结果表明,正常脑电和癫痫脑电的奇异谱有明显的不同。此外,基于高阶统计的奇异谱和基于二阶统计的奇异谱相比更能反映出信号的细节。     

妊娠期抗癫痫药物的药代动力学变化及胎盘转运特征

癫痫是一种常见的神经系统慢性疾病,多数患者妊娠期需继续应用抗癫痫药物(AEDs)治疗,以控制癫痫发作。但妊娠期妇女体 内一系列生理变化可改变 AEDs 的药代动力学行为,导致癫痫发作并危及胎儿的生长发育。基于此,综述妊娠期 AEDs 的药代动力学变化 及胎盘转运特征,为妊娠期癫痫患者的精准合理用药提供参考。     

膜超极化激活离子通道及其靶向抗癫痫药物研究进展

癫痫是一种较为常见的神经系统疾病,主要以大量神经元同步异常放电为特征。目前普遍认为,神经元或神经网络兴奋性和抑制性 电信号传输的失衡,是癫痫发病的最根本原因。现有的抗癫痫药物主要以钠离子通道、钙离子通道、钾离子通道、谷氨酸受体和γ-氨基丁 酸离子通道为靶点,但接受这些药物治疗后,仍有近1/3的病人无法控制癫痫发作。因此,抗癫痫药物的研发亟需新靶点和新思路。许多 研究证据表明,膜超极化激活离子通道的基因突变可以导致遗传型癫痫的发作,且在脑部损伤后,膜超极化激活离子通道会发生表达水平、 通道生物物理学性质及通道亚基构成的改变,从而增加神经元和神经网络兴奋性,促使癫痫发病。故近年来,膜超极化激活离子通道及其 靶向抗癫痫药物研究引起人们广泛关注。综述膜超极化激活离子通道与癫痫发病之间的关系,并探讨以膜超极化激活离子通道为靶点进行 抗癫痫药物开发和治疗的可行性。     

Complexity of Multi-Channel Electroencephalogram Signal Analysis in Childhood Absence Epilepsy

Absence epilepsy is an important epileptic syndrome in children. Multiscale entropy (MSE), an entropy-based method to measure dynamic complexity at multiple temporal scales, is helpful to disclose the information of brain connectivity. This study investigated the complexity of electroencephalogram (EEG) signals using MSE in children with absence epilepsy. In this research, EEG signals from 19 channels of the entire brain in 21 children aged 5-12 years with absence epilepsy were analyzed. The EEG signals of pre-ictal (before seizure) and ictal states (during seizure) were analyzed by sample entropy (SamEn) and MSE methods. Variations of complexity index (CI), which was calculated from MSE, from the pre-ictal to the ictal states were also analyzed. The entropy values in the pre-ictal state were significantly higher than those in the ictal state. The MSE revealed more differences in analysis compared to the SamEn. The occurrence of absence seizures decreased the CI in all channels. Changes in CI were also significantly greater in the frontal and central parts of the brain, indicating fronto-central cortical involvement of “cortico-thalamo-cortical network” in the occurrence of generalized spike and wave discharges during absence seizures. Moreover, higher sampling frequency was more sensitive in detecting functional changes in the ictal state. There was significantly higher correlation in ictal states in the same patient in different seizures but there were great differences in CI among different patients, indicating that CI changes were consistent in different absence seizures in the same patient but not from patient to patient. This implies that the brain stays in a homogeneous activation state during the absence seizures. In conclusion, MSE analysis is better than SamEn analysis to analyze complexity of EEG, and CI can be used to investigate the functional brain changes during absence seizures.     

Forecasting Seizures in Dogs with Naturally Occurring Epilepsy

Seizure forecasting has the potential to create new therapeutic strategies for epilepsy, such as providing patient warnings and delivering preemptive therapy. Progress on seizure forecasting, however, has been hindered by lack of sufficient data to rigorously evaluate the hypothesis that seizures are preceded by physiological changes, and are not simply random events. We investigated seizure forecasting in three dogs with naturally occurring focal epilepsy implanted with a device recording continuous intracranial EEG (iEEG). The iEEG spectral power in six frequency bands: delta (0.1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–30 Hz), low-gamma (30–70 Hz), and high-gamma (70–180 Hz), were used as features. Logistic regression classifiers were trained to discriminate labeled pre-ictal and inter-ictal data segments using combinations of the band spectral power features. Performance was assessed on separate test data sets via 10-fold cross-validation. A total of 125 spontaneous seizures were detected in continuous iEEG recordings spanning 6.5 to 15 months from 3 dogs. When considering all seizures, the seizure forecasting algorithm performed significantly better than a Poisson-model chance predictor constrained to have the same time in warning for all 3 dogs over a range of total warning times. Seizure clusters were observed in all 3 dogs, and when the effect of seizure clusters was decreased by considering the subset of seizures separated by at least 4 hours, the forecasting performance remained better than chance for a subset of algorithm parameters. These results demonstrate that seizures in canine epilepsy are not randomly occurring events, and highlight the feasibility of long-term seizure forecasting using iEEG monitoring.     

Stability of synchronization clusters and seizurability in temporal lobe epilepsy

Purpose

Identification of critical areas in presurgical evaluations of patients with temporal lobe epilepsy is the most important step prior to resection. According to the “epileptic focus model”, localization of seizure onset zones is the main task to be accomplished. Nevertheless, a significant minority of epileptic patients continue to experience seizures after surgery (even when the focus is correctly located), an observation that is difficult to explain under this approach. However, if attention is shifted from a specific cortical location toward the network properties themselves, then the epileptic network model does allow us to explain unsuccessful surgical outcomes.

Methods

The intraoperative electrocorticography records of 20 patients with temporal lobe epilepsy were analyzed in search of interictal synchronization clusters. Synchronization was analyzed, and the stability of highly synchronized areas was quantified. Surrogate data were constructed and used to statistically validate the results. Our results show the existence of highly localized and stable synchronization areas in both the lateral and the mesial areas of the temporal lobe ipsilateral to the clinical seizures. Synchronization areas seem to play a central role in the capacity of the epileptic network to generate clinical seizures. Resection of stable synchronization areas is associated with elimination of seizures; nonresection of synchronization clusters is associated with the persistence of seizures after surgery.

Discussion

We suggest that synchronization clusters and their stability play a central role in the epileptic network, favoring seizure onset and propagation. We further speculate that the stability distribution of these synchronization areas would differentiate normal from pathologic cases.     

Epilepsy and Nonlinear Dynamics

This overview summarizes findings obtained from analyzing electroencephalographic (EEG) recordings from epilepsy patients with methods from the theory of nonlinear dynamical systems. The last two decades have shown that nonlinear time series analysis techniques allow an improved characterization of epileptic brain states and help to gain deeper insights into the spatial and temporal dynamics of the epileptic process. Nonlinear EEG analyses can help to improve the evaluation of patients prior to neurosurgery, and with an unequivocal identification of precursors of seizures, they can be of great value in the development of seizure warning and prevention techniques.     

Patient-Specific Epileptic Seizure Prediction in Long-Term Scalp EEG Signal Using Multivariate Statistical Process Control

An accurate epileptic seizure prediction algorithm can alleviate the problem and reduce risks in the life of a patient suffering from epilepsy. The main motive of this work is to propose a model which can predict seizures well in advance of its occurrence. Multivariate statistical process control (MSPC) has been used for seizure predictions in long-term scalp EEG signal. It has been observed that excessive neuronal activity in the preictal period of seizure changes the electrical characteristic from chaotic to rhythmic behavior. These changes have been utilized for prediction. Eight temporal based features are used for predicting the seizures by using multivariate statistical process control, which is widely known as an anomaly monitoring method. 90 seizures from the CHB-MIT EEG data of ten patients are analyzed.ResultThe results of the proposed method demonstrated that 80 seizures out of 90 in preictal period were correctly predicted prior to the seizure onset, thereby giving a sensitivity of 88.89%. The false positive rate is observed to 0.39 per hour.ConclusionThis study proposed a temporal based patient-specific epileptic seizure prediction method using MSPC in long-term scalp EEG signals. It also provides the possibility of realizing an EEG-based epileptic seizure prediction system which requires less computational power.SignificanceThe proposed method does not require preictal data for modeling. The extracted features are computationally easy. The tested result shows good accuracy on the CHB-MIT data base.     

Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier

Electroencephalogram shortly termed as EEG is considered as the fundamental segment for the assessment of the neural activities in the brain. In cognitive neuroscience domain, EEG-based assessment method is found to be superior due to its non-invasive ability to detect deep brain structure while exhibiting superior spatial resolutions. Especially for studying the neurodynamic behavior of epileptic seizures, EEG recordings reflect the neuronal activity of the brain and thus provide required clinical diagnostic information for the neurologist. This specific proposed study makes use of wavelet packet based log and norm entropies with a recurrent Elman neural network (REN) for the automated detection of epileptic seizures. Three conditions, normal, pre-ictal and epileptic EEG recordings were considered for the proposed study. An adaptive Weiner filter was initially applied to remove the power line noise of 50 Hz from raw EEG recordings. Raw EEGs were segmented into 1 s patterns to ensure stationarity of the signal. Then wavelet packet using Haar wavelet with a five level decomposition was introduced and two entropies, log and norm were estimated and were applied to REN classifier to perform binary classification. The non-linear Wilcoxon statistical test was applied to observe the variation in the features under these conditions. The effect of log energy entropy (without wavelets) was also studied. It was found from the simulation results that the wavelet packet log entropy with REN classifier yielded a classification accuracy of 99.70 % for normal-pre-ictal, 99.70 % for normal-epileptic and 99.85 % for pre-ictal-epileptic.     

Synchronization regulation in a model of coupled neural masses

A model of coupled neural masses can generate seizure-like events and dynamics similar to those observed during interictal to ictal transitions and thus can be used for theoretical study of the control of epileptic seizures. In an effort to understand the mechanisms underlying epileptic seizures and how to avoid them, we added a control input to this model. Epileptic seizures are always accompanied by hypersynchronous firing of neurons, so research on synchronization among cortical areas is significant for seizure control. In this study, principal component analysis (PCA) was used to identify synchronization clusters composed of several neural masses. A method for calculating the synchronization cluster strength and participation rate is presented. The synchronization cluster strength can be used to identify synchronization clusters and the participation rate can be employed to identify neural masses that participate in the clusters. Each synchronization cluster is controlled as a whole using a proportional-integral-derivative (PID) controller. We illustrate these points using coupled neural mass models of synchronization to show their responses to increased (between node) coupling with and without control. Experiment results indicated that PID control can effectively regulate synchronization between neural masses and has the potential for seizure prevention.     

Extraction of reproducible seizure patterns based on EEG scalp correlations

The objective of this work is to identify similarities in the spatio-temporal dynamics of epileptic seizures, record with scalp EEG. A comprehensive method is proposed and applied in EEG of the patients who suffer from temporal lobe epilepsy. The method is based on the computation of the time-varying degree of non linear correlation between scalp electrodes at seizure onset and during seizure spread, determined by a nonlinear regression analysis. The quantification and coding of these similarity relations allow the comparison between two epileptic networks. Results show that reproducible patterns may be extracted from different seizures of the same patient and confirm the existence of different subtypes of temporal lobe epilepsy.     

Dynamic Imaging of Coherent Sources Reveals Different Network Connectivity Underlying the Generation and Perpetuation of Epileptic Seizures

The concept of focal epilepsies includes a seizure origin in brain regions with hyper synchronous activity (epileptogenic zone and seizure onset zone) and a complex epileptic network of different brain areas involved in the generation, propagation, and modulation of seizures. The purpose of this work was to study functional and effective connectivity between regions involved in networks of epileptic seizures. The beginning and middle part of focal seizures from ictal surface EEG data were analyzed using dynamic imaging of coherent sources (DICS), an inverse solution in the frequency domain which describes neuronal networks and coherences of oscillatory brain activities. The information flow (effective connectivity) between coherent sources was investigated using the renormalized partial directed coherence (RPDC) method. In 8/11 patients, the first and second source of epileptic activity as found by DICS were concordant with the operative resection site; these patients became seizure free after epilepsy surgery. In the remaining 3 patients, the results of DICS / RPDC calculations and the resection site were discordant; these patients had a poorer post-operative outcome. The first sources as found by DICS were located predominantly in cortical structures; subsequent sources included some subcortical structures: thalamus, Nucl. Subthalamicus and cerebellum. DICS seems to be a powerful tool to define the seizure onset zone and the epileptic networks involved. Seizure generation seems to be related to the propagation of epileptic activity from the primary source in the seizure onset zone, and maintenance of seizures is attributed to the perpetuation of epileptic activity between nodes in the epileptic network. Despite of these promising results, this proof of principle study needs further confirmation prior to the use of the described methods in the clinical praxis.