Grammatical Evolution for Features of Epileptic Oscillations in Clinical Intracranial Electroencephalograms

This paper presents grammatical evolution (GE) as an approach to select and combine features for detecting epileptic oscillations within clinical intracranial electroencephalogram (iEEG) recordings of patients with epilepsy. Clinical iEEG is used in preoperative evaluations of a patient who may have surgery to treat epileptic seizures. Literature suggests that pathological oscillations may indicate the region(s) of brain that cause epileptic seizures, which could be surgically removed for therapy. If this presumption is true, then the effectiveness of surgical treatment could depend on the effectiveness in pinpointing critically diseased brain, which in turn depends on the most accurate detection of pathological oscillations. Moreover, the accuracy of detecting pathological oscillations depends greatly on the selected feature(s) that must objectively distinguish epileptic events from average activity, a task that visual review is inevitably too subjective and insufficient to resolve. Consequently, this work suggests an automated algorithm that incorporates grammatical evolution (GE) to construct the most sufficient feature(s) to detect epileptic oscillations within the iEEG of a patient. We estimate the performance of GE relative to three alternative methods of selecting or combining features that distinguish an epileptic gamma (~65-95 Hz) oscillation from normal activity: forward sequential feature-selection, backward sequential feature-selection, and genetic programming. We demonstrate that a detector with a grammatically evolved feature exhibits a sensitivity and selectivity that is comparable to a previous detector with a genetically programmed feature, making GE a useful alternative to designing detectors.     

A feedback control systems view of epileptic seizures

To understand basic functional mechanisms that cause epileptic seizures, the paper discusses some key features of theoretical brain functioning models. The hypothesis is put forward that a plausible reason for seizures is pathological feedback in brain circuitry. The analysis of such circuitry has an interesting physical interpretation and may be used to cure epilepsy. The paper is dedicated to the 70th birthday of Academician I. V. Sergienko. Published in Kibernetika i Sistemnyi Analiz, No. 4, pp. 26–40, July–August 2006.     

癫痫脑电的递归确定性分析

利用排序递归图的分析方法对癫痫脑电进行了确定性(DET)的分析,得出癫痫头皮脑电(EEG)的DET高于健康EEG。DET特征的差异性在局部导联上更明显,局部导联的DET特征可以作为癫痫疾病的自动诊断特征。通过分析发作阶段和发作间隙皮层脑电(ECoG)的DET,得出整个频带的DET差别不大,而在beta频带,发作阶段的确定性明显高于发作间隙的DET。Beta频带的DET特征可以作为癫痫发作的预测特征。研究结果为癫痫疾病的自动诊断和癫痫发作预测提供了理论依据。     

Combined neural network model employing wavelet coefficients for EEG signals classification

This paper illustrates the use of combined neural network model to guide model selection for classification of electroencephalogram (EEG) signals. The EEG signals were decomposed into time–frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The first-level networks were implemented for the EEG signals classification using the statistical features as inputs. To improve diagnostic accuracy, the second-level networks were trained using the outputs of the first-level networks as input data. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified with the accuracy of 94.83% by the combined neural network. The combined neural network model achieved accuracy rates which were higher than that of the stand-alone neural network model.     

Application of particle swarm optimization in epileptic spike EEG source localization

Surgical therapy has become an important therapeutic alternative for patients with medically intractable epilepsy. Correct and anatomically precise localization of an epileptic focus is essential to decide if resection of brain tissue is possible. The inverse problem in EEG-based source localization is to determine the location of the brain sources that are responsible for the measured potentials at the scalp electrodes. We propose a new global optimization method based on particle swarm optimization (PSO) to solve the epileptic spike EEG source localization inverse problem. In a forward problem a modified subtraction method is proposed to reduce the computational time. The good accuracy and fast convergence are demonstrated for 2D and 3D cases with realistic head models. The results from the new method are promising for use in the pre-surgical clinic in the future.     

癫痫脑电的互信息和同步性分析

脑电（EEG）分析是研究癫痫的一个重要手段。以临床采集的健康对象和癫痫患者的头皮EEG为研究对象,计算不同导联EEG数据之间的排序互信息,结果表明癫痫患者不同导联之间的互信息明显高于健康对象,因此,排序互信息可以作为癫痫疾病诊断的重要特征。以排序互信息为依据,对癫痫脑电进行了同步性的分析,结果表明癫痫患者左脑区域内、右脑区域内及左右脑区之间的信息交流明显增强,即其同步性强于健康对象。互信息和同步性的分析方法还可对癫痫发作前期和发作阶段的EEG进行分析,对癫痫发作作出预测。     

基于排列熵和支持向量机的癫痫发作预测研究

针对癫痫发作给病人带来的巨大伤害,为临床治疗留下足够空余时间,提出一个可以预测癫痫发作的系统模型。对21名癫痫病人进行研究,提取具有较低算法复杂度的排列熵构成特征向量,将其输入支持向量机（support vector machine,SVM）训练出学习模型,用来识别发作期样本,利用投票机制充分考虑病人差异来判断所处状态,最终实现癫痫的实时预测。结果表明,其中81%的发作可以提前平均50多分钟预测到,且具有较低的误报率。为癫痫发作预测系统的理论研究打下坚实基础。     

Analysis of delay-induced basal ganglia oscillations: the role of external excitatory nuclei

Basal ganglia are interconnected deep brain structures involved in movement generation. Their persistent beta-band oscillations (13–30 Hz) are known to be linked to Parkinson’s disease motor symptoms. In this paper, we provide conditions under which these oscillations may occur, by explicitly considering the role of the pedunculopontine nucleus (PPN). We analyse the existence of equilibria in the associated firing-rate dynamics and study their stability by relying on a delayed multiple-input/multiple-output (MIMO) frequency analysis. Our analysis suggests that the PPN has an influence on the generation of pathological beta-band oscillations. These results are illustrated by simulations that confirm numerically the analytic predictions of our two main theorems.     

Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients

The ability of computational intelligence methods to predict epileptic seizures is evaluated in long-term EEG recordings of 278 patients suffering from pharmaco-resistant partial epilepsy, also known as refractory epilepsy. This extensive study in seizure prediction considers the 278 patients from the European Epilepsy Database, collected in three epilepsy centres: Hôpital Pitié-là-Salpêtrière, Paris, France; Universitätsklinikum Freiburg, Germany; Centro Hospitalar e Universitário de Coimbra, Portugal.     

Determination of ECoG information flow activity based on Granger causality and Hilbert transformation

Analysis of directional information flow patterns among different regions of the brain is important for investigating the relation between ECoG (electrocorticographic) and mental activity. The objective is to study and evaluate the information flow activity at different frequencies in the primary motor cortex. We employed Granger causality for capturing the future state of the propagation path and direction between recording electrode sites on the cerebral cortex. A grid covered the right motor cortex completely due to its size (approx. 8 cm × 8 cm) but grid area extends to the surrounding cortex areas. During the experiment, a subject was asked to imagine performing two activities: movement of the left small finger and/or movement of the tongue. The time series of the electrical brain activity was recorded during these trials using an 8 × 8 (0.016–300 Hz band with) ECoG platinum electrode grid, which was placed on the contralateral (right) motor cortex. For detection of information flow activity and communication frequencies among the electrodes, we have proposed a method based on following steps: (i) calculation of analytical time series such as amplitude and phase difference acquired from Hilbert transformation, (ii) selection of frequency having highest interdependence for the electrode pairs for the concerned time series over a sliding window in which we assumed time series were stationary, (iii) calculation of Granger causality values for each pair with selected frequency. The information flow (causal influence) activity and communication frequencies between the electrodes in grid were determined and shown successfully. It is supposed that information flow activity and communication frequencies between the electrodes in the grid are approximately the same for the same pattern. The successful employment of Granger causality and Hilbert transformation for the detection of the propagation path and direction of each component of ECoG among different sub-cortex areas were capable of determining the information flow (causal influence) activity and communication frequencies between the populations of neurons successfully.     

基于功率谱及有限穿越可视图的癫痫脑电信号分析算法

针对可视图（VG）算法存在噪声鲁棒性差的问题,提出一种改进的有限穿越可视图（LPVG）建网方法。该算法基于可视图（VG）算法的可视性准则,并设定有限穿越视距,将时间序列中满足条件的点连接起来,从而将时间序列映射为网络。首先,对LPVG算法进行性能分析;然后,将LPVG算法结合功率谱密度（PSD）算法应用到癫痫发作前、中、后脑电信号的识别上;最后,提取三种状态下癫痫脑电信号的LPVG网络特征参数,研究癫痫对网络拓扑结构的影响。仿真结果表明,与VG和水平穿越可视图（HVG）相比,虽然LPVG算法的时间复杂度较高,但是LPVG对信号中的噪声具有较强的鲁棒性：分别对周期、随机、分形和混沌四种时间序列进行LPVG建网,发现随着噪声强度增大,LPVG网络聚类系数的波动率均为最低,分别为6.73%、0.05%、0.99%和3.20%。接下来对脑电信号的PSD和LPVG建网分析结果表明,癫痫发作中,PSD值在delta频带下显著增强,而在theta频带下显著降低;LPVG网络拓扑结构有所改变,网络中各模块的独立性有所提高,网络的平均路径长度增大,复杂度降低。所提的功率谱密度和有限穿越可视图算法能够有效表征癫痫前、中、后三种状态下的脑电信号能量分布和单通道信号可视化后的网络拓扑结构的异常,为癫痫的病理研究和临床诊断提供帮助。     

Clustering of entropy topography in epileptic electroencephalography

Epileptic seizures have been considered sudden and unpredictable events for centuries. A seizure seems to occur when a massive group of neurons in the cerebral cortex begins to discharge in a highly organized rhythmic pattern, then it develops according to some poorly described dynamics. As proved by the results reported by different research groups, seizures appear not completely random and unpredictable events. Thus, it is reasonable to wonder when, where and why the epileptogenic processes start up in the brain and how they result in a seizure. In order to detect these phenomena from the very beginning (hopefully minutes before the seizure itself), we introduced a technique, based on entropy topography, that studies the synchronization of the electric activity of neuronal sources in the brain. We tested it over 3 EEG data set from patients affected by partial epilepsy and 25 EEG recordings from patients affected by generalized seizures as well as over 40 recordings from healthy subjects. Entropy showed a very steady spatial distribution and appeared linked to the brain zone where seizures originated. A self-organizing map-based spatial clustering of entropy topography showed that the critical electrodes shared the same cluster long time before the seizure onset. The healthy subjects showed a more random behaviour.     

脑磁图脑功能连接网络癫痫棘波识别方法研究

脑磁图（MEG）现在被广泛用于临床检查及很多领域的医学研究中,基于静息态的脑磁图脑网络分析能用于研究大脑生理或病理机制。脑磁图分析对癫痫疾病的诊断具有重要的参考价值。对癫痫脑磁信号的自动分类可以及时对患者的情况作出判断,在临床上有很重要的意义。现有文献中对癫痫脑电信号的自动分类方法的研究已比较充分,但对癫痫脑磁信号的研究比较薄弱。提出了一种基于脑功能连接网络的全频段机器学习癫痫脑磁棘波信号自动判别方法,对四种分类器进行了综合判别对比,选择了效果最优的分类器,判别准确率可达到93.8%。因此,该方法在脑磁图癫痫棘波的自动识别与标记方面有较好的应用前景。     

Roles of coherent ongoing oscillations among dynamic cell assemblies in object perception

I constructed a cortical neural network model and investigated possible roles of coherent ongoing oscillations in membrane potentials of neurons in object perception. The model has a hierarchical structure consisting of two lower networks and one higher network that are reciprocally connected via divergent/convergent projections. Information about features and their relationships (or objects) is encoded by the population activities of neurons (or dynamic cell assemblies) of the lower networks and the higher network, respectively. The ongoing state of the network is expressed by 'random itinerancy' among these dynamic cell assemblies. Under the ongoing state, the dynamic cell assemblies belonging to the same object are transiently linked across the networks and coherently oscillate at lower frequencies (approximately 15 Hz). When the model perceives a presented object, the dynamic cell assemblies corresponding to the object are persistently linked together across the networks and coherently oscillate at higher frequencies (approximately 40 Hz). When the feedback pathways are impaired, the dynamic phase transition from the slow- to fast-oscillations is not induced by the object presentation, keeping the lower frequency oscillations (approximately 15 Hz) where the activated dynamic cell assemblies oscillate incoherently. Reaction times to the object presentation are greatly reduced if the ongoing oscillation frequencies fall within a specific range (approximately 20-30 Hz). I suggest that coherent ongoing slow-oscillations in cortical activity may serve as a ready state for sensory input, whereby the brain can respond effectively to sensory stimulation. Top-down processing via feedback pathways may give an essential contribution to the induction of coherent fast-oscillations across multiple cortical areas, by which relevant features are effectively integrated into a unified percept when stimulated with a sensory object.     

ILP-assisted de novo drug design

The accurate and early detection of epileptic seizures in continuous electroencephalographic (EEG) data has a growing role in the management of patients with epilepsy. Early detection allows for therapy to be delivered at the start of seizures and for caregivers to be notified promptly about potentially debilitating events. The challenge to detecting epileptic seizures, however, is that seizure morphologies exhibit considerable inter-patient and intra-patient variability. While recent work has looked at addressing the issue of variations across different patients (inter-patient variability) and described patient-specific methodologies for seizure detection, there are no examples of systems that can simultaneously address the challenges of inter-patient and intra-patient variations in seizure morphology. In our study, we address this complete goal and describe a multi-task learning approach that trains a classifier to perform well across many kinds of seizures rather than potentially overfitting to the most common seizure types. Our approach increases the generalizability of seizure detection systems and improves the tradeoff between latency and sensitivity versus false positive rates. When compared against the standard approach on the CHB–MIT multi-channel scalp EEG data, our proposed method improved discrimination between seizure and non-seizure EEG for almost 83 % of the patients while reducing false positives on nearly 70 % of the patients studied.     

EEG signal classification using wavelet feature extraction and a mixture of expert model

Mixture of experts (ME) is modular neural network architecture for supervised learning. A double-loop Expectation-Maximization (EM) algorithm has been introduced to the ME network structure for detection of epileptic seizure. The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. EEG signals were decomposed into the frequency sub-bands using discrete wavelet transform (DWT). Then these sub-band frequencies were used as an input to a ME network with two discrete outputs: normal and epileptic. In order to improve accuracy, the outputs of expert networks were combined according to a set of local weights called the “gating function”. The invariant transformations of the ME probability density functions include the permutations of the expert labels and the translations of the parameters in the gating functions. The performance of the proposed model was evaluated in terms of classification accuracies and the results confirmed that the proposed ME network structure has some potential in detecting epileptic seizures. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network model.     

Gamma oscillations in a nonlinear regime: a minimal model approach using heterogeneous integrate-and-fire networks

Fast oscillations and in particular gamma-band oscillation (20-80 Hz) are commonly observed during brain function and are at the center of several neural processing theories. In many cases, mathematical analysis of fast oscillations in neural networks has been focused on the transition between irregular and oscillatory firing viewed as an instability of the asynchronous activity. But in fact, brain slice experiments as well as detailed simulations of biological neural networks have produced a large corpus of results concerning the properties of fully developed oscillations that are far from this transition point. We propose here a mathematical approach to deal with nonlinear oscillations in a network of heterogeneous or noisy integrate-and-fire neurons connected by strong inhibition. This approach involves limited mathematical complexity and gives a good sense of the oscillation mechanism, making it an interesting tool to understand fast rhythmic activity in simulated or biological neural networks. A surprising result of our approach is that under some conditions, a change of the strength of inhibition only weakly influences the period of the oscillation. This is in contrast to standard theoretical and experimental models of interneuron network gamma oscillations (ING), where frequency tightly depends on inhibition strength, but it is similar to observations made in some in vitro preparations in the hippocampus and the olfactory bulb and in some detailed network models. This result is explained by the phenomenon of suppression that is known to occur in strongly coupled oscillating inhibitory networks but had not yet been related to the behavior of oscillation frequency.     

基于脑网络和TSK模糊系统的癫痫脑电识别

脑电检测是癫痫疾病诊断的重要手段,但基于脑电信号特征的人工标记方法,对癫痫发作状态识别的准确度较低。将脑功能网络与TSK模糊系统相结合,提出一种癫痫脑电信号识别的新方法。通过分析多通道脑电信号之间的同步性,构建癫痫患者的脑功能网络,采用复杂网络方法提取特征参数;以脑网络参数为输入特征建立TSK模糊系统模型,通过监督式学习训练分类器,用于识别癫痫发作期的脑电波形。实验结果证明了该方法的有效性,模糊分类器对癫痫发作状态识别的准确度达到98.36%,99.48%敏感度和97.24%特异度。该方法将复杂网络与机器学习算法相融合,为通过脑电检测识别癫痫疾病状态提供了新方法,具有重要的应用价值。     

Emotion recognition using EEG signals with relative power values and Bayesian network

Many researchers use electroencephalograms (EEGs) to study brain activity in the context of seizures, epilepsy, and lie detection. It is desirable to eliminate EEG artifacts to improve signal collection. In this paper, we propose an emotion recognition system for human brain signals using EEG signals. We measure EEG signals relating to emotion, divide them into five frequency ranges on the basis of power spectrum density, and eliminate low frequencies from 0 to 4 Hz to eliminate EEG artifacts. The resulting calculations of the frequency ranges are based on the percentage of the selected range relative to the total range. The calculated values are then compared to standard values from a Bayesian network, calculated from databases. Finally, we show the emotion results as a human face avatar.     

An approach for EEG of post traumatic sleep spindles and epilepsy seizures detection and classification in rats

The electroencephalographic (EEG) features of post traumatic epilepsy (PTE) are analyzed in the paper. The proposed method allows detection and classification of sleep spindles and epilepsy seizures. The experiments were conducted on a laboratory rats before and after traumatic brain inquiry (TBI). In the introduction, the details of the experiment along with the information about manual markup are provided. In the first part, the new method of sleep spindles and epilepsy seizures detection is described. The method is based on the analysis of the wavelet spectrogram extrema. Moreover, the described procedure of background extraction and ridge segmentation helps to classify signals as epilepsy seizures and sleep spindles. In the second part, the information about the clustering is given. k-Means clustering of seizures and spindles was performed based on signals power and frequency. The results of the clustering, along with the research of TBI effect on the EEG, are provided in the third part. It was shown that PTE may be considered as the cause of the frequency variance among clusters of sleep spindles and epilepsy seizures.