NeuroKinect: A Novel Low-Cost 3Dvideo-EEG System for Epileptic Seizure Motion Quantification

Epilepsy is a common neurological disorder which affects 0.5–1% of the world population. Its diagnosis relies both on Electroencephalogram (EEG) findings and characteristic seizure−induced body movements − called seizure semiology. Thus, synchronous EEG and (2D)video recording systems (known as Video−EEG) are the most accurate tools for epilepsy diagnosis. Despite the establishment of several quantitative methods for EEG analysis, seizure semiology is still analyzed by visual inspection, based on epileptologists’ subjective interpretation of the movements of interest (MOIs) that occur during recorded seizures. In this contribution, we present NeuroKinect, a low-cost, easy to setup and operate solution for a novel 3Dvideo-EEG system. It is based on a RGB-D sensor (Microsoft Kinect camera) and performs 24/7 monitoring of an Epilepsy Monitoring Unit (EMU) bed. It does not require the attachment of any reflectors or sensors to the patient’s body and has a very low maintenance load. To evaluate its performance and usability, we mounted a state-of-the-art 6-camera motion-capture system and our low-cost solution over the same EMU bed. A comparative study of seizure-simulated MOIs showed an average correlation of the resulting 3D motion trajectories of 84.2%. Then, we used our system on the routine of an EMU and collected 9 different seizures where we could perform 3D kinematic analysis of 42 MOIs arising from the temporal (TLE) (n = 19) and extratemporal (ETE) brain regions (n = 23). The obtained results showed that movement displacement and movement extent discriminated both seizure MOI groups with statistically significant levels (mean = 0.15 m vs. 0.44 m, p<0.001; mean = 0.068 m³ vs. 0.14 m³, p<0.05, respectively). Furthermore, TLE MOIs were significantly shorter than ETE (mean = 23 seconds vs 35 seconds, p<0.01) and presented higher jerking levels (mean = 345 ms⁻³ vs 172 ms⁻³, p<0.05). Our newly implemented 3D approach is faster by 87.5% in extracting body motion trajectories when compared to a 2D frame by frame tracking procedure. We conclude that this new approach provides a more comfortable (both for patients and clinical professionals), simpler, faster and lower-cost procedure than previous approaches, therefore providing a reliable tool to quantitatively analyze MOI patterns of epileptic seizures in the routine of EMUs around the world. We hope this study encourages other EMUs to adopt similar approaches so that more quantitative information is used to improve epilepsy diagnosis.     

基于脑电信号的癫痫发作预测特征及识别

解码癫痫发作前脑电信号的神经元集群异常痫样放电活动,对癫痫发作进行有效预测并实施病前干预,可显著减少疾病病损,是癫痫防治的研究热点之一。基于脑电信号的癫痫发作预测研究关键在于发作间期和前期的异常状态识别,研究上述两状态间的神经动力学特征差异对明确癫痫发病机制、选取高分辨特征,进而有效识别该渐进性疾病所处的发作阶段具有重要价值。目前,研究者已对当前主流特征提取及模式识别方法进行了充分的调研梳理,但忽视了神经动态特征变化对于癫痫发作预测的重要意义。基于此,本文归纳总结了5类典型的发作预测特征分析方法及其优缺点,重点剖析了发作间期至前期神经生理特征的动态变化及其动力学特性,类比分析了当前该领域主流的机器学习和深度学习特征识别方法,以期为进一步建立精准、高效的癫痫发作预测技术提供新思路。     

Epileptic Seizure Detection Based on New Hybrid Models with Electroencephalogram Signals

Objectives: Epileptic seizures are one of the most common diseases in society and difficult to detect. In this study, a new method was proposed to automatically detect and classify epileptic seizures from EEG (Electroencephalography) signals.Methods: In the proposed method, EEG signals classification five-classes including the cases of eyes open, eyes closed, healthy, from the tumor region, an epileptic seizure, has been carried out by using the support vector machine (SVM) and the normalization methods comprising the z-score, minimum-maximum, and MAD normalizations. To classify the EEG signals, the support vector machine classifiers having different kernel functions, including Linear, Cubic, and Medium Gaussian, have been used. In order to evaluate the performance of the proposed hybrid models, the confusion matrix, ROC curves, and classification accuracy have been used. The used SVM models are Linear SVM, Cubic SVM, and Medium Gaussian SVM.Results: Without the normalizations, the obtained classification accuracies are 76.90%, 82.40%, and 81.70% using Linear SVM, Cubic SVM, and Medium Gaussian SVM, respectively. After applying the z-score normalization to the multi-class EEG signals dataset, the obtained classification accuracies are 77.10%, 82.30%, and 81.70% using Linear SVM, Cubic SVM, and Medium Gaussian SVM, respectively. With the minimum-maximum normalization, the obtained classification accuracies are 77.20%, 82.40%, and 81.50% using Linear SVM, Cubic SVM, and Medium Gaussian SVM, respectively. Moreover, finally, after applying the MAD normalization to the multi-class EEG signals dataset, the obtained classification accuracies are 76.70%, 82.50%, and 81.40% using Linear SVM, Cubic SVM, and Medium Gaussian SVM, respectively.Conclusion: The obtained results have shown that the best hybrid model is the combination of cubic SVM and MAD normalization in the classification of EEG signals classification five-classes.     

Seizure Outcomes and Predictors of Recurrent Post-Stroke Seizure: A Retrospective Observational Cohort Study

Background

Seizure is a common complication after stroke (termed “post-stroke seizure,” PSS). Although many studies have assessed outcomes and risk factors of PSS, no reliable predictors are currently available to determine PSS recurrence. We compared baseline clinical characteristics and post-stroke treatment regimens between recurrent and non-recurrent PSS patients to identify factors predictive of recurrence.

Methods

Consecutive PSS patients admitted to our stroke center between January 2011 and July 2013 were monitored until February 2014 (median 357 days; IQR, 160–552) and retrospectively evaluated for baseline clinical characteristics and PSS recurrence. Cumulative recurrence rates at 90, 180, and 360 days post-stroke were estimated by Kaplan—Meier analysis. Independent predictors of recurrent PSS were identified by Cox proportional-hazards analysis.

Results

A total of 104 patients (71 men; mean age, 72.1 ± 11.2 years) were analyzed. PSS recurred in 31 patients (30%) during the follow-up. Factors significantly associated with PSS recurrence by log-rank analysis included previous PSS, valproic acid (VPA) monotherapy, polytherapy with antiepileptic drugs (AEDs), frontal cortical lesion, and higher modified Rankin Scale score at discharge (all p < 0.05). Independent predictors of recurrent PSS were age <74 years (HR 2.38, 95% CI 1.02–5.90), VPA monotherapy (HR 3.86, 95% CI 1.30–12.62), and convulsions on admission (HR 3.87, 95% CI 1.35–12.76).

Conclusions

Approximately one-third of PSS patients experienced seizure recurrence within one year. The predictors of recurrent PSS were younger age, presence of convulsions and VPA monotherapy. Our findings should be interpreted cautiously in countries where monotherapy with second-generation AEDs has been approved because this study was conducted while second-generation AEDs had not been officially approved for monotherapy in Japan.     

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.     

Analysis of Longitudinal Data of Epileptic Seizure Counts – A Two‐State Hidden Markov Regression Approach

This paper discusses a two‐state hidden Markov Poisson regression (MPR) model for analyzing longitudinal data of epileptic seizure counts, which allows for the rate of the Poisson process to depend on covariates through an exponential link function and to change according to the states of a two‐state Markov chain with its transition probabilities associated with covariates through a logit link function. This paper also considers a two‐state hidden Markov negative binomial regression (MNBR) model, as an alternative, by using the negative binomial instead of Poisson distribution in the proposed MPR model when there exists extra‐Poisson variation conditional on the states of the Markov chain. The two proposed models in this paper relax the stationary requirement of the Markov chain, allow for overdispersion relative to the usual Poisson regression model and for correlation between repeated observations. The proposed methodology provides a plausible analysis for the longitudinal data of epileptic seizure counts, and the MNBR model fits the data much better than the MPR model. Maximum likelihood estimation using the EM and quasi‐Newton algorithms is discussed. A Monte Carlo study for the proposed MPR model investigates the reliability of the estimation method, the choice of probabilities for the initial states of the Markov chain, and some finite sample behaviors of the maximum likelihood estimates, suggesting that (1) the estimation method is accurate and reliable as long as the total number of observations is reasonably large, and (2) the choice of probabilities for the initial states of the Markov process has little impact on the parameter estimates.     

A Computational Study of Alternate SELEX

Systematic evolution of ligands by exponential enrichment (SELEX) is a procedure for identifying nucleic acid (NA) molecules with affinities for specific target species, such as proteins, peptides, or small organic molecules. Here, we extend the work in Seo et al. (Bull Math Biol 72:1623–1665, 2010) (multiple-target SELEX or positive SELEX) and examine an alternate SELEX process with multiple targets by incorporating negative selection into a positive SELEX protocol. The alternate SELEX process is done iteratively by alternating several positive selection rounds with several negative selection rounds. At the end of each positive selection round, NAs are eluted from the bound product and amplified by polymerase chain reaction (PCR) to increase the size of the pool of NA species that bind preferentially to the given positive target vector. The enriched population of NAs is then exposed to the negative targets (undesired targets). The free NA species (instead of the bound NA species being eluted) are retained and amplified by PCR (negative selection). The goal is to minimize an enrichment of nonspecifically binding NAs against multiple targets. While positive selection alone results in a pool of NAs that bind tightly to a given target vector, negative selection results in the subset of the NAs that bind best to the nontarget vectors that are also present. By alternating the two processes, we eventually obtain a refined population of nucleic acids that bind to the desired target(s) with high “selectivity” and “specificity.” In the present paper, we give formulations of the negative and alternate selection processes and define their efficiencies in a meaningful way. We study the asymptotic behavior of alternate SELEX system as a discrete-time dynamical system. To do this, we use the chemical potential to examine how alternate SELEX leads to the selection of NAs with more specific interactions when the ratio of the number of positive selection rounds to the number of negative selection rounds is fixed. Alternate SELEX is said to be globally asymptotically stable if, given the initial target vector and a fixed ratio, the distribution of the limiting NA fractions does not depend on the relative concentrations of the NAs in the initial pool (provided that all of the NA species are initially present in the initial pool). We state conditions on the matrix of NA—target affinities that determine when the alternate SELEX process is globally asymptotically stable in this sense and illustrate these results computationally.     

左旋组氨酸对匹罗卡品致痫大鼠癫痫发作和海马区神经细胞凋亡的影响

目的:观察左旋组氨酸(L-His)对匹罗卡品(PLO)致痫大鼠皮质脑电图及大鼠海马各区神经细胞凋亡的影响。方法:雄性SD大鼠30只,随机平均分为对照组(匹罗卡品组)、干预组(匹罗卡品+左旋组氨酸组)。各组给予相关干预处理后腹腔注射匹罗卡品建立癫痫模型,行皮质脑电图观察及海马区细胞凋亡染色观察。结果:经皮质脑电图显示,干预组潜伏期延长、痫波频率及大发作次数较对照组明显降低,差异有统计学意义(P<0.05)。凋亡染色显示,对照组的海马各区细胞在各时间点的凋亡数明显高于干预组,差异具有统计学意义(P<0.05)。结论:左旋组氨酸可以延迟匹罗卡品致痫的形成并降低点燃后癫痫发作程度,降低海马各区细胞凋亡数值,提示左旋组氨酸具有抗痫作用。     

Computational Modeling of Seizure Dynamics Using Coupled Neuronal Networks: Factors Shaping Epileptiform Activity

Epileptic seizure dynamics span multiple scales in space and time. Understanding seizure mechanisms requires identifying the relations between seizure components within and across these scales, together with the analysis of their dynamical repertoire. Mathematical models have been developed to reproduce seizure dynamics across scales ranging from the single neuron to the neural population. In this study, we develop a network model of spiking neurons and systematically investigate the conditions, under which the network displays the emergent dynamic behaviors known from the Epileptor, which is a well-investigated abstract model of epileptic neural activity. This approach allows us to study the biophysical parameters and variables leading to epileptiform discharges at cellular and network levels. Our network model is composed of two neuronal populations, characterized by fast excitatory bursting neurons and regular spiking inhibitory neurons, embedded in a common extracellular environment represented by a slow variable. By systematically analyzing the parameter landscape offered by the simulation framework, we reproduce typical sequences of neural activity observed during status epilepticus. We find that exogenous fluctuations from extracellular environment and electro-tonic couplings play a major role in the progression of the seizure, which supports previous studies and further validates our model. We also investigate the influence of chemical synaptic coupling in the generation of spontaneous seizure-like events. Our results argue towards a temporal shift of typical spike waves with fast discharges as synaptic strengths are varied. We demonstrate that spike waves, including interictal spikes, are generated primarily by inhibitory neurons, whereas fast discharges during the wave part are due to excitatory neurons. Simulated traces are compared with in vivo experimental data from rodents at different stages of the disorder. We draw the conclusion that slow variations of global excitability, due to exogenous fluctuations from extracellular environment, and gap junction communication push the system into paroxysmal regimes. We discuss potential mechanisms underlying such machinery and the relevance of our approach, supporting previous detailed modeling studies and reflecting on the limitations of our methodology.     

Detection of Focal and Non-focal Epileptic Seizure Using Continuous Wavelet Transform-Based Scalogram Images and Pre-trained Deep Neural Networks

Epilepsy is a neurological disease from which a large number of younger and older people suffer all over the world. The status of the patients is primarily examined by using Electroencephalogram (EEG) signals. The most important part for successful surgery is to locate the epileptic seizure in the brain. For this reason, it is very useful to detect the seizure area automatically before surgery. In this research, a novel method based on continuous wavelet transform (CWT) and two-dimensional (2D) convolutional neural networks (CNNs) has been proposed to predict focal and non-focal epileptic seizure. The AlexNet, InceptionV3, Inception-ResNetV2, ResNet50 and VGG16 pre-trained models have been used to automatically classify 2D-scalogram images into focal and non-focal epileptic seizure. The performances of 5 pre-trained models were compared and the detection results of 2D-scalograms were examined. The best classification accuracy of 92.27% is yielded by the InceptionV3 model among the other used four pre-trained models. As a result, it may be said that the pre-trained models and 2D-scalogram images of focal and non-focal EEG signals will be useful to neurologists for rapid and robust prediction epileptic seizure before surgery.     

Catastrophe Theory Enables Moves to be Detected Towards and Away from Self-Organization: The Example of Epileptic Seizure Onset

Macroscopic systems with many interacting subunits, when driven far from equilibrium, exhibit self-organization, for example when a pathological rhythm appears suddenly in an epileptic patient. Sudden changes occurring while conditions vary smoothly have, in cases of interest, underlying mathematics that are the subject of Thom’s catastrophe theory. The assumption made herein that the system’s state variables, akin to order parameters, reduce in practice to only one single real variable, ensures that the system derives from a potential function, and warrants recourse to the catastrophe theory. The order parameter is, furthermore, interpreted as a measure of the electropathophysiological activity in the brain, increasing monotonously with the degree of neuronal synchronism. With two neuronal influences, excitatory and inhibitory, as control parameters, the catastrophe is the archetypal cusp. Implementation of catastrophe theory leads to equations showing that fluctuations in a system’s dynamics may be utilised for signalling steps precursory to oncoming catastrophes. Pre-seizure dynamics in epileptic patients exhibit steps towards and away from catastrophe; the steps away are interpreted in terms of homeostatic feedback, consequent on changing patterns of neuronal activity. A number of characteristics of epileptic seizures of differing types merely follow from the geometry of the cusp equilibrium surface. In particular, types of seizures are distinguished by their angle of final approach to onset in parameter space. The measurable parameters by which approach to catastrophe is characterized, may be of use in investigations of the organism’s plasticity in epileptic patients, and in tests of therapeutic means for preventing seizures. There is no need to resort to a model, in the usual sense of the word, and therefore no differential equation needs to be set up.     

Cellulose-Lignin Interactions (A Computational Study)

Within a broader program of study of the molecular structure of plant cell walls, molecular dynamics calculations were used to explore the character of the motion of lignin model compounds near a cellulose surface. Model cellulose microfibrils, which have a large number of hydroxyl groups on the surface, appear to have a net attractive interaction with the lignin models examined in this study. The lignin monomer coniferyl alcohol rapidly adsorbed onto the surface from a water layer after it was released 13 A from the surface. The major long-range force responsible for this adsorption is likely electrostatic. The attractive interaction is sufficient to restrict the motion of coniferyl alcohol when it is within 1 A of the surface and to orient the phenyl ring parallel to the surface. The [beta]-O-4-linked trimer also was observed to adsorb onto the surface with two of its phenyl rings parallel to the surface. These results suggest a mechanism by which the polysaccharide component of the plant cell wall could influence the structure of lignin. Furthermore, they provide a rationalization of the experimental observation that polysaccharides can change the course of dehydrogenation polymerization of cinnamyl alcohols.     

连翘醋营抗感染 、 解热作用研究

本文观察了连翘酯苷的抗感染作用及解热作用,探讨其与中药连翘清热解毒作用的关系。制备小鼠三种细菌（绿脓杆菌、大肠杆菌、金黄色葡菌球菌）感染模型,观察动物的死亡数,计算死亡率;复制酵母致大鼠发热模型与内毒素致家兔发热模型。观察不同时间动物体温的变化。结果表明连翘酯苷各剂量组均可显著降低细菌感染模型动物的死亡数和死亡率;均可显著降低发热模型动物的体温。提示连翘酯苷具有明显的抗感染和解热作用。     

关于一类污染治理的单种群模型的数学研究

以脉冲微分方程为基础,建立了一类污染环境中在固定时刻对污染进行治理的具有时滞效应的单种群阶段结构模型.详细研究了该模型的动力学性质,给出了种群灭绝和持续生存的充分条件,并进一步研究了污染治理和时滞效应对种群灭绝的影响.本文具有很强的生物意义,为环境污染治理问题提供了可靠的依据.     

Stimulus Set Meaningfulness and Neurophysiological Differentiation: A Functional Magnetic Resonance Imaging Study

A meaningful set of stimuli, such as a sequence of frames from a movie, triggers a set of different experiences. By contrast, a meaningless set of stimuli, such as a sequence of ‘TV noise’ frames, triggers always the same experience—of seeing ‘TV noise’—even though the stimuli themselves are as different from each other as the movie frames. We reasoned that the differentiation of cortical responses underlying the subject’s experiences, as measured by Lempel-Ziv complexity (incompressibility) of functional MRI images, should reflect the overall meaningfulness of a set of stimuli for the subject, rather than differences among the stimuli. We tested this hypothesis by quantifying the differentiation of brain activity patterns in response to a movie sequence, to the same movie scrambled in time, and to ‘TV noise’, where the pixels from each movie frame were scrambled in space. While overall cortical activation was strong and widespread in all conditions, the differentiation (Lempel-Ziv complexity) of brain activation patterns was correlated with the meaningfulness of the stimulus set, being highest in the movie condition, intermediate in the scrambled movie condition, and minimal for ‘TV noise’. Stimulus set meaningfulness was also associated with higher information integration among cortical regions. These results suggest that the differentiation of neural responses can be used to assess the meaningfulness of a given set of stimuli for a given subject, without the need to identify the features and categories that are relevant to the subject, nor the precise location of selective neural responses.     

Microstructure Study of Electrochemically Driven LixSi

We report the direct observation of microstructural changes of Li_xSi electrode with lithium insertion. HRTEM experiments confirm that lithiated amorphous silicon forms a shell around a core made up of the unlithiated silicon and that fully lithiated silicon contains a large number of pores of which concentration increases toward the center of the particle. Chemomechanical modeling is employed in order to explain this mechanical degradation resulting from stresses in the Li_xSi particles with lithium insertion. Because lithiation‐induced volume expansion and pulverization are the key mechanical effects that plague the performance and lifetime of high‐capacity Si anodes in lithium‐ion batteries, our observations and chemomechanical simulation provide important mechanistic insight for the design of advanced battery materials.