应用因果分析方法对癫痫发作间期头皮脑电信号进行致痫灶定侧

目的利用因果分析方法对癫痫发作间期的头皮脑电信号进行致痫灶定侧。方法在频域因果分析方法——自适应直接传递函数（ADTF）的基础上提出功率谱加权ADTF（psADTF）法,以给定频段内信号的功率谱对ADTF的标准化做加权,以适应不同癫痫波频域信息不同的特点。利用该方法对2组共30例患者的头皮脑电信号进行分析,含组1癫痫手术患者15例,组2门诊癫痫患者15例。其中组1患者共截取癫痫波样本数104个,组2患者共截取癫痫波样本数98个。结果组1患者通过psADTF分析对致痫灶定侧与手术侧一致的有96个,平均单个癫痫波致痫灶定侧准确率可达92％;组2患者通过psADTF分析对致痫灶定侧与专家判读结果一致的有94个,平均单个癫痫波致痫灶定侧准确率达96％。结论发作间期头皮脑电癫痫波信号的psADTF分析能够很好地辅助临床致痫灶定侧。     

Evaluation of driver fatigue on two channels of EEG data

Electroencephalogram (EEG) data is an effective indicator to evaluate driver fatigue. The 16 channels of EEG data are collected and transformed into three bands (θ, α, and β) in the current paper. First, 12 types of energy parameters are computed based on the EEG data. Then, Grey Relational Analysis (GRA) is introduced to identify the optimal indicator of driver fatigue, after which, the number of significant electrodes is reduced using Kernel Principle Component Analysis (KPCA). Finally, the evaluation model for driver fatigue is established with the regression equation based on the EEG data from two significant electrodes (Fp1 and O1). The experimental results verify that the model is effective in evaluating driver fatigue.     

基于C-FFuzzyEn的神经电生理信号同步性分析

基于传统互模糊熵,结合分数阶微积分提出分数阶互模糊熵（C-FFuzzyEn）,并基于该算法分析混沌耦合系统的同步性,进行健康对照者和癫痫患者不同脑区脑电信号的耦合性对比。结果表明,与传统互模糊熵相比,C-FFuzzyEn提高了不同耦合度模型的区分能力;与健康对照者相比,癫痫患者在癫痫发作时不同通道脑电信号之间C-FFuzzyEn较小,与癫痫发作时各神经元同步放电现象相吻合;相比互模糊熵,C-FFuzzyEn区分健康对照者与癫痫患者脑区之间脑电信号同步性的效果更好。C-FFuzzyEn可应用于脑电信号等神经电生理信号的同步性分析。     

Chaos–chaos transition of left hemisphere EEGs during standard tasks of Waterloo-Stanford Group Scale of hypnotic susceptibility

Abstract

A recent study, recurrence quantification analysis of EEG signals during standard tasks of Waterloo-Stanford Group Scale of hypnotic susceptibility investigated recurrence quantifiers (RQs) of hypnotic electroencephalograph (EEG) signals recorded after hypnotic induction while subjects were doing standard tasks of Waterloo-Stanford Group Scale (WSGS) of hypnotic susceptibility to distinguish subjects of different hypnotizability levels. Following the same analysis, the current study determines the capability of different RQs to distinguish subjects of low, medium and high hypnotizability level and studies the influence of hypnotizability level on underlying dynamic of tasks. Besides, EEG channels were sorted according to the number of their RQs, which differed significantly among subjects of different hypnotizability levels. Another valuable result was determination of major brain regions in observing significant differences in various task types (ideomotors, hallucination, challenge and memory).     

Automatic sleep scoring: a search for an optimal combination of measures

Objective

The objective of this study is to find the best set of characteristics of polysomnographic signals for the automatic classification of sleep stages.

Methods

A selection was made from 74 measures, including linear spectral measures, interdependency measures, and nonlinear measures of complexity that were computed for the all-night polysomnographic recordings of 20 healthy subjects. The adopted multidimensional analysis involved quadratic discriminant analysis, forward selection procedure, and selection by the best subset procedure. Two situations were considered: the use of four polysomnographic signals (EEG, EMG, EOG, and ECG) and the use of the EEG alone.

Results

For the given database, the best automatic sleep classifier achieved approximately an 81% agreement with the hypnograms of experts. The classifier was based on the next 14 features of polysomnographic signals: the ratio of powers in the beta and delta frequency range (EEG, channel C3), the fractal exponent (EMG), the variance (EOG), the absolute power in the sigma 1 band (EEG, C3), the relative power in the delta 2 band (EEG, O2), theta/gamma (EEG, C3), theta/alpha (EEG, O1), sigma/gamma (EEG, C4), the coherence in the delta 1 band (EEG, O1-O2), the entropy (EMG), the absolute theta 2 (EEG, Fp1), theta/alpha (EEG, Fp1), the sigma 2 coherence (EEG, O1-C3), and the zero-crossing rate (ECG); however, even with only four features, we could perform sleep scoring with a 74% accuracy, which is comparable to the inter-rater agreement between two independent specialists.

Conclusions

We have shown that 4-14 carefully selected polysomnographic features were sufficient for successful sleep scoring. The efficiency of the corresponding automatic classifiers was verified and conclusively demonstrated on all-night recordings from healthy adults.     

认知负载状态下的精神分裂症患者脑电图特异性研究

认知功能损害是精神分裂症的三大原发症状之一,在疾病早期发现和高危人群风险预警等方面具有重要价值。为了研究精神分裂症患者在认知负载状态下的脑电图特异性,本试验收集17例精神分裂症患者和19例健康受试者的脑电信号作为对照,基于小波变换提取各频段信号,计算非线性动力学及脑功能网络属性等特征,并利用机器学习算法将两类人群进行自动分类分析。试验结果表明,两组受试者在认知负载状态下,Fp1和Fp2导联在α、β、θ、γ这4个频带的关联维数和样本熵的差异均具有统计学意义,提示大脑额叶功能损伤是精神分裂症认知功能损害的重要原因。进一步基于机器学习的自动分类分析结果表明,将非线性动力学与脑功能网络属性相结合作为分类器的输入特征,所得分类效果最优,其结果显示准确率为76.77%、敏感度为72.09%、特异性为80.36%。本研究结果表明,脑电信号的非线性动力学和脑功能网络属性等特征,或可作为精神分裂症早期筛查和辅助诊断的潜在生物标记物。     

Analysis of chosen SNVs in GPC5, CD58 and IRF8 genes in multiple sclerosis patients

PurposeMultiple sclerosis (MS) is an autoimmune disease of the central nervous system with a neurodegenerative compound. Heterogenetic background of autoimmunity pathway components has been suggested in the MS pathogenesis. The main aim of our study was to evaluate the association between selected polymorphisms of theCD58, IRF8 and GPC5 genes and treatment effectiveness in a group of relapsing-remitting MS patients. This is the first study of MS patients from Podlaskie Region in the Polish population.Materials and methodsThe study group comprised 174 relapsing-remitting MS patients diagnosed under 40 years of age. Genotyping was performed using ready to use TaqMan assays.ResultsWe demonstrate a strong association of the polymorphisms with sex, age of onset and response to the treatment applied. A significant correlation was observed in the presence of allele T of rs10492503 polymorphism inGPC5 gene with sex and age of MS onset. Logistic regression analysis revealed an increased risk of the interaction of rs17445836 in IRF8 gene with male sex and the type of treatment (OR = 3.80, p < 0.05), and a decreased risk in the interaction of female sex with disease progress according to the EDSS scale (OR=-2.33, p < 0.05).ConclusionsThe analysis of the correlation between different alleles, genotypes and clinical status confirmed the interaction between the genetic factors of age of onset and response to therapy. The study suggests that genetic variants inGPC5, CD58 and IRF8 genes may be of clinical interest in MS as predictors of age of onset and response to therapy.     

A statistical approach to computerized EEG: Preliminary data on control subjects and epileptic patients

Factor Analysis represents an important method of extracting salient features from EEG data condensing functional as well as spatial informations in a few factors and so reducing redundancy of multi-channels computerized EEG data. A 16-channel computerized frequency analysis of background brain electrical activity during 3 functional conditions (eyes closed, eyes open and hyperventilation) were performed in 2 groups, 50 healthy subjects and 14 epileptic patients with generalized seizures. The differences in each frequency band between the 2 groups have been assessed by means of factor scores. Factor scores differences between control and epileptic patients have been found in theta and alpha frequency bands. These interictal EEG abnormalities were more evident in epileptic patients. This preliminary study shows the confirmatory capability of Factor Analysis and its usefulness in managing and comparing computerized EEG data.     

用相空间内邻近轨线伸展的相关性研究脑电的混沌特性

本文的研究针对目前脑电的非线性动力学研究中一个尚未定论的关键问题：脑电究竟是混沌信号还是无序的随机信号？文中介绍了作者的研究成果：用相空间中一对邻近轨线伸展的伸关性来鉴别混沌和无序的随机运动,并应用这种新的混沌行为分析方法证明了脑电的混沌特性,作为比较和验证,本文也介绍了用该方法对加上不同水平噪声和Lorenz系统仿真的研究结果。     

Novel potential biomarkers for pancreatic cancer – A systematic review

BackgroundIt is estimated that in developed countries the incidence rate of pancreatic cancer (PC) will continue to rise and by 2020 will be the second most fatal cancer. The mortality of PC patients closely parallels the incidence rate, as this malignancy remains asymptomatic until it reaches an advanced stage of disease. Thus, novel biochemical markers that improve the management of PC patients are necessary. The aim of the work that follows is to investigate whether selected inflammatory mediators might be used in the diagnosis of PC, with the aim of improving the prognosis for PC patients.MethodsWe performed a thorough search for literature pertaining to our investigation via the MEDLINE/PubMed database.ResultsIt has been proved that certain in?ammatory mediators might be involved in tumor progression, such as growth, proliferation, migration and angiogenesis of tumor cells. In the present review, we summarized and referred to a number of original papers concerning the clinical significance of selected cytokines and specific inflammatory proteins such as C-reactive protein, as well as of various matrix metalloproteinases and their tissue inhibitors, as potential biomarkers for PC in comparison to well-established tumor markers for this malignancy.ConclusionPresented proteins might be potential biomarkers in the diagnosis and progression of PC.     

脑电近似熵分析对帕金森病患者认知功能障碍的评估作用

目的：观察帕金森病（PD）患者与对照者的脑电近似熵的差异,并探讨其对PD认知功能障碍的评估作用。方法：对31例PD患者和31例对照组进行简易精神状态检查量表（MMsE）测试、蒙特利尔认知评估量表（MoCA）测试、事件相关电位（ERP）检测P300波潜伏期（P3PL）,采集安静闭眼、闭眼心算、安静睁眼三种状态下的脑电信号,利用非线性参数近似熵对脑电数据进行分析。结果：①与对照组相比,PD组MMSE评分和MoCA评分均降低,P3PL延长,差异有统计学意义;②与对照组相比,在安静闭眼、闭眼心算、安静睁眼三种状态下,PD组的近似熵在大部分脑区降低,差异有统计学意义这些脑区在安静闭眼状态时是Fp1、Fp2、F4、O2、F8、T3、T4、T5、T6,在闭眼心算状态时是Fp1、F3、F4、C3、P3、O1、T3、T5、T6,在安静睁眼状态时除C4外的15个脑区;③与对照组相比,PD患者近似熵变化值于F3、C3、P3、O1降低,差异有统计学意义;④所有受试者安静闭眼、闭眼心算、安静睁眼三种状态下大部分脑区近似熵值与MMSE评分、MoCA评分呈正相关,与P3PL呈负相关。结论：脑电近似熵可以作为一项客观评定PD患者认知功能障碍的电生理指标。     

Classification of low-density EEG for epileptic seizures by energy and fractal features based on EMD

We are here to present a new method for the classification of epileptic seizures from electroencephalogram (EEG) signals. It consists of applying empirical mode decomposition (EMD) to extract the most relevant intrinsic mode functions (IMFs) and subsequent computation of the Teager and instantaneous energy, Higuchi and Petrosian fractal dimension, and detrended fluctuation analysis (DFA) for each IMF. We validated the method using a public dataset of 24 subjects with EEG signals from 22 channels and showed that it is possible to classify the epileptic seizures, even with segments of six seconds and a smaller number of channels (e.g., an accuracy of 0.93 using five channels). We were able to create a general machine-learning-based model to detect epileptic seizures of new subjects using epileptic-seizure data from various subjects, after reducing the number of instances, based on the k-means algorithm.     

Recurrence quantification analysis of sleep electoencephalogram in sleep apnea syndrome in humans

The aim of this study is to elucidate whether the results of recurrence quantification analysis (RQA) of sleep EEGs in sleep apnea syndrome are valuable for analyzing sleep EEGs in sleep apnea syndrome. We investigated the ability of RQA to discriminate sleep stages and to characterize the different behaviors of sleep EEGs in sleep apnea syndrome. RQA was applied to EEG signals during sleep stages 1, 2, slow wave sleep (SWS), REM and the stage 'awake.' The sleep EEG signals were obtained from the MIT-BIH polysomnographic database. To examine the differences in the RQA measures for all sleep stages, one-way analysis of variance (ANOVA) and post hoc analysis were performed. From the results, all sleep stages could be distinctly discriminated by means of the RQA measure of %RATIO. We observed that stage 1 and REM had fewer recurrences, and that stage 2 was more autocorrelated than the other stages. The different dynamic behaviors of wakefulness and sleep EEG were also observed. Of significant interest was the observation that RQA was able to distinguish stage 1 from REM. In conclusion, we suggest that the information obtained from RQA of sleep EEGs in sleep apnea syndrome is valuable for its analysis, and that RQA constitutes a useful tool for analyzing sleep EEGs in subjects with sleep apnea syndrome.     

Diagnostic power of VEGF,MMP-9 and TIMP-1 in patients with breast cancer. A multivariate statistical analysis with ROC curve

PurposeVascular endothelial growth factor is an important factor in promoting angiogenesis in malignant processes, matrix metalloproteinase-9 in the degradation of extracellular matrix, which enhances metastasis, and tissue inhibitor of metalloproteinase-1 is its inhibitor. The aim of this study was to investigate the diagnostic power of these parameters in comparison to CA15-3 in breast cancer patients and in relation to the control group.Materials/methodsThe study included 120 breast cancer patients, 60 patients with benign breast tumors and 60 healthy women. Plasma levels of tested parameters were determined by enzyme-linked immunosorbent assay, CA15-3 by chemiluminescent microparticle immuno assay.ResultsTissue inhibitor of metalloproteinase-1 showed the highest value of sensitivity in breast cancer group (86.25%) and, more importantly, highest value in breast cancer stage I (85%). Vascular endothelial growth factor also showed high sensitivity (stage I and II–75%, III–85%, IV–70% and 76.25% in total breast cancer group) and the highest specificity (85%) from all tested parameters. It was also the only parameter which had statistically significant area under curve in all stages. In the total breast cancer group all tested parameters showed statistically significant area under curve, but the maximum range was obtained for combination: ‘vascular endothelial growth factor + CA15-3′. Vascular endothelial growth factor seems to be the best candidate for diagnosing breast cancer stage I and for differentiating between breast cancer and non-carcinoma cases.ConclusionsThe combined analysis of tested parameters and CA15-3 resulted in an increase in sensitivity and area under curve values, which provides hope for developing new panel of biomarkers that may be used in diagnosing breast cancer in the future.     

Seizure characterisation using frequency-dependent multivariate dynamics

The characterisation of epileptic seizures assists in the design of targeted pharmaceutical seizure prevention techniques and pre-surgical evaluations. In this paper, we expand on the recent use of multivariate techniques to study the cross-correlation dynamics between electroencephalographic (EEG) channels. The maximum overlap discrete wavelet transform (MODWT) is applied in order to separate the EEG channels into their underlying frequencies. The dynamics of the cross-correlation matrix between channels, at each frequency, are then analysed in terms of the eigenspectrum. By examination of the eigenspectrum, we show that it is possible to identify frequency-dependent changes in the correlation structure between channels which may be indicative of seizure activity.The technique is applied to EEG epileptiform data and the results indicate that the correlation dynamics vary over time and frequency, with larger correlations between channels at high frequencies. Additionally, a redistribution of wavelet energy is found, with increased fractional energy demonstrating the relative importance of high frequencies during seizures. Dynamical changes also occur in both correlation and energy at lower frequencies during seizures, suggesting that monitoring frequency-dependent correlation structure can characterise changes in EEG signals during these. Future work will involve the study of other large eigenvalues and inter-frequency correlations to determine additional seizure characteristics.     

癫痫脑电特征波的综合检测分类方法研究

本文将小波变换、人工神经网络、专家规则判据等多种检测方法有机地结合起来 ,用于癫痫脑电特征波的检测与分类 ,以充分发挥不同方法的优势。这种综合检测分类方法是先将预处理的多导脑电时间序列经小波变换将脑电中癫痫特征波在不同尺度下分离出来 ,再对选出的癫痫嫌疑波进行特征参数提取 ,然后把特征参数送入已经训练好的人工神经网络进行分类识别 ,最后再由专家规则判断筛选并作出检测分类统计报告。研究表明 ,该方法具有很好的信号特征提取和屏蔽随机噪声能力 ,获得了较好的检出率 ;尤其适合于非平稳、非线性生物医学信号的检测分类 ,值得进一步深入研究     

一种基于改进经验模态分解的癫痫脑电识别新方法

在临床癫痫诊断过程中,为了提高癫痫脑电的识别率,能在癫痫发作前期就预测到癫痫疾病,其特征波的提取至关重要。针对这一问题,提出将平行延拓与镜像延拓相结合来改进EMD算法。首先,使用平行延拓的方法,在原始脑电信号的左、右端点处分别预测出一个极值;然后,使用基于镜像延拓的EMD方法,对信号进行镜像延拓,以避免经验模态分解过程中的端点效应;最后,采用支持向量机进行信号的分类识别。算法验证数据取自德国伯恩大学癫痫研究中心的脑电数据库,其中50例是正常脑电信号、50例是癫痫发作间期的脑电信号。实验研究表明：该方法对总测试脑电信号的识别率达到94%。其中,正常脑电信号和癫痫脑电信号的独立识别率均为94%,比传统EMD算法处理后的脑电识别率提高了5%,可见该方法可以有效地预测癫痫脑电。     

Epilepsy detection from EEG signals: a review

Abstract

Over many decades, research is being attempted for the detection of epileptic seizure to support for automatic diagnosis system to help clinicians from burdensome work. In this respect, an enormous number of research papers is published for identification of epileptic seizure. It is difficult to present a detailed review of all these literature. Therefore, in this paper, an attempt has been made to review the detection of an epileptic seizure. More than 100 research papers have been discussed to discern the techniques for detecting the epileptic seizure. Further, the literature survey shows that the pattern recognition required to detect epileptic seizure varies with different conditions of EEG datasets. This is mainly due to the fact that EEG detected under different conditions has different characteristics. This is, in turn, necessitates the identification of pattern recognition technique to effectively distinguish EEG epileptic data from a various condition of EEG data.     

利用广义复杂度对癫痫病发作进行预测的方法研究

目的：通过用广义复杂度的运算方法,分析癫痫病患者发病期间的脑电图,计算出这一段脑电图数据的复杂度,通过对复杂度数据的分析,对癫痫病的发作进行预测。进而提高癫痫病患者的生活质量,降低癫痫病患者的生命危险。方法：将癫痫病发病的脑电图的图像信号还原成数字信号,计算出该数字信号各个区间的广义复杂度值。得出癫痫病发病前脑电图数据复杂度的变化状态,对癫痫病患者发病进行预测。结果：计算出21名癫痫病患者在发病前后共一个小时的脑电图数字信号的广义复杂度值,通过观察21名癫痫病患者在癫痫病发病期间的脑电图的数字信号的广义复杂度变化情况,对癫痫病的发作进行预测（对2l名病人癫痫病发作的数据进行预测研究,平均准确率可达94.3%）。结论：本文提出了一种基于广义复杂度的癫痫发作预测方法,通过对21名癫痫病患者的脑电图信号的实验和计算,结果表明,该方法可以有效地预测到癫痫发作。     

CDKL5 mutations cause infantile spasms, early onset seizures, and severe mental retardation in female patients

Objective

To determine the frequency of mutations in CDKL5 in both male and female patients with infantile spasms or early onset epilepsy of unknown cause, and to consider whether the breadth of the reported phenotype would be extended by studying a different patient group.

Methods

Two groups of patients were investigated for CDKL5 mutations. Group 1 comprised 73 patients (57 female, 16 male) referred to Cardiff for CDKL5 analysis, of whom 49 (42 female, 7 male) had epileptic seizure onset in the first six months of life. Group 2 comprised 26 patients (11 female, 15 male) with infantile spasms previously recruited to a clinical trial, the UK Infantile Spasms Study. Where a likely pathogenic mutation was identified, further clinical data were reviewed.

Results

Seven likely pathogenic mutations were found among female patients from group 1 with epileptic seizure onset in the first six months of life, accounting for seven of the 42 in this group (17%). No mutations other than the already published mutation were found in female patients from group 2, or in any male patient from either study group. All patients with mutations had early signs of developmental delay and most had made little developmental progress. Further clinical information was available for six patients: autistic features and tactile hypersensitivity were common but only one had suggestive Rett‐like features. All had a severe epileptic seizure disorder, all but one of whom had myoclonic jerks. The EEG showed focal or generalised changes and in those with infantile spasms, hypsarrhythmia. Slow frequencies were seen frequently with a frontal or fronto‐temporal predominance and high amplitudes.

Conclusions

The spectrum of the epileptic seizure disorder, and associated EEG changes, in those with CDKL5 mutations is broader than previously reported. CDKL5 mutations are a significant cause of infantile spasms and early epileptic seizures in female patients, and of a later intractable seizure disorder, irrespective of whether they have suspected Rett syndrome. Analysis should be considered in these patients in the clinical setting.