Electroencephalogram findings in 10 patients with post-stroke epilepsy:A retrospective study

BACKGROUND Post-stroke epilepsy is a common and easily overlooked complication of acute cerebrovascular disease.Long-term seizures can seriously affect the prognosis and quality of life of patients.Electroencephalogram(EEG)is the simplest way to diagnose epilepsy,and plays an important role in predicting seizures and guiding medication.AIM To explore the EEG characteristics of patients with post-stroke epilepsy and improve the detection rate of inter-seizure epileptiform discharges.METHODS From January 2017 to June 2020,10 patients with post-stroke epilepsy in our hospital were included.The clinical,imaging,and EEG characteristics were collected.The stroke location,seizure type,and ictal and interictal EEG manifestations of the patients with post-stroke epilepsy were then retrospectively analyzed.RESULTS In all 10 patients,epileptiform waves occurred in the side opposite to the stroke lesion during the interictal stage;these manifested as sharp wave,sharp-wave complex,or spike discharges in the anterior head lead of the side opposite to the lesion.CONCLUSION In EEG,epileptiform waves can occur in the side opposite to the stroke lesion in patients with post-stroke epilepsy.

Hurricane damage assessment for residential construction considering the non-stationarity in hurricane intensity and frequency

Natural hazards such as hurricanes may cause extensive economic losses and social disruption for civil structures and infrastructures in coastal areas, implying the importance of understanding the construction performance subjected to hurricanes and assessing the hurricane damages properly. The intensity and frequency of hurricanes have been reported to change with time due to the potential impact of climate change.In this paper, a probability-based model of hurricane damage assessment for coastal constructions is proposed taking into account the non-stationarity in hurricane intensity and frequency. The nonhomogeneous Poisson process is employed to model the non-stationarity in hurricane occurrence while the non-stationarity in hurricane intensity is reflected by the time-variant statistical parameters(e.g., mean value and/or standard deviation), with which the mean value and variation of the cumulative hurricane damage are evaluated explicitly. The Miami-Dade County, Florida, USA, is chosen to illustrate the hurricane damage assessment method proposed in this paper. The role of non-stationarity in hurricane intensity and occurrence rate due to climate change in hurricane damage is investigated using some representative changing patterns of hurricane parameters.

Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform

Classification of electroencephalogram(EEG)signals for humans can be achieved via artificial intelligence(AI)techniques.Especially,the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions.From this perspective,an automated AI technique with a digital processing method can be used to improve these signals.This paper proposes two classifiers:long short-term memory(LSTM)and support vector machine(SVM)for the classification of seizure and non-seizure EEG signals.These classifiers are applied to a public dataset,namely the University of Bonn,which consists of 2 classes–seizure and non-seizure.In addition,a fast Walsh-Hadamard Transform(FWHT)technique is implemented to analyze the EEG signals within the recurrence space of the brain.Thus,Hadamard coefficients of the EEG signals are obtained via the FWHT.Moreover,the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG recordings.Also,a k-fold cross-validation technique is applied to validate the performance of the proposed classifiers.The LSTM classifier provides the best performance,with a testing accuracy of 99.00%.The training and testing loss rates for the LSTM are 0.0029 and 0.0602,respectively,while the weighted average precision,recall,and F1-score for the LSTM are 99.00%.The results of the SVM classifier in terms of accuracy,sensitivity,and specificity reached 91%,93.52%,and 91.3%,respectively.The computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s,respectively.The results show that the LSTM classifier provides better performance than SVM in the classification of EEG signals.Eventually,the proposed classifiers provide high classification accuracy compared to previously published classifiers.

Functional role of frontal electroencephalogram alpha asymmetry in the resting state in patients with depression:A review

Depression is a psychological disorder that affects the general public worldwide.It is particularly important to make an objective and accurate diagnosis of depression,and the measurement methods of brain activity have gradually received increasing attention.Resting electroencephalogram(EEG)alpha asymmetry in patients with depression shows changes in activation of the alpha frequency band of the left and right frontal cortices.In this paper,we review the findings of the relationship between frontal EEG alpha asymmetry in the resting state and depression.Based on worldwide studies,we found the following:(1)Compared with individuals without depression,those with depression showed greater right frontal EEG alpha asymmetry in the resting state.However,the pattern of frontal EEG alpha asymmetry in the resting state in depressive individuals seemed to disappear with age;(2)Compared with individuals without maternal depression,those with maternal depression showed greater right frontal EEG alpha asymmetry in the resting state,which indicated that genetic or experience-based influences have an impact on frontal EEG alpha asymmetry at rest;and(3)Frontal EEG alpha asymmetry in the resting state was stable,and little or no change occurred after antidepressant treatment.Finally,we concluded that the contrasting results may be due to differences in methodology,clinical characteristics,and participant characteristics.

Serum neuronal pentraxin 2 is related to cognitive dysfunction and electroencephalogram slow wave/fast wave frequency ratio in epilepsy

BACKGROUND Cognitive dysfunction in epileptic patients is a high-incidence complication.Its mechanism is related to nervous system damage during seizures,but there is no effective diagnostic biomarker.Neuronal pentraxin 2(NPTX2)is thought to play a vital role in neurotransmission and the maintenance of synaptic plasticity.This study explored how serum NPTX2 and electroencephalogram(EEG)slow wave/fast wave frequency ratio relate to cognitive dysfunction in patients with epilepsy.AIM To determine if serum NPTX2 could serve as a potential biomarker for diagnosing cognitive impairment in epilepsy patients.METHODS The participants of this study,conducted from January 2020 to December 2021,comprised 74 epilepsy patients with normal cognitive function(normal group),37 epilepsy patients with cognitive dysfunction[epilepsy patients with cognitive dysfunction(ECD)group]and 30 healthy people(control group).The minimental state examination(MMSE)scale was used to evaluate cognitive function.We determined serum NPTX2 levels using an enzyme-linked immunosorbent kit and calculated the signal value of EEG regions according to the EEG recording.Pearson correlation coefficient was used to analyze the correlation between serum NPTX2 and the MMSE score.RESULTS The serum NPTX2 level in the control group,normal group and ECD group were 240.00±35.06 pg/mL,235.80±38.01 pg/mL and 193.80±42.72 pg/mL,respectively.The MMSE score was lowest in the ECD group among the three,while no significant difference was observed between the control and normal groups.In epilepsy patients with cognitive dysfunction,NPTX2 level had a positive correlation with the MMSE score(r=0.367,P=0.0253)and a negative correlation with epilepsy duration(r=−0.443,P=0.0061)and the EEG slow wave/fast wave frequency ratio value in the temporal region(r=−0.339,P=0.039).CONCLUSION Serum NPTX2 was found to be related to cognitive dysfunction and the EEG slow wave/fast wave frequency ratio in patients with epilepsy.It is thus a potential biomarker for the diagnosis of cognitive impairment in patients with epilepsy.

基于多尺度卷积和自注意力特征融合的多模态情感识别方法

基于生理信号的情感识别受噪声等因素影响,存在准确率低和跨个体泛化能力弱的问题。对此,提出一种基于脑电(EEG)、心电(ECG)和眼动信号的多模态情感识别方法。首先,对生理信号进行多尺度卷积,获取更高维度的信号特征并减少参数量;其次,在多模态信号特征的融合中使用自注意力机制,以提升关键特征的权重并减少模态之间的特征干扰;最后,使用双向长短期记忆(Bi-LSTM)网络提取融合特征的时序信息并进行分类。实验结果表明,所提方法在效价、唤醒度和效价/唤醒度四分类任务上分别取得90.29%、91.38%和83.53%的识别准确率,相较于脑电单模态和脑电/心电双模态方法,准确率上提升了3.46~7.11和0.92~3.15个百分点。所提方法能够准确识别情感,在个体间的识别稳定性更好。

定量化脑电图对前循环脑梗死血管重建术后患者预后判断的研究

目的探讨定量化脑电图对前循环脑梗死血管重建术后患者预后的判断价值。方法选取赣州市人民医院2022年1月至12月收治的89例前循环脑梗死患者作为研究对象,所有患者均行血管重建术治疗,并随访3个月,依据改良Rankin量表(mRS)评价患者预后,分为预后良好组及预后不良组;所有患者均行定量化脑电图检测,包括(θ+δ)/(α+β)比值(DTABR)、大脑对称指数(BSI),比较预后良好及预后不良组基础资料间差异。采用logistic回归分析影响前循环脑梗死血管重建术后预后的危险因素;绘制ROC曲线分析,定量化脑电图预测前循环脑梗死血管重建术后预后的临床价值。结果89例患者随访3个月,其中61例预后良好为预后良好组,28例预后不良为预后不良组。预后良好组DTABR、BSI为(0.84±0.11)、(0.09±0.02),低于预后不良组的(1.35±0.16)、(0.28±0.05),差异有统计学意义(P<0.05);logistic回归分析显示,DTABR、BSI为影响前循环脑梗死血管重建术后预后的高危因素(OR>1,P<0.05);ROC曲线显示,DTABR、BSI及联合检测预测前循环脑梗死患者血管重建术后预后的曲线下面积(AUC)分别为:0.850、0.833、0.965,联合检测价值更高。结论定量化脑电图在预测前循环脑梗死术后预后中具有较高价值,且操作简单、更为客观,可作为早期必检项目推广,以便于完善脑梗死的早期救治方案。

基于Partial New Causality的因果脑网络情绪识别

为了研究情绪产生过程中脑区以及通道之间的因果作用,在部分格兰杰与新型因果关系的基础上,提出一种用于研究时间序列之间因果关系的部分新型因果关系(PNC)方法。在不同情绪下选取脑区内的8个通道,用PNC计算脑区内通道之间的因果连接关系,根据连接关系构建因果网络;对因果网络中节点的信息流向和介数属性进行分析,将PNC因果网络和Granger因果网络节点之间的因果连接视为一种特征送入SVM中训练分类。实验结果表明,基于PNC因果网络和Granger因果网络的平均识别精度分别为76.4%和68.5%,PNC可用于计算时间序列之间的因果关系。

基于脑电的快速序列视觉呈现脑-机接口系统研究进展综述

脑-机接口(BCI)系统建立大脑与外部设备之间的直接交流通路,结合快速序列视觉呈现(RSVP)范式能够实现利用人类视觉系统进行高流通量图像目标检索。近些年来,RSVP-BCI系统在范式编码、脑电(EEG)解码和系统应用方面的研究取得了长足的进步。对范式编码的研究揭示不同范式参数对系统性能的影响,促进提升系统性能;脑电解码的研究在提升算法分类性能的同时推动少训练、零训练样本、多模态等场景下的应用;对RSVP-BCI系统应用的研究实现推动系统走向实际应用并拓宽了应用领域。同时,系统仍面临着迈向实际时可应用领域范围窄、脑电跨域解码难题以及计算机视觉飞速进步带来的挑战。该文对RSVP-BCI近年来的相关研究进展进行了回顾与总结,并对未来的发展方向进行了展望。

左乙拉西坦对儿童良性癫痫伴中央颞区棘波68例脑电图影响的研究

目的 探讨左乙拉西坦(LEV)对儿童良性癫痫伴中央颞区棘波(BECTS)患儿脑电图的影响。方法 收集我院2021年10月至2022年10月门诊诊断BECTS患儿资料,根据抗癫痫发作药物,分为对照组36例和研究组32例。对照组服用奥卡西平(OXC),研究组服用左乙拉西坦(LEV)。比较2组癫痫发作控制效果、脑电图痫样放电改善情况、不良反应发生情况。组间计数资料用χ^(2)检验,等级资料行秩和检验;计量资料用x±s表示,比较用t检验。结果 研究组BECTS控制效果优于对照组(Z=2.596,P<0.05)。研究组脑电图痫样放电改善效果优于对照组(Z=2.045,P<0.05)。研究组癫痫发作控制效果和脑电图痫样放电改善效果均优于对照组(P<0.05)。结论 LEV可有效控制患儿脑电图痫样放电频率和癫痫发作频率,安全有效。

射干制剂对失眠大鼠自主活动及TLR7/MyD88信号通路的影响

目的 探研射干制剂对PCPA致失眠大鼠自主活动、脑电波以及脑组织TLR7、MyD88基因表达的影响。方法 随机将70只健康SD大鼠分为空白对照组、模型对照组、射干制剂低剂量组(26 mg·kg^(-1))、射干制剂中剂量组(52 mg·kg^(-1))、射干制剂高剂量组(104 mg·kg^(-1))、朱砂安神丸组(1.62 g·kg^(-1))、地西泮片组(0.9 mg·kg^(-1))。荧光定量PCR检测各组TLR7、My D88基因的相对表达量;采用Etho Vision大小鼠行为学系统记录大鼠自主活动情况;通过无线遥测系统记录各组睡眠时脑电图。结果 与空白组对照比较,模型对照组脑组织TLR7、MyD88相对表达量显著升高(P<0.01);同模型对照组相比,射干制剂低剂量组、射干制剂中剂量组TLR7、MyD88的mRNA表达量均降低(P<0.05),射干制剂高剂量组TLR7、MyD88 mRNA表达量显著降低(P<0.01);与空白对照组相比,PCPA造模后大鼠运动量明显增加,不同剂量射干制剂组、朱砂安神丸组及地西泮片组运动有不同程度的减少,射干制剂高剂量组运动减少较为明显;给药120 min后,与空白对照组相比,模型对照组脑电波中的δ波百分比降低(P<0.05);与模型对照组相比,射干低中剂量组、朱砂安神丸组、地西泮片组δ波百分比均升高(P<0.05)。结论 射干制剂可有效抑制大鼠兴奋性,增加脑电图δ波百分比,改善睡眠,这些作用可能是通过调节脑组织TLR7和MyD88基因的表达来实现。

多层次时空特征自适应集成与特有-共享特征融合的双模态情感识别

在结合脑电(EEG)信号与人脸图像的双模态情感识别领域中,通常存在两个挑战性问题:(1)如何从EEG信号中以端到端方式学习到更具显著性的情感语义特征;(2)如何充分利用双模态信息,捕捉双模态特征中情感语义的一致性与互补性。为此,提出了多层次时空特征自适应集成与特有-共享特征融合的双模态情感识别模型。一方面,为从EEG信号中获得更具显著性的情感语义特征,设计了多层次时空特征自适应集成模块。该模块首先通过双流结构捕捉EEG信号的时空特征,再通过特征相似度加权并集成各层次的特征,最后利用门控机制自适应地学习各层次相对重要的情感特征。另一方面,为挖掘EEG信号与人脸图像之间的情感语义一致性与互补性,设计了特有-共享特征融合模块,通过特有特征的学习和共享特征的学习来联合学习情感语义特征,并结合损失函数实现各模态特有语义信息和模态间共享语义信息的自动提取。在DEAP和MAHNOB-HCI两种数据集上,采用跨实验验证和5折交叉验证两种实验手段验证了提出模型的性能。实验结果表明,该模型取得了具有竞争力的结果,为基于EEG信号与人脸图像的双模态情感识别提供了一种有效的解决方案。

基于CNN-NLSTM的脑电信号注意力状态分类方法

通过脑电信号进行注意力状态检测,对扩大脑-机接口技术的应用范围具有重要意义。为了提高注意力状态的分类准确率,该文提出一种基于CNN-NLSTM的脑电信号分类模型。首先采用Welch方法获得脑电信号的功率谱密度特征并将其表示为二维灰度图像。然后使用卷积神经网络从灰度图像中学习表征注意力状态的特征,并将相关特征输入到嵌套长短时记忆神经网络依次获得所有时间步骤的注意力特征。最后将两个网络依次连接来构建深度学习框架进行注意力状态分类。实验结果表明,该文所提出的模型通过进行多次5-折交叉验证评估后得到89.26%的平均分类准确率和90.40%的最大分类准确率,与其他模型相比具有更好的分类效果和稳定性。

基于脑电图的脑机接口技术在脑卒中患者上肢运动功能康复中的应用

背景:目前的康复方案对脑卒中后遗症的干预已取得不错的疗效,但治疗周期较长且人工成本较高。脑机接口技术通过特殊设备提取大脑神经活动信号,并将此信号转换处理为计算机可以识别的指令,可用于脑卒中后遗症的治疗。目的:分析和总结近些年脑机接口技术在脑卒中患者上肢运动功能康复中的应用,探讨脑机接口技术在脑卒中患者上肢功能康复中的临床应用价值。方法:以“脑卒中、脑电图、脑机接口、上肢、虚拟现实技术、功能性电刺激、外骨骼”为中文检索词,在中国知网进行相关文献检索;以“stroke、brain-computer interface、computer assistance、upper limb、virtual reality technology、functional electrical stimulation、exoskeleton”为英文检索词,在PubMed数据库进行相关文献检索,检索时间范围为2000-2022年。结果与结论:脑机接口对脑卒中患者上肢运动功能恢复具有良好的应用前景,并且被证明可以产生常规治疗无法实现的效果,非常值得进一步研究和推广,但是其机制尚未被完全阐释清楚。同时从脑机接口系统采集患者脑电信号的角度来看,准确地解码上肢运动的所有自由度以提供灵活和自然的控制能力仍然是一个挑战。未来的研究应该集中在阐明脑机接口技术促进脑卒中上肢运动恢复的特定神经机制,并确定脑机接口与外接设备相结合等康复方案,以促进脑卒中患者上肢运动功能恢复。

基于脑电节律能量与模糊熵的VR诱发晕动症水平检测研究

晕动症一直是影响虚拟现实用户体验及限制虚拟现实行业发展的一个关键因素。为解决这一问题,本文研究了虚拟现实晕动症对大脑神经活动的影响,并利用脑电特征对晕动症水平进行检测。为得到可度量眩晕水平的特征,记录受试者在体验眩晕测试场景前及过程中的脑电信号,计算节律能量和模糊熵,并利用统计分析进行特征选择,最后分类验证该特征的有效性。结果表明,受试者产生晕动症时,CP4和Oz的θ、α频段能量及C4的β、γ频段能量显著降低(p<0.01);在模糊熵方面,δ频段有FC4、Cz模糊熵值显著升高(p<0.0001),β频段有O1模糊熵值显著降低(p<0.0001)。对比线性判别分析(Linear discriminant analysis,LDA)、逻辑回归(Logistic regression,LR)和支持向量机(Support vector machine,SVM),K最近邻(K-nearest neighbor,KNN)算法的分类效果较好,它在节律能量和模糊熵上的分类准确率分别为89%和91%。本研究表明脑电节律能量及模糊熵有望成为晕动症水平检测的有效指标,为研究虚拟现实晕动症成因及缓解方案提供客观依据。

神经电生理技术在意识障碍患者分类诊断中的应用

部分意识障碍患者存在隐匿意识,仅依靠标准行为量表进行诊断误诊率较高,如何精确地分类诊断是临床所面临的巨大挑战.借助电生理技术将显著提高隐匿意识的检出率,为制定临床干预决策打下夯实基础.文章围绕中枢与周围神经电生理技术在意识障碍领域分类诊断中的应用,以敏感性等指标为主要结论,梳理应用潜力较大的范式与分析方法,便于临床工作者使用与推广.

儿童癫痫综合征智能分析:综述与展望

儿童癫痫综合征智能分析是指通过统计分析、机器学习等数据驱动方法,挖掘临床有效生物标志物,构建相应的专家系统,以解决临床和预后管理问题的研究.文中首先简述儿童癫痫综合征的定义、发作类型和分类等临床基础知识.然后,回顾基于脑电信号的儿童癫痫综合征智能分析框架和各组成部分典型方法存在的优缺点,包括数据收集及预处理、特征提取、决策器系统和专家系统.其中,将专家系统分为特定波形检测系统、诊断分类系统、发作检测系统、发作预测系统和量化评估系统,并进行全面概括与理论解释.最后,结合儿童癫痫综合征智能分析领域现有研究的局限性和挑战,展望未来研究方向,以推动儿童癫痫综合征智能分析系统的研究进展,减轻该病带来的负面影响.

基于心率变异性的自动睡眠分期及其脑电图验证

睡眠障碍会严重影响人们的日常生活,因此睡眠的早期监测对睡眠疾病的预防和诊断有重要意义。采用自行研制的便携式多导睡眠监护仪,开展了103人次的居家夜间睡眠数据收集(含脑电、眼电、肌电和心电信号)。然后,从同步采集的心电数据RR间期中提取时域、频率和非线性特征,组合出最高达426个心率变异性(HRV)特征,基于Xgboost算法构建模型对睡眠中的清醒期(wake)、非快速眼动|期(N1)、非快速眼动Ⅱ期(N2)、非快速眼动Ⅲ期(N3)和快速眼动期(REM)进行五分类(wake、N1、N2、N3、REM)、三分类(wake+N1、REM、N2+N3)和二分类(wake、N1+N2+N3+REM)预测,并与脑电图睡眠分期标签进行验证。最后,五分类、三分类和二分类测试结果准确率分别达到84.0%、89.1%和95.2%,F1-score达到83.2%、88.9%和94.9%,为同类模型研究中表现最佳。说明HRV与睡眠阶段具有良好的相关性,基于便携式设备收集数据构建的算法模型可以较好地识别睡眠状态。

围术期神经认知障碍的中枢机制:基于脑电图和磁共振成像的研究进展

围术期神经认知障碍(PND)是围术期常见疾病,给患者及社会带来沉重负担。由于PND的发病机制复杂且缺乏可靠的诊断及干预手段,而脑电图(EEG)及磁共振成像(MRI)具备提供客观指标的优势,因此两者应用于PND的研究逐渐成为热点。本文对PND患者术中量化脑电指标、EEG频谱分析、EEG功能连通性分析、EEG非线性动力学分析以及围术期MRI分析进行综述,旨在探索EEG和MRI对预测及诊断PND的临床价值。

EEG-fNIRS技术在神经精神疾病研究中的应用进展

通过脑电图(EEG)、功能近红外光谱成像(fNIRS)、功能磁共振成像对神经精神疾病已经开展广泛的研究与应用。近年来随着技术的不断发展,EEG与fNIRS的同步采集设备被开发且逐步应用于神经精神疾病的研究中。本文首先对EEG-fNIRS设备的同步检测与数据分析技术进行简要概述,总结脑卒中、癫痫以及其他神经精神疾病研究中EEG-fNIRS的分析方法与最新发现,并探讨研究的发展方向。