基于EEG信号分析处理的癫痫预报及研究进展

癫痫是一种常见的脑部疾病,对癫痫发作进行预报具有重要的临床意义。本文回顾了基于EEG信号分析处理的癫痫预报的历史,并综述了时域、频域、非线性动力学和智能分析技术在发作预报上的应用。     

Application of wavelet-based similarity analysis to epileptic seizures prediction

Epileptic seizures prediction is an interesting issue in epileptology, since it can promise a novel approach to control seizures and understand the mechanism of epileptic seizures. In this paper, we describe a new method, called wavelet-based nonlinear similarity index (WNSI), to predict epileptic seizures using EEG recordings in real time. This method combines wavelet techniques and nonlinear dynamics. The test results of EEG recordings of rats and humans show that WNSI can track the hidden dynamical changes of brain electrical activity. Particularly, we found that it can obtain the best performance of seizure prediction at the beta (10-30 Hz) frequency band of EEG signals. A possible reason is suggested from the functional connectivity of the brain. In terms of this study, it is recommended that wavelet technique is very useful to improve the performance of epileptic seizures prediction.     

癫癎发作前期脑电变化特征分析

目的 :探讨癫临床发作前期的脑波变化特征。方法 :采用Video -EEG监护系统对 82例癫患者进行脑电和行为监测 ,监护系统自动记录发作时的行为表现及脑电变化。结果 :82例中共监测到 16例临床发作 ,发作期脑波被大量肌电干扰不易分辨 ,发作前期脑波有如下几种表现 :①背景波先变为低幅快波 ,波幅渐升高 ,或背景为低幅慢波 ,发作前 5～ 14秒内波幅升高频率增快 ,但仍为慢波 ;②发作前背景节律变慢 ,波幅升高 ;③发作前背景节律不变 ,仅波幅明显升高 ;④出现样放电波形。结论 :癫发作前脑波频率增快或波幅升高 ,应视为与癫发作有密切关系的现象。     

Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application

Electroencephalography (EEG) is widely used in clinical settings to investigate neuropathology. Since EEG signals contain a wealth of information about brain functions, there are many approaches to analyzing EEG signals with spectral techniques. In this study, the short-time Fourier transform (STFT) and wavelet transform (WT) were applied to EEG signals obtained from a normal child and from a child having an epileptic seizure. For this purpose, we developed a program using Labview software. Labview is an application development environment that uses a graphical language G, usable with an online applicable National Instruments data acquisition card. In order to obtain clinically interpretable results, frequency band activities of delta, theta, alpha and beta signals were mapped onto frequency-time axes using the STFT, and 3D WT representations were obtained using the continuous wavelet transform (CWT). Both results were compared, and it was determined that the STFT was more applicable for real-time processing of EEG signals, due to its short process time. However, the CWT still had good resolution and performance high enough for use in clinical and research settings.     

儿童失神癫(癎)脑电图的多尺度特征

目的:研究儿童失神癫癎脑电图的多尺度定量特征。方法:对15例失神癫癎患儿10次临床发作和20次亚临床癎样放电的脑电图进行子波分析,提取失神癫癎发作过程中脑电信号的多尺度定量典型特征,与发作前10 s及发作后10 s的脑电信号进行比较,并与12例正常同龄儿童脑电图进行比较。结果:研究显示儿童失神癫癎发作过程中脑电信号的多尺度典型特征主要表现为12尺度(对应频率3 Hz)的节律性活动显著增强,发作时20尺度(低频大尺度,对应频率0．12 Hz)结构与频率3 Hz的结构具有非正常的跳跃式尺度关系,3 Hz节律性棘慢复合波与大尺度(频率1 Hz以下)背景低频放电结构共同存在。发作过程中分尺度功率主要集中在20尺度和12尺度,其演变规律为20尺度能量逐渐减低,12尺度能量逐渐增加。10次临床发作的脑电信号均显示上述特征。发作前10 s和后10 s的脑电多尺度信号中仍然存在隐性的3 Hz棘慢复合波成分,与一般认为3 Hz棘慢复合波突起突止不同．而从传统的脑电图上无法分辨出发作前后的这些多尺度细节的定量特征。亚临床癎样放电的多尺度特征与发作期无明显差别,但持续时间短。结论:子波分析作为一种新的信号分析方法,适合于脑电信号的分析,可以获得比传统视觉脑电图更多的定量信息。通过对失神癫癎患儿的脑电信号进行子波分析,得到其发作过程中典型的多尺度定量特征,有助于失神癫癎发作的临床辅助诊断、预后评价以及神经电生理机理的基础研究。     

癫痫性痉挛发作的头皮及颅内脑电图特点

目的：研究难治性癫痫性痉挛发作患者的头皮及颅内脑电图（EEG）特点,探讨与痉挛发作相关的EEG变化及其与发作间期放电、神经影像学之间的关系。方法：回顾性分析经外科手术治疗的11例患者的临床资料,分析头皮同步视频脑电图（V-EEG）。此11例患者均行术中皮层EEG监测30～60min,其中4例术前行颅内电极长程EEG监测。结果：8例患者表现为双侧基本对称的痉挛发作,发作期头皮EEG为全导高波幅慢波、尖波伴低波幅快波活动或广泛低波幅快波活动发放;另3例患者表现为一侧肢体的痉挛发作,EEG为局灶性棘慢波发放。术中皮层监测5例患者为反复的、暴发出现的多棘波活动,2例患者见持续性的棘波、尖波活动,4例未见明显的癫痫样电活动。4例行颅内电极监测者发作期EEG表现,2例为“前导性”的高波幅棘波伴随20Hz左右的低波幅快波发放;另2例为局灶性低波幅快波活动并迅速扩散,无“前导性棘波”。手术切除“前导性棘波”或反复性、节律性痫样放电的皮层可消除发作。结论：在一部分癫痫性痉挛发作患者,其痉挛发作可能因新皮层局灶的电发放点燃,颅内EEG如果存在前导性的棘波,这个棘波部位可能是促发痉挛发作的点燃灶。完整切除术中监测呈现反复性、节律性痫样放电的皮层可取得较好的手术效果。     

老年人迟发性癫Xian发作的临床及脑电图分析

目的：探讨老年人迟发性癫Xian发作的临床及脑电图特点。方法：回顾性分析80例老年人迟发性癫Xian的临床及脑电图资料。结果：癫Xian发作的可能因为脑血管病41例（脑梗死30例、脑出血11例）,脑肿瘤19例,脑外伤4例,脑萎缩8例。癫Xian发作的类型为全身强直阵挛发作48例;强直阵挛发作持续状态2例,失神发作6例,单纯运动性发作17例,单纯体感性发作7例。脑电图正常7例,异常73例。异常脑电图主要表现为弥漫性慢波活动22例,局限于一侧半球的慢波活动34例,散在或阵发性棘波、尖波或棘慢、尖慢综合波49例。结论：脑血管病（脑梗死、脑出血）、脑肿瘤是老年迟发性癫Xian发作的主要原因。癫Xian发作以全身强直阵挛发作为主。脑电图异常率高,主要表现为在弥漫性慢波活动基础上出现癫Xian样放电。     

Comparison of subspace-based methods with AR parametric methods in epileptic seizure detection

Electroencephalography is an important clinical tool for the evaluation and treatment of neurophysiologic disorders related to epilepsy. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. In this study, we have proposed subspace-based methods to analyze and characterize epileptiform discharges in the form of 3-Hz spike and wave complex in patients with absence seizure. The variations in the shape of the EEG power spectra were examined in order to obtain medical information. These power spectra were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of epileptic seizure. Global performance of the proposed methods was evaluated by means of the visual inspection of power spectral densities (PSDs). Graphical results comparing the performance of the proposed methods with that of the autoregressive techniques were given. The results demonstrate consistently superior performance of the proposed methods over the autoregressive ones.     

伴发脑病的成人全面性癫癎电一临床分析

目的对伴发脑病的全面性癫癎成年病人作电生理学与临床症状学分析。方法：入选标准：①18岁以上明确诊断全面性癫癎的患者;②行简易智能精神状态检查量表（MMSE）及日常生活活动能力量表（ADL）检查明确有精神发育迟滞及不同程度残障的癫癎患者,进行了长时间视频脑电图＋肌电多导仪（以下简称长时间V-EEG＋EMG多导仪）监测,记录间歇期EEG及发作时同步V—EEG＋EMG。结果：符合条件的6例患者共记录到64次临床发作：轴肌强直发作19次,短暂性强直发作8次,不典型失神发作1次,全面性强直阵挛发作2次,全面性强直阵挛发作2次,轴肌肌阵挛发作20次,眼睑肌阵挛10次,右侧上肢肌阵挛发作1次,无法分类的发作1次。6例患者中有5例为中度,1例为重度精神发育迟滞。结论：年龄依赖性癫癎性脑病患者发病早期的诊断及癫癎发作分型并进行长期的电生理学临床症状学随访到成年是必要的。     

癫癎发作时心率和心电图变化及其影响因素

目的:探讨癫患者癫发作时心率和心电图(ECG)变化及其影响因素。方法:选择在我院癫中心进行录像脑电/心电(VEEG/ECG)监测时有癫发作的患者46例,对其癫发作时的EEG、ECG及其行为学进行分析。结果:在共106次发作中,97次(91.5%)癫发作时心率加快,发作时心率平均增加58.6次/min,癫发作时心率最快达182次/min。19例(41.3%)患者在38次(35.8%)的癫发作时伴有ECG异常改变,主要表现为房性早搏、房室传导阻滞、房颤、心脏停搏、ST段降低、ST段抬高、T波倒置。全身性发作、颞叶癫以及在睡眠中发作三个因素对癫发作时的心率变化有一定的影响。结论:癫患者发作时心率和ECG有明显变化,这种变化可能与癫患者突然意外死亡有关。     

癫痫发作预测研究的新进展

癫痫是一种慢性神经系统紊乱的疾病 ,发病率约为 0 .5 %～ 2 % ,其中 10 %～ 5 0 %的患者药物治疗无效或不适于进行手术。对这些患者来说 ,不可控制的发作是造成残疾甚至死亡的主要原因。因此 ,研究癫痫发作的预测方法 ,使患者或医生提前采取预防措施 ,减小发作时对病人的伤害是十分有意义的。本文介绍了癫痫发作预测的可行性 ,研究现状、存在的问题以及癫痫预测可能的应用领域     

儿童癫痫发作与睡眠觉醒周期的关系

目的：研究儿童癫痫发作与睡眠觉醒周期的关系。方法：经同步录像脑电图（V—EEG）监测,对142例儿童癫痫发作期的临床表现与V-EEG进行同步分析。结果：142例患儿中94例（66．2％）患儿的发作与睡眠觉醒周期相关。此94例患儿的154次发作中,思睡期36次（23．4％）,睡眠I-Ⅱ期64次（41．6％）,睡眠Ⅲ-Ⅳ期10次（6．5％）,REM期1次（0．6％）,觉醒后43次（27．9％）;发作类型中,部分性发作或伴泛化全面性发作81次（52．6％）,痉挛发作27次（17．5％）,肌阵挛25次（16．2％）,强直发作11次（7．1％）,失神发作7次（4．6％）,强直阵挛发作3次（2．0％）。结论：儿童癫痫发作与睡眠觉醒周期密切相关,了解发作与睡眠觉醒周期的关系,既有助于更好地管理和治疗癫痫患儿,又有助于合理安排EEG检查的方式和时机,提高检查阳性率,确定发作类型。     

基于小波变换的脑电图癫痫波形检测

脑电图中癫痫波形的自动检测与分类是临床上很有意义的工作。我们根据脑电图中的癫痫特征波形，利用小波变换的时频局部化特性，给出了一种高效的癫痫波表的自动检测方法，构造了一个连续的癫痫波检测系统。通过检测不同尺度上的局部极大值，确定出对应的脑电图中的锐变点位置，并由此检测出脑电图中的癫痫波，从初步临床试验的结果来看，系统具有检测精度高，可连续作业等优点，获得了较好的效果。     

Reduction of epileptic seizures through EEG biofeedback training

Biofeedback training of the sensorimotor rhythm (SMR) was carried out in three male and three female adolescent epileptics and in two normal controls. The patients represented a cross-section of epilepsies including grand mal, myoclonic, afocal and psychomotor types. Three of the cases were mentally retarded. 12–14 Hz (SMR) activity was detected by a combination of sharp analog filtering and digital processing. The patients were provided with feed-back whenever they produced 0.5 sec of 12–14 Hz activity of a specified amplitude. Additional feedback was provided for epileptiform activity slow waves or movement. Furthermore, feedback for SMR production was inhibited by digital logic circuitry when movement, slow waves or spikes were present. Seizure reduction was obtained in five of the six epileptics. Several patients showed increased percentage of SMR when feedback was provided and varying degrees of normalization in their EEG as demonstrated by fast Fourier, crossed power spectral density and coherence analyses.     

额叶癫痫发作的临床与脑电图特征

目的 :分析额叶癫发作的临床及EEG特征。方法 :经同步录像脑电图 (Video—EEG)监测 ,对 40例癫病人 181次额叶发作的临床表现及EEG进行同步分析。结果 :额叶发作频繁而短暂 ,以睡眠中发作为主。常见的临床表现依次为过度运动、扭转性强直、姿势性强直、发声、假性失神等。发作间期额区棘、尖波稀少且波形不典型 ,发作期额叶限局性或弥漫性的改变与背景活动的差别不明显。结论 :临床和EEG不典型是导致额叶发作临床诊断困难或误诊的主要原因。认识额叶发作的临床特点 ,延长EEG记录时间及发作期临床—EEG同步分析有助于对额叶发作的诊断。     

基于脑电信号分析的癫痫特征检测方法及研究进展

癫痫特征的自动检测在临床上有很重要的意义,可以减轻医疗工作者的劳动量。本文综述和分析了癫痫特征检测的各种方法,包括非线性滤波、模板匹配、拟态法等传统的方法和小波变换、神经网络等近年发展起来的新方法。     

基于EEG模糊相似性的癫痫发作预测

本研究提出基于EEG序列模糊相似性指数方法预测癫痫发作.首先,结合复自相关法和Cao法对EEG序列进行了相空间重构;然后,计算相关积分时用Gaussian函数代替Heavyside函数,克服了Heavyside函数的刚性边界问题,使得计算相似性指数更加准确和可靠;最后,分析大鼠癫痫EEG信号,检测癫痫发作前期状态.分析结果表明模糊相似性指数方法能够比动态相似性指数方法获得更长的预测时间和更低的错误预测率.     

Detection of artifacts from high energy bursts in neonatal EEG

Detection of non-cerebral activities or artifacts, intermixed within the background EEG, is essential to discard them from subsequent pattern analysis. The problem is much harder in neonatal EEG, where the background EEG contains spikes, waves, and rapid fluctuations in amplitude and frequency. Existing artifact detection methods are mostly limited to detect only a subset of artifacts such as ocular, muscle or power line artifacts. Few methods integrate different modules, each for detection of one specific category of artifact. Furthermore, most of the reference approaches are implemented and tested on adult EEG recordings. Direct application of those methods on neonatal EEG causes performance deterioration, due to greater pattern variation and inherent complexity. A method for detection of a wide range of artifact categories in neonatal EEG is thus required. At the same time, the method should be specific enough to preserve the background EEG information. The current study describes a feature based classification approach to detect both repetitive (generated from ECG, EMG, pulse, respiration, etc.) and transient (generated from eye blinking, eye movement, patient movement, etc.) artifacts. It focuses on artifact detection within high energy burst patterns, instead of detecting artifacts within the complete background EEG with wide pattern variation. The objective is to find true burst patterns, which can later be used to identify the Burst-Suppression (BS) pattern, which is commonly observed during newborn seizure. Such selective artifact detection is proven to be more sensitive to artifacts and specific to bursts, compared to the existing artifact detection approaches applied on the complete background EEG. Several time domain, frequency domain, statistical features, and features generated by wavelet decomposition are analyzed to model the proposed bi-classification between burst and artifact segments. A feature selection method is also applied to select the feature subset producing highest classification accuracy. The suggested feature based classification method is executed using our recorded neonatal EEG dataset, consisting of burst and artifact segments. We obtain 78% sensitivity and 72% specificity as the accuracy measures. The accuracy obtained using the proposed method is found to be about 20% higher than that of the reference approaches. Joint use of the proposed method with our previous work on burst detection outperforms reference methods on simultaneous burst and artifact detection. As the proposed method supports detection of a wide range of artifact patterns, it can be improved to incorporate the detection of artifacts within other seizure patterns and background EEG information as well.