Benign Rolandic epilepsy in children. Topographic EEG analysis

Topographic EEG investigation with instant voltage mapping showed maximal negativity of "Rolandic" spikes over central or midtemporal electrodes with spread to parietal or upper frontal areas with a dipol formation (centro-temporal negativity, frontal positivity). Spike amplitude or duration was not correlated with spread to adjacent areas. No other focal abnormalities, such as focal slowing, occurred. Spike activity was pronounced during light sleep and was often associated with generalized spike wave activity, which is more likely to be a sign of functional disturbance rather than the sequelae of brain damage. A review of literature shows that this pattern is helpful in the differentiation from focal abnormalities due to brain lesions.     

基于渥太华决策支持框架的双J管留置患者自我管理健康教育视频内容构建

目的建立科学、合理、易于患者理解掌握的自我管理视频健康教育内容,为留置双J管患者的健康教育提供依据。方法采用文献资料调研、访谈、成立讨论小组等方法对自我管理健康教育进行分析、总结,逐步构建自我管理健康教育视频内容,运用德尔菲专家咨询法对各级指标进行2轮意见征询,确定双J管留置患者自我管理健康教育视频内容及权重。结果 2轮专家函询中,专家的积极性分别为80%和100%,权威系数为0.903,一级指标Kendall′s W为0.827;二级指标的重要性和合理性Kendall′s W分别为0.504、0.672(P<0.05),最终形成一级指标2项,二级指标14项的内容框架。结论本研究构建的泌尿系结石留置双J管患者自我管理健康教育视频内容框架科学,易于患者接受,可根据该内容框架制作健康教育视频。     

Decision analysis: A framework for critical care decision assistance

The ultimate goal of medical computer systems is to help clinicians make good decisions. Such systems must be based on sound principles. Decision analysis is a 25-year-old discipline that provides the needed rigorous foundation for decision assistance. Decision analysis comprises the philosophy, procedures, and tools that can correct the flaws in existing critical care decision-making practice. Intelligent decision systems — computer-based systems that automate decision analysis — make it practical to apply decision analysis to critical care.Orchestra is a pilot intelligent decision system (now under development) that coordinates the efforts of the critical care specialist, the bedside physician, and the bedside nurse in building decision models that can provide recommendations and insight for ventilator management decisions. Decision analysis delivered by intelligent decision systems has great potential for improving critical care decision-making.     

癫痫患儿视频脑电图的监测与护理

视频脑电图(Video-electroen cephalngram,V-EEG)已广泛应用于临床,在癫痫的诊断和鉴别诊断中发挥了不可替代的作用.我科室于2006年6月～2007年6月对245例患儿进行了V-EEG监测.V-EEG监测的护理关键问题是监测前要做好患儿和家长的准备工作,取得家长及患儿的积极配合.在整个监测过程中,要及时发现和处理监测过程中出现的各种问题,正确识别伪差,为医生的诊断提供可靠的依据.     

A regularized discriminative framework for EEG analysis with application to brain–computer interface

We propose a framework for signal analysis of electroencephalography (EEG) that unifies tasks such as feature extraction, feature selection, feature combination, and classification, which are often independently tackled conventionally, under a regularized empirical risk minimization problem. The features are automatically learned, selected and combined through a convex optimization problem. Moreover we propose regularizers that induce novel types of sparsity providing a new technique for visualizing EEG of subjects during tasks from a discriminative point of view. The proposed framework is applied to two typical BCI problems, namely the P300 speller system and the prediction of self-paced finger tapping. In both datasets the proposed approach shows competitive performance against conventional methods, while at the same time the results are easier accessible to neurophysiological interpretation. Note that our novel approach is not only applicable to Brain imaging beyond EEG but also to general discriminative modeling of experimental paradigms beyond BCI.     

A collaborative model of service delivery for children with movement disorders: a framework for evidence-based decision making

Models of physical therapist service delivery provide a framework for integration of knowledge, research, and assumptions in a clinically relevant context that facilitates evidence-based decision making. In this perspective, a collaborative model of service delivery for children with movement disorders is presented. The focus is on services that address child and family priorities and preferences in settings where children live, learn, and play. The International Classification of Functioning, Disability and Health (ICF) is applied to identify relationships among the components of functioning, environmental, and personal factors that are important for the plan of care and achievement of outcomes. An assumption of the model is that physical therapists use multiple types of evidence to guide decision making. Application of the model and how child and family priorities change over time are illustrated through a longitudinal case report of a child with cerebral palsy.     

伴中央-颞区棘波的儿童良性癫痫临床与脑电图分析

目的 探讨伴中央颞区棘波儿童良性癫痫（benign ctfildhood epilepsy with centro-temporal spikes，BECT）的临床特点、脑电图（EEG）改变及预后情况。方法 采用数字视频脑电图仪和动态脑电监测，对31例BECT患儿进行清醒和睡眠脑电图描记，结合临床表现及影像学资料进行随访分析。结果 本组发病年龄为2．5岁-14岁，其中5—10岁发病24例（占77．42％），发作均与睡眠有关，2例发病后有语言障碍。发作间期EEG背景波正常，在一侧或双侧中央区和中颞区出现负性、双向或多向的棘波或尖波，或棘慢综合波，入睡后放电频率明显增加。影像学检查有1例侧脑室扩大，其余正常。结论 BECT多见于5—10岁儿童，临床发作与睡眠密切相关，EEG有特征性改变，表现为一侧或双侧中央和／或中颞区有尖波、棘波或棘慢综合波发放。抗癫痫药物治疗有效，大部分预后良好，2例有认知功能障碍。     

Clinical analysis of 19 children with refractory epilepsy

Objective:The refractory epilepsy in children is hopeful to be controlled by etiolopgical treatment with the modern diagnosis technology,We have collected 19 cases with refractory epilepsy treated with modern diagnosis technology and performed summary and analysis of the diagnosis as follow.Result:under the help of modern diagnosis technology,the causes of some refractory epilepsy became clear,including 1 pituitary tumor.1 pachygria,2 tuberous sclerosis,1 Yito hypoigmentation,2 local soft focuses after viral encephalitis,1 phenylketonurea.1 third glutaric acidemia,2 ischemia anoxic encephalopathy sequelas,3 infantile spasm,and 5 no-cause lesion.After operation,one patient with pituitary tumor accepted anti-epilepsy drug,and seizure could be controlled.One patient with brain soft lesion got remission by operation treatment,and one with phenylketonurea was advised by low phenylalaine diet combined with anti-epilepsy drugs,whose conditions got remission.The casuses or brain developmental defect,and there was effctive therapy method at present.But clear causes were helpful to diagnosis and evaluate prognosis.Conclusion:It is useful to treat the refractory epilepsy by finding the cause of the disease and eliminating the cause of the disease with the modern diagnosis technology.     

小儿难治性癫痫19例分析

Background: At present, 70% ～ 80% epilepsy in children can be controlled by treatment, but 20% ～ 30% children with the disease appeared seizure repeatedly.The disease can be controlled hardly with common drugs,and quality of life is poor in the children.     

婴幼儿癫痫的临床和脑电图特点分析

目的研究各型婴幼儿癫痫发作特征、脑电图、病因、神经影像学的改变,探讨早期诊断方法及预后。方法对我院2003年6月-2008年1月门诊和住院诊治的125例3岁以内起病的婴幼儿癫痫患儿进行临床观察及脑电图监测,并进行CT或MRI等神经影像学及血生化、尿遗传代谢病筛查等辅助检查,随访治疗效果和远期预后。结果本组1岁以内发病占54．4％。全面性发作占37．6％,局灶性发作占53．6％,不能明确分类的发作占8．8％。症状性癫痫占58．4％,其中围生期异常是第一位病因,占症状性癫痫的50．68％;特发性癫痫占41．6％。脑电图背景活动正常占53．6％,异常占46．4％,发作问期EEG正常占24．8％,异常占75．2％。影像学检查异常占38．4％。结论婴幼儿以症状性癫痫多见,局灶性发作所占的比例高,发作形式多样,应与非癫痫性发作鉴别,视频脑电图检查对诊断及鉴别有重要意义。     

A single-trial analytic framework for EEG analysis and its application to target detection and classification

Modern neuroimaging technologies afford a non-invasive view into the functions of the human brain with great spatial (fMRI) and temporal resolution (EEG). However, common signal analytic methods require averaging over many trials, which limits the potential for practical application of these technologies. In this paper we advance a novel single-trial analysis method for EEG and demonstrate this approach with a target detection task. The method utilizes a framework consisting of multiple processing modules that can be applied in whole or in part, including noise mitigation, source-space transformation, discriminant analysis, and performance evaluation. The framework introduces an enhanced noise mitigation technology based on Directed Components Analysis (DCA) that improves upon existing spatial filtering techniques. Source-space transformation, utilizing a finite difference model (FDM) of the human head, estimates activity measures of the cortical sources involved in task performance. Such a source-space discrimination provides measurement invariance between training and testing sessions and holds the promise of providing a degree of classification not possible with scalp-recorded EEG. The framework's discrimination modules interface with performance evaluation modules to generate classification performance statistics. When applied to EEG acquired during performance of a target detection task, this method demonstrated that neural signatures of target recognition correctly classified up to 87% of targets in a rapid serial visual presentation (RSVP) of target/non-target images. On average, the single-trial classification method resulted in greater than 60% improvement over behavioral performance for target detection.     

A confident decision support system for interpreting electrocardiograms

Computer-aided interpretation of electrocardiograms (ECGs) is widespread but many physicians hesitate to rely on the computer, because the advice is presented without information about the confidence of the advice. The purpose of this work was to develop a method to validate the advice of a computer by estimating the error of an artificial neural network output. A total of 1249 ECGs, recorded with computerized electrocardiographs, on patients who had undergone diagnostic cardiac catheterization were studied. The material consisted of two groups, 414 patients with and 835 without anterior myocardial infarction. The material was randomly divided into three data sets. The first set was used to train an artificial neural network for the diagnosis of anterior infarction. The second data set was used to calculate the error of the network outputs. The last data set was used to test the network performance and to estimate the error of the network outputs. The performance of the neural network, measured as the area under the receiver operating characteristic (ROC) curve, was 0·887 (0·845–0·922). The 25% test ECGs with the lowest error estimates had an area under the ROC curve as high as 0·995 (0·982–1·000), i.e. almost all of these ECGs were correctly classified. Neural networks can therefore be trained to diagnose myocardial infarction and to signal when the advice is given with great confidence or when it should be considered more carefully. This method increases the possibility that artificial neural networks will be accepted as reliable decision support systems in clinical practice.     

住院患儿疼痛管理决策支持系统的构建与应用研究

目的构建住院患儿疼痛管理决策支持系统,并评价其应用效果。方法以疼痛管理理论与临床决策支持技术为基础,构建集护理程序各环节、医嘱处理和护理任务清单为一体的疼痛管理信息系统。于2018年9月—2019年11月在某三级甲等综合性儿科医院进行系统试运行,比较系统使用前后的护理文书书写质量、医嘱处理及时性及护士使用体验。结果住院患儿疼痛管理决策支持系统使用后,护理文书书写质量各指标均明显提高,与原疼痛管理系统比较,差异具有统计学意义(P<0.05);医嘱处理及时性使用前后均为100%,差异无统计学意义(χ²=0.012,P=0.913);在临床护士使用体验中,使用后"信息质量""服务质量""用户满意""净收益"维度体验性均提高,与原疼痛管理系统比较,差异具有统计学意义(P<0.05)。结论住院患儿疼痛管理决策支持系统的使用提高了住院患儿疼痛管理的同质性、连续性、完整性、及时性,且护士对该系统总体体验良好。     

A unified Bayesian framework for MEG/EEG source imaging

The ill-posed nature of the MEG (or related EEG) source localization problem requires the incorporation of prior assumptions when choosing an appropriate solution out of an infinite set of candidates. Bayesian approaches are useful in this capacity because they allow these assumptions to be explicitly quantified using postulated prior distributions. However, the means by which these priors are chosen, as well as the estimation and inference procedures that are subsequently adopted to affect localization, have led to a daunting array of algorithms with seemingly very different properties and assumptions. From the vantage point of a simple Gaussian scale mixture model with flexible covariance components, this paper analyzes and extends several broad categories of Bayesian inference directly applicable to source localization including empirical Bayesian approaches, standard MAP estimation, and multiple variational Bayesian (VB) approximations. Theoretical properties related to convergence, global and local minima, and localization bias are analyzed and fast algorithms are derived that improve upon existing methods. This perspective leads to explicit connections between many established algorithms and suggests natural extensions for handling unknown dipole orientations, extended source configurations, correlated sources, temporal smoothness, and computational expediency. Specific imaging methods elucidated under this paradigm include the weighted minimum l(2)-norm, FOCUSS, minimum current estimation, VESTAL, sLORETA, restricted maximum likelihood, covariance component estimation, beamforming, variational Bayes, the Laplace approximation, and automatic relevance determination, as well as many others. Perhaps surprisingly, all of these methods can be formulated as particular cases of covariance component estimation using different concave regularization terms and optimization rules, making general theoretical analyses and algorithmic extensions/improvements particularly relevant.     

120例癫痫及可疑癫痫患者临床与动态脑电图分析

目的探讨动态脑电图对癫痫及可疑癫痫患者临床诊断价值。方法对67例癫痫患者和53例可疑癫痫患者行24h动态脑电图监测，结合临床进行分析。结果癫痫组痫样放电阳性率65．7％，可疑癫痫组痫样放电阳性率34．0％。85．5％的痫样放电出现在睡眠期，睡眠中又以非快速眼球运动（NREM）期明显。痫样放电以尖波为主，部位以多灶为主。结论动态脑电图监测可对癫痫及可疑癫痫的诊断提供重要依据，但对癫痫发作的分型、癫痫源的定位作用有限。