脑电图描记中伪差的识别和消除

在脑电图（EEG）实际操作中经常见到各种伪差,它是指来自脑外的电位活动在脑电图中的反应。一份EEG诊断正确与否不仅取决于解释记录的水平,更直接取决于记录质量的优略。因此,掌握识别伪差．了解它产生的原因和消除的方法是一项必不可少的基础技术知识。现按照伪差的来源分为以下3方面。     

ND-82B脑电图机产生伪差的原因分析

脑电图中的干扰又称伪差 ,干扰是来自脑外电活动在脑电图中的反映。干扰主要来源于受试者 ,空气传来的和脑电图机及其附件 ,除了来自脑电图机及其附件 ,操作使用人员、病人检查前先洗头、净化空气等应尽力排除。脑电图机及其附件故障带来的伪差有下列几种 :(1)记录笔产生的伪差。笔齐步不一 ,可使记录的脑电图产生假性位相差 ;笔的位置不正可致波形失真 ;因记录笔的线圈磁力减小 ,笔安装过紧和机械阻尼过大使摆幅过小 ,造成稍高波幅的波呈平顶样波。(2 )走纸速度不恒定的伪差。记录纸位置不适当及马达或调整部分的故障使走纸速度不恒定 ,造…     

频发室上性期前收缩和呼吸肌肌电伪差并存酷似心房分离一例

有关心房分离(AD)的误诊，虽已经过1984年国内心电学界的大讨论，但仍有强调的必要。现报道1例频发室上性期前收缩和呼吸肌肌电伪差并存酷似AD的患者。     

动态心电图的伪差、误判与防范措施

24h动态心电图(DCG)作为一种无创伤性心脏电生理检查方法,已广泛应用于临床。诸多因素均可影响DCG的自动分析结果,即使DCG分析系统本身运转正常,也仍然会产生一些伪差和误判。我科室引进美国三导联和十二导联动态心电图各一台,将我科室2002——2012年3600例DCG检测中,有明显伪差和误判且记录完整的980例进行分析,并探讨防范措施。     

OCT图像运动伪差校正算法的研究

目的光学相干层析成像系统（Optical Coherence Tomography, OCT）进行动态成像时,运动伪差会使图像产生畸变,从而可能导致临床诊断的误诊以及错诊,运动伪差的校正已经是亟待解决问题。方法首先用改进的自适应非线性复合扩散滤波器对连续采集的序列图像进行预处理降噪,然后用图像的质量重心对畸变的体数据在Y方向进行粗略的对齐,再用差值平均法对Y方向的运动伪差进行校正。结果经过校正,纵切2D图像毛刺消失,表面平滑;3D图像表面平滑,结构清晰,层次分明,视网膜en-face单层切片图像清晰,可以观察到眼底组织细微结构。结论经过运动伪差校正后,眼底视网膜的生理结构得到了真实的反应。     

排除脑电监护、脑电图干扰的方法

当脑电图、脑电监护受到干扰时 ,它往往表现出 ,描迹出来的波形不光滑 ,抖动大 ,影响了临床的诊断。由于脑电信号是一种极其微弱的生物电信号 ,在对它的采集放大处理过程、对外界电磁波的屏蔽、周围环境等 ,这些环节中有任何一点出了问题都可能是该机产生干扰的原因。下面依据上述几个方面问题逐一进行分析与处理。周围环境的影响 :先查看周围是否有其它电器在工作 ,尽可能地远离这些能产生电磁波的设备 ,或在监护诊断时暂时关闭这些设备 ;查看电极盒和导联线以及患者是否碰到比如金属床架和仪器外壳等物体 ,如有及时把它们隔开。做好电磁波…     

MR图像伪影的种类及消除方法的探讨

本文就MR扫描中常见伪影的种类、出现伪影的原因、及消除伪影的方法逐类进行了研究和探讨，有效地减少了MR图像中的伪影，提高了MR图像质量。     

磁共振成像的伪影及消除对策分析

分析了各种伪影产生的原因、图像特点及相应的处理技术,并介绍了扫描时应如何调整扫描参数,旨在进一步提高图像质量,利于病变的检出,更好地配合临床诊断。     

超声血流成像检测中镜像伪差产生的现象及原因分析

目的探讨超声血流成像检测中镜像伪差现象及原因分析。方法通过制作模拟实验,运用超声血流成像检测,对镜像伪差(伪像)现象的产生原因进行分析。结果超声仪将第二次试管的图像成像在水槽底部更远处,在水槽的底部的更深面,再次形成试管的图像。结论正确认识超声血流成像检测中镜像伪差产生的现象及原因,可避免可能由此引起的误诊,应引起足够重视。     

CT图像伪影的产生及消除

CT图像伪影就是指扫描重建出来的CT图像中出现的不属于图像本身的信息。不管CT机制造、校准、维护的多么好，图像伪影都是不可避免的。CT图像伪影可分为两类：与扫描、采集有关的伪影及与CT机系统本身有关的伪影。扫描伪影的产生与扫描过程中扫描方法、参数的选择有关。主要包括部分体积效应伪影、X线硬化伪影、金属伪影、运动伪影等。当X线穿过人体时，人体内各点密度不同，而探测器不可能做的太小，因而就会出现在同一个探测器单元上一半有高密度的测量数据，另一半有低密度的测量数据，而探测器的输出信号是按一个信号来自一个探测…     

An autoregressive linear mixed effects model for the analysis of unequally spaced longitudinal data with dose-modification

The assessment of the dose-response relationship is important but not straightforward when the therapeutic agent is administered repeatedly with dose-modification in each patient and a continuous response is measured repeatedly. We recently proposed an autoregressive linear mixed effects model for such data in which the current response is regressed on the previous response, fixed effects, and random effects. The model represents profiles approaching each patient's asymptote, takes into account the past dose history, and provides a dose-response relationship of the asymptote as a summary measure. In an autoregressive model, intermittent missing data mean the missing values in previous responses as covariates. We previously provided the marginal (unconditional on the previous response) form of the proposed model to deal with intermittent missing data. Irregular timings of dose-modification or measurement can also be treated as equally spaced data with intermittent missing values by selecting an adequately small unit of time. The likelihood is, however, expressed by matrices whose sizes depend on the number of observations for a patient, and the computational burden is large. In this study, we propose a state space form of the autoregressive linear mixed effects model to calculate the marginal likelihood without using large matrices. The regression coefficients of the fixed effects can be concentrated out of the likelihood in this model by the same way of a linear mixed effects model. As an illustration of the approach, we analyzed immunologic data from a clinical trial for multiple sclerosis patients and estimated the dose-response curves for each patient and the population mean.     

对超声伪像的重新认识及其控制

当人类跨入21世纪时,超声医学作为临床影像医学的重要组成部分,正以高速发展的新面貌展现在我们面前。文章讨论了各种超声图像伪像形成的原因,并在此基础上从声波的传播角度,对超声伪像给出了新的认识,提出了一些控制方法。     

A method for processing multivariate data in medical studies

Traditional displays of principal component analyses lack readability to discriminate between putative clusters of variables or cases. Here, the author proposes a method that clusterizes and visualizes variables or cases through principal component analyses thus facilitating their analysis. The method displays pre‐determined clusters of variables or cases as urchins that each has a soma (the average point) and spines (the individual variables or cases). Through three examples in the field of neuropsychology, the author illustrates how urchins help examine the modularity of cognitive tasks on the one hand and identify groups of healthy versus brain‐damaged participants on the other hand. Some of the data used in this article were obtained from the Alzheimer's Disease Neuroimaging Initiative database. The urchin method was implemented in MATLAB , and the source code is available in the Supporting information. Urchins can be useful in biomedical studies to identify distinct phenomena at first glance, each having several measures (clusters of variables) or distinct groups of participants (clusters of cases). Copyright © 2013 John Wiley & Sons, Ltd.     

A tractable method to account for high-dimensional nonignorable missing data in intensive longitudinal data

Despite the need for sensitivity analysis to nonignorable missingness in intensive longitudinal data (ILD), such analysis is greatly hindered by novel ILD features, such as large data volume and complex nonmonotonic missing-data patterns. Likelihood of alternative models permitting nonignorable missingness often involves very high-dimensional integrals, causing curse of dimensionality and rendering solutions computationally prohibitive to obtain. We aim to overcome this challenge by developing a computationally feasible method, nonlinear indexes of local sensitivity to nonignorability (NISNI). We use linear mixed effects models for the incomplete outcome and covariates. We use Markov multinomial models to describe complex missing-data patterns and mechanisms in ILD, thereby permitting missingness probabilities to depend directly on missing data. Using a second-order Taylor series to approximate likelihood under nonignorability, we develop formulas and closed-form expressions for NISNI. Our approach permits the outcome and covariates to be missing simultaneously, as is often the case in ILD, and can capture U-shaped impact of nonignorability in the neighborhood of the missing at random model without fitting alternative models or evaluating integrals. We evaluate performance of this method using simulated data and real ILD collected by the ecological momentary assessment method.     

Correction of lateral response artifacts from flatbed scanners for dual-channel radiochromic film dosimetry

In this study, we evaluated the inter-unit variability of the lateral response artifact for multiple flatbed scanners, focusing on the dual-channel method, and investigated the correction method of the lateral non-uniformity. Four scanners with A3+ paper-size and five scanners with A4 paper-size were evaluated. To generate the dose–response curves, small pieces of the Gafchromic EBT3 and EBT-XD films were irradiated, and five of the pieces were repeatedly scanned by moving them on the scanner to evaluate the lateral non-uniformity. To calculate the dose distribution accounting for the lateral non-uniformity, linear functions of the correction factor, representing the difference between the pixel values at offset position and the scanner midline, were calculated for red and blue color channels at each lateral position. Large variations of the lateral non-uniformity among the scanners were observed, even for the same model of scanner. For high dose, red color showed pixel value profiles similar to symmetric curves, whereas the profiles for low dose were asymmetric. The peak positions changed with dose. With correction of the lateral non-uniformity, the dose profiles of the pyramidal dose distribution measured at various scanner positions and that calculated with a treatment planning system showed almost identical profile shapes at all high-, middle- and low-dose levels. The dual-channel method used in this study showed almost identical dose profiles measured with all A3+ and A4 paper-size scanners at any positions when the corrections were applied for each color channel.     

An autoregressive linear mixed effects model for the analysis of longitudinal data which show profiles approaching asymptotes

In longitudinal data, a continuous response sometimes shows a profile approaching an asymptote. For such data, we propose a new class of models, autoregressive linear mixed effects models in which the current response is regressed on the previous response, fixed effects, and random effects. Asymptotes can shift depending on treatment groups, individuals, and so on, and can be modelled by fixed and random effects. We also propose error structures that are useful in practice. The estimation methods of linear mixed effects models can be used as long as there is no intermittent missing.     

A MUSIC-based method for SSVEP signal processing

The research on brain computer interfaces (BCIs) has become a hotspot in recent years because it offers benefit to disabled people to communicate with the outside world. Steady state visual evoked potential (SSVEP)-based BCIs are more widely used because of higher signal to noise ratio and greater information transfer rate compared with other BCI techniques. In this paper, a multiple signal classification based method was proposed for multi-dimensional SSVEP feature extraction. 2-second data epochs from four electrodes achieved excellent accuracy rates including idle state detection. In some asynchronous mode experiments, the recognition accuracy reached up to 100 %. The experimental results showed that the proposed method attained good frequency resolution. In most situations, the recognition accuracy was higher than canonical correlation analysis, which is a typical method for multi-channel SSVEP signal processing. Also, a virtual keyboard was successfully controlled by different subjects in an unshielded environment, which proved the feasibility of the proposed method for multi-dimensional SSVEP signal processing in practical applications.     

Bayesian inference from incomplete longitudinal data: A simple method to quantify sensitivity to nonignorable dropout

Bayesian approach has been increasingly used for analyzing longitudinal data. When dropout occurs in the study, analysis often relies on the assumption of ignorable dropout. Because ignorability is a critical and untestable assumption without obtaining additional data or making other unverifiable assumptions, it is important to assess the impact of departures from the ignorability assumption on the key Bayesian inferences. In this paper, we extend the Bayesian index of local sensitivity to non‐ignorability (ISNI) method proposed by Zhang and Heitjan to longitudinal data with dropout. We derive formulas for the Bayesian ISNI when the complete longitudinal data follow a linear mixed‐effect model. The calculation of the index only requires the posterior draws or summary statistics of these draws from the standard analysis of the ignorable model. Thus, our approach avoids fitting any complicated nonignorable model. One can use the method to evaluate which Bayesian parameter estimates or functions of these estimates in a linear mixed‐effect model are susceptible to nonignorable dropout and which ones are not. We illustrate the method using a simulation study and two real examples: rats data set and rheumatoid arthritis clinical trial data set. Copyright © 2009 John Wiley & Sons, Ltd.     

A Bayesian semi-parametric model for colorectal cancer incidences

A Bayesian semi-parametric model is proposed to capture the interaction among demographic effects (age and gender), spatial effects (county) and temporal effects of colorectal cancer incidences simultaneously. In particular, an extension of multivariate conditionally autoregressive (CAR) processes to a partially informative Gaussian demographic spatial temporal CAR (DSTCAR) process for a spatial-temporal setting is proposed. The precision matrix of the Gaussian DSTCAR process is the Kronecker product of several components. The spatial component is modelled with a CAR prior. A pth order intrinsic autoregressive prior (IAR(p)) is implemented for the temporal component to estimate a smoothed and non-parametric temporal trend. The demographic component is modelled with a Wishart prior. Data analysis shows significant spatial correlation only exists in the age group of 50-59. Males and females in their 50s and 60s show fairly strong correlation. The hypothesis testing based on Bayes factor suggests that gender correlation cannot be ignored in this model.