人工神经网络应用于糖尿病/糖耐量异常的疾病分类研究

目的 探讨人工神经网络(ANN)用于疾病分类研究的前景。方法 利用某矿区1996年糖尿病现况调查资料，采用学习向量量化(LVQ)网络和判别分析方法进行糖尿病借耐量(DM／IGT)异常／正常状态的判别比较；同时人为设置变量缺损值，检验LVQ网络对缺失数据的适应性。结果 LVQ网络结构为25→13→3；网络判断准确率为96．98％，对血糖异常者的正确判断率为92．45％。利用逐步判别分析建立的含11个变量的判别方程的判断准确率为87．34％，对血糖异常者的正确判断率为85．53％。LVQ网络对带缺失项样本的误判比例为1／30，判别分析则为7／30。结论 利用LVQ网络进行疾病分类预测，不仅能获得更好的预测效果，而且对资料的类型、分布不作任何限制，也不需要对分析变量做任何处理，还能很好地处理带缺失项的资料，是一种很好的流行病学分类预测新方法。     

应用人工神经网络预测糖尿病/糖耐量异常

目的 在流行病学调查资料的基础上，探讨学习向量量化(LVQ)网络用于糖尿病(DM)／糖耐量异常(IGT)疾病状态的分类预测的前景。方法 采用LVQ网络和判别分析方法对某矿区糖尿病现况调查资料和某综合性医院的DM病例—对照资料，进行DM／IGT／正常状态的判别比较；同时人为设置变量缺损值。检验LVQ网络对缺失数据的适应性。结果 LVQ网络结构为25→13→3；网络判断DM、IGT的灵敏度分别为70．45％、64．79％，特异度为100．00％。准确度为96．98％，对血糖异常的正确判断率为92．45％。利用逐步判别分析建立的含11个变量的判别方程判断DM、IGT的灵敏度分别为67．05％、60．56％，特异度为89．75％，准确率为87．34％，对血糖异常的正确判断率为85．53％。对来自某综合性医院的DM病例—对照资料进行模型验证发现，LVQ网络预测效果优于判别的分析，网络能识别出全部对照及92．37％的病例。判别准确率为96．19％。LVQ网络对带缺失项样本的误判比例为1／30，判别分析则为7／30。结论 利用LVQ网络进行疾病分类预测，不仅能获得更好的预测效果，而且对资料的类型、分布不作任何限制，也不需要对分析变量做任何处理，还能很好地处理带缺失项的资料，是一种很好的流行病学分类预测新方法。     

3种蜚蠊成虫自动分类的研究

[目的]探索一种准确、快捷适合基层单位使用的蜚蠊分类方法。[方法]将图像数字形态学特征提取与LVQ神经网络模式识别相结合，计设和实现一种能将3种蜚蠊自动分类的系统。该系统建立、训练了2个分类器：完整虫体的分类网络net1和残缺虫体的分类网络net2。[结果]样品图片送入系统后，自动完成分类过程。经检验，完整虫体分类器net1的正确率为100％；残缺虫体分类器net2的正确率为97．2％。[结论]对3种蜚蠊的自动分类取得成功，为开发出适用于更多种类蜚蠊共至其他昆虫的自动分类系统打下基础。     

福建省4市2014年丙肝报告质量分析

目的 了解福建省丙型病毒性肝炎(丙肝)病例报告的存在问题,为提高报告质量提供依据.方法 抽查福建省丙肝网络报告数较多的4市10家医疗机构,核查2014年1～3月丙肝病例报告质量.结果 10家医疗机构1～3月共检出抗-HCV或HCV-RNA阳性360例,网络报告213例,报告率59.2％;抗-HCV阳性353例,报告239例,报告率67.7％;HCV-RNA阳性183例,报告147例,报告率80.3％.有134例报告了诊断分类,分类正确率50.0％;有134例报告了急慢性分类,分类正确率51.1％.结论 福建省丙肝病例报告存在漏报且分类正确率低等问题,建议出台网络直报工作指南,修订和完善诊断标准,加强丙肝诊断报告培训.     

基于小波包分解和HMM模型的纹理分析

本文用小波包分解法(WPD)对肝脏B超图像的分类进行了研究，分类对象为正常肝图像和脂肪肝图像二类，这些图像分近程图像和远程图像来分别对待。用隐马尔可夫模型(HMM)分类。实验结果显示该法分类正确率要比多分辨分形特征法(MFF)高，是一种潜在的分析B超肝脏图像纹理的工具。     

基于共同空间模式和支持向量机的脑–机接口信号分类

在脑-机接口(BCI)研究中一个关键问题是准确地对EEG信号进行特征提取和模式分类,以得到人机通信与控制命令。经过对非靶刺激和靶刺激下诱发的EEG进行去均值、低通滤波、下采样等处理后,利用共同空间模式算法对所采集到的EEG数据进行特征提取,然后通过网格搜索法获取最优分类参数的情况下,利用基于径向基函数的支持向量机设计分类器。通过对3名受试者的实验数据进行各10次的处理后得到较好的分类效果,平均分类准确率为99.2%。实验结果表明,本文的方法适合于基于"模拟阅读"的脑-机接口中。     

珠海口岸常见鼠形动物外形的模式识别研究

目的探索鼠形动物鉴别较为客观、高效的方法,以作为专家经验的补充。方法对珠海口岸常见的4种成年鼠形动物的5个外形指标(体重、体长、尾长、耳长、后足长)进行分析。以统计学方法和神经网络方法进行模式识别的试探。结果分别以全模型法(强迫引入法)和逐步选择法建立分类函数,并建立BP神经网络来对种类进行分类鉴定,用训练样本数据进行回判时,分别得到96.0%、96.5%、95.5%的正确率,3种方法的正确率无显著性差异。结论模式识别方法能较好地鉴别珠海口岸的常见鼠形动物。     

经颅多普勒和脑电图对短暂性脑缺血发作患者的临床应用

目的 探讨经颅多普勒和脑电图对短暂性脑缺血发作患者的临床应用价值。方法 采用彩色经颅多普勒超声检查仪(Transcranial Doppler，TCD)和脑电图仪(Electroencephalography，EEG)对85例短暂性脑缺血(Transient ischemic attack，TIA)患者和60名正常对照组进行检测，并对检查结果进行分析。结果 TIA组TCD和EEG结果与对照组比较有统计学意义，TCD异常率为77．7％，EEG异常率为68．2％，经χ^2检验异常率无明显差异。结论 TCD检查对TIA诊断具有较高的敏感性，结合TCD和EEG检查有助于TIA的诊断。     

基于模糊神经网络的麻醉深度的监测研究

提出模糊神经网络对麻醉信号进行信息融合，实现麻醉深度监测的方法。实验是从31例复合全麻病人的EEG信号中提取出非线性动力学参数——KC复杂度、近似嫡，和25例训练样本的训练及6例检验样本的前瞻性检验,结果表明以EEG信号的非线性动力学参数为输入的ANFIS网络输出具有显著的差异性，可以作为一种反映麻醉深度的定量指标。     

彩色多普勒超声在诊断假性动脉瘤中的价值

探讨彩色多普勒超声在假性动脉瘤的影像特征及临床诊断价值。应用彩色多普勒超声对10例假性动脉瘤进行检查．分析假性动脉瘤的内部回声、血液信号和频谱特征。结果表明，彩超定性正确率为90％(9／10)，判断来源动脉的正确率为80％(8／10)，所有病例均显示附壁血栓和腔内涡流，瘤颈部“双期双向”频谱为其特征性表现。     

Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2-D cursor movements for BCI using SVM and ANN

Brain computer interface (BCI) is a new communication way between man and machine. It identifies mental task patterns stored in electroencephalogram (EEG). So, it extracts brain electrical activities recorded by EEG and transforms them machine control commands. The main goal of BCI is to make available assistive environmental devices for paralyzed people such as computers and makes their life easier. This study deals with feature extraction and mental task pattern recognition on 2-D cursor control from EEG as offline analysis approach. The hemispherical power density changes are computed and compared on alpha–beta frequency bands with only mental imagination of cursor movements. First of all, power spectral density (PSD) features of EEG signals are extracted and high dimensional data reduced by principle component analysis (PCA) and independent component analysis (ICA) which are statistical algorithms. In the last stage, all features are classified with two types of support vector machine (SVM) which are linear and least squares (LS-SVM) and three different artificial neural network (ANN) structures which are learning vector quantization (LVQ), multilayer neural network (MLNN) and probabilistic neural network (PNN) and mental task patterns are successfully identified via k-fold cross validation technique.     

Epileptic seizure detection in EEG signal using machine learning techniques

Epilepsy is a well-known nervous system disorder characterized by seizures. Electroencephalograms (EEGs), which capture brain neural activity, can detect epilepsy. Traditional methods for analyzing an EEG signal for epileptic seizure detection are time-consuming. Recently, several automated seizure detection frameworks using machine learning technique have been proposed to replace these traditional methods. The two basic steps involved in machine learning are feature extraction and classification. Feature extraction reduces the input pattern space by keeping informative features and the classifier assigns the appropriate class label. In this paper, we propose two effective approaches involving subpattern based PCA (SpPCA) and cross-subpattern correlation-based PCA (SubXPCA) with Support Vector Machine (SVM) for automated seizure detection in EEG signals. Feature extraction was performed using SpPCA and SubXPCA. Both techniques explore the subpattern correlation of EEG signals, which helps in decision-making process. SVM is used for classification of seizure and non-seizure EEG signals. The SVM was trained with radial basis kernel. All the experiments have been carried out on the benchmark epilepsy EEG dataset. The entire dataset consists of 500 EEG signals recorded under different scenarios. Seven different experimental cases for classification have been conducted. The classification accuracy was evaluated using tenfold cross validation. The classification results of the proposed approaches have been compared with the results of some of existing techniques proposed in the literature to establish the claim.     

Data selection in EEG signals classification

The alcoholism can be detected by analyzing electroencephalogram (EEG) signals. However, analyzing multi-channel EEG signals is a challenging task, which often requires complicated calculations and long execution time. This paper proposes three data selection methods to extract representative data from the EEG signals of alcoholics. The methods are the principal component analysis based on graph entropy (PCA-GE), the channel selection based on graph entropy (GE) difference, and the mathematic combinations channel selection, respectively. For comparison purposes, the selected data from the three methods are then classified by three classifiers: the J48 decision tree, the K-nearest neighbor and the Kstar, separately. The experimental results show that the proposed methods are successful in selecting data without compromising the classification accuracy in discriminating the EEG signals from alcoholics and non-alcoholics. Among them, the proposed PCA-GE method uses only 29.69 % of the whole data and 29.5 % of the computation time but achieves a 94.5 % classification accuracy. The channel selection method based on the GE difference also gains a 91.67 % classification accuracy by using only 29.69 % of the full size of the original data. Using as little data as possible without sacrificing the final classification accuracy is useful for online EEG analysis and classification application design.     

基于能量法的脑机接口运动想象分类研究

针对脑机接口运动想象脑电信号的分类识别问题,提出了一种基于小波包分解的C3、C4二通道能量特征提取方法。该方法首先采用6阶的巴特沃斯带通滤波对二通道脑电信号进行降噪;然后采用Daubechies类小波函数对其进行5层分解,选择第四层CD4、第五层CD5的小波系数进行重构并抽取其能量特征;最后采用线性距离判别进行分类和使用Kappa系数进行分类衡量。利用BCI2008竞赛的标准数据BCICIV_2b_gdf进行验证,结果表明利用该方法可以较好地反映事件相关同步和事件相关去同步的特征,为BCI研究中事件相关电位的分类识别提供了有效的手段。     

Diagnosis of multiple sclerosis from EEG signals using nonlinear methods

EEG signals have essential and important information about the brain and neural diseases. The main purpose of this study is classifying two groups of healthy volunteers and Multiple Sclerosis (MS) patients using nonlinear features of EEG signals while performing cognitive tasks. EEG signals were recorded when users were doing two different attentional tasks. One of the tasks was based on detecting a desired change in color luminance and the other task was based on detecting a desired change in direction of motion. EEG signals were analyzed in two ways: EEG signals analysis without rhythms decomposition and EEG sub-bands analysis. After recording and preprocessing, time delay embedding method was used for state space reconstruction; embedding parameters were determined for original signals and their sub-bands. Afterwards nonlinear methods were used in feature extraction phase. To reduce the feature dimension, scalar feature selections were done by using T-test and Bhattacharyya criteria. Then, the data were classified using linear support vector machines (SVM) and k-nearest neighbor (KNN) method. The best combination of the criteria and classifiers was determined for each task by comparing performances. For both tasks, the best results were achieved by using T-test criterion and SVM classifier. For the direction-based and the color-luminance-based tasks, maximum classification performances were 93.08 and 79.79% respectively which were reached by using optimal set of features. Our results show that the nonlinear dynamic features of EEG signals seem to be useful and effective in MS diseases diagnosis.     

基于小波神经网络的脑电信号数据压缩与棘波识别研究

在对小波神经网络及其算法研究的基础上，提出了一种对脑电信号压缩表达和痫样脑电棘波识别的新方法。实验结果显示，小波网络在大量压缩数据的同时，能够较好的恢复原有信号，另外，在脑电信号的时频谱等高线图上，得到了易于自动识别的棘波和棘慢复合波特征，说明此方法在电生理信号处理和时频分析方面有着光明的应用前景。     

A stable feature extraction method in classification epileptic EEG signals

Epilepsy is one of the most common neurological disorders. Electroencephalogram (EEG) signals are generally employed in diagnosing epilepsy. Therefore, extracting relevant features from EEG signals is one of the major tasks in an accurate diagnosis. In this study, the local ternary patterns, which is an image processing method, was improved in order to extract robust features from epileptic EEG signals. The EEG signals that were recorded by the Department of Etymology in the Bonn University were employed in the evaluation and validation of the proposed approach. Low and up features, which were extracted by the proposed one-dimensional ternary patterns, were classified by some machine learning methods such that support vector machine, functional trees, random forest (RF), Bayes networks (BayesNet), and artificial neural network, while the highest accuracies were obtained by RF. Achieved accuracies were found successful according to the current literature.     

Estimation of effective brain connectivity with dual Kalman filter and EEG source localization methods

Effective connectivity is one of the most important considerations in brain functional mapping via EEG. It demonstrates the effects of a particular active brain region on others. In this paper, a new method is proposed which is based on dual Kalman filter. In this method, firstly by using a brain active localization method (standardized low resolution brain electromagnetic tomography) and applying it to EEG signal, active regions are extracted, and appropriate time model (multivariate autoregressive model) is fitted to extracted brain active sources for evaluating the activity and time dependence between sources. Then, dual Kalman filter is used to estimate model parameters or effective connectivity between active regions. The advantage of this method is the estimation of different brain parts activity simultaneously with the calculation of effective connectivity between active regions. By combining dual Kalman filter with brain source localization methods, in addition to the connectivity estimation between parts, source activity is updated during the time. The proposed method performance has been evaluated firstly by applying it to simulated EEG signals with interacting connectivity simulation between active parts. Noisy simulated signals with different signal to noise ratios are used for evaluating method sensitivity to noise and comparing proposed method performance with other methods. Then the method is applied to real signals and the estimation error during a sweeping window is calculated. By comparing proposed method results in different simulation (simulated and real signals), proposed method gives acceptable results with least mean square error in noisy or real conditions.     

Quantifying clinical improvements in patients with depression under the treatment of transcranial direct current stimulation using event related potentials

The main goal of this study was to assess the changes in brain activities of patients with severe depression by applying transcranial direct current stimulation (tDCS) using event related potentials (ERPs). Seven patients (four males, with the mean age 34.85?±?4.25) were asked to fill out Beck’s depression questionnaires. EEG signals of subjects were recorded during Stroop test. This test entailed 360 stimulations, which included 120 congruent, 120 incongruent, and 120 neutral stimulations lasting for 12 min. Subsequently, the dorso lateral prefrontal cortex in patients’ left hemisphere was stimulated for six sessions using tDCS. At the end of tDCS treatment period, subjects filled out Beck’s depression questionnaires again and EEG signal recordings were repeated simultaneously with Stroop test. Wavelet coefficients of EEG frequency bands in every stimulation type were extracted from ERP components. The changes in Beck score before and after tDCS were estimated using neural network model. The ERP results showed that the latency period of N400 component after applying tDCS decreased significantly. Moreover, a significant correlation was observed between percentage changes of congruent and incongruent accuracy and the increase in the average energy of wavelet coefficients in alpha band in Pz electrode with p?=?0.0128, r?=?0.9060 and p?=?0.0037, r?=?0.95, respectively. Additionally, the results of neural network model revealed that the changes in Beck score were estimated with an average error of 0.0519. Consequently, the improvement of depressed patients treated with tDCS could be estimated with good accuracy using average energy of wavelet coefficients in alpha band.