基于短时傅里叶变换的肌电信号识别方法

针对肌电信号的非平稳特性,采用短时傅里叶变换方法对表面肌电信号进行分析,并通过奇异值分解有效地提取特征矢量进行模式识别,能够成功地从掌长肌和肱桡肌采集的两道表面肌电信号中识别展拳、握拳、腕内旋、腕外旋四种运动模式。实验表明,基于短时傅里叶变换的奇异值分解方法是一种稳定、有效的特征提取方法。     

基于BP神经网络的表面肌电信号模式分类的研究

将神经网络与AR模型相结合提出了一种表面肌电信号模式分类算法。该算法能成功地从腕伸肌和腕屈肌的两道表面肌电信号中识别脱伸、腕屈、腕内旋和腕外旋四种运动模式。实验结果表明：用AR模型参数作BP网输入的肌电模式分类器，运行速度快、识别率高、鲁棒性好，在假肢等人一机仿生系统的控制中具有很好的应用前景。     

用LM算法改进的BP网络在表面肌电信号识别中的应用研究

提出用Levenberg-Marquardt算法改进BP神经网络识别表面肌电信号的方法.采用多尺度小波变换对肌电信号进行分析,提取各尺度下小波系数幅值的最大和最小值构造特征矢量,输入BP神经网络可进行模式识别,经过训练能够成功地从表面肌电信号中识别展拳、握拳、前臂内旋、前臂外旋四种运动模式.实验表明,LM算法在响应时间和识别精度上都比标准的BP算法有了很大提高.     

改进的BP算法在表面肌电信号识别中的应用

针对肌电信号的非平稳特性,采用小波变换方法对表面肌电信号进行分析,提取小波系数幅值的最大和最小值构造特征向量,输入BP神经网络可进行模式识别,网络经过学习能够成功地从表面肌电信号中识别展拳、握拳、前臂内旋、前臂外旋4种运动模式。比较了标准的BP算法和用贝叶斯正则化与Levenberg-Marquardt算法相结合的改进BP网络训练的结果。实验表明,改进的BP网络在训练速度和识别精度上都比标准的BP算法有了很大提高,这对于肌电假肢的控制具有良好的应用前景。     

肌电信号的频谱分析及前臂假手控制系统

本文利用7T08信号处理仪对健康人的腕伸肌和腕屈肌在指伸、指屈、腕伸、腕屈、旋前,旋后六个动作下所发出的肌电信号一一作了频谱分析。并根据频谱图提出了利用两对电极,从两块肌肉上取出两组电信号来控制三自由度前臂假手的方案。具体电路在健康人身上作的试验表明从频率分离来研制肌电假手是可行的。     

基于多尺度主元分析的表面肌电信号模式分类

用基于小波变换的多尺度主元分析提取表面肌电信号特征,然后用贝叶斯分类器进行模式分类。实验结果显示,当选用Harr小波和bior2．6小波对肌电信号进行5层小波分解时．该方法对前臂6种动作模式（内翻,外翻．握拳．展拳．上切和下切）的正确识别率可以达到99.44％。研究表明,该方法优于基于小波系数统计特征和主元分析降维相结合的特征提取方法．能成功识别出多种动作模式。     

桡侧腕长伸肌腱、尺侧腕屈肌腱移位重建伸拇、伸指功能

前臂桡神经深支损伤（旋后肌以下）可导致伸拇、伸指功能障碍，如神经缺损过多不能修复，或经早期修复后伸拇、伸指功能仍未恢复，可行桡侧腕长伸肌腱、尺侧腕屈肌腱移位重建伸拇、伸指功能。我院于2000-2006年应用该术式重建伸拇、伸指功能12例，效果满意，报道如下。     

认识“网球肘” 远离“网球肘”

<正>网球肘即肱骨外上髁炎,是一种前臂伸肌总腱起点特别是桡侧伸腕短肌的慢性撕拉伤。这些肌肉反复收缩牵拉肌肉起点,造成累积性损伤,是过劳性综合征的典型例子。由于该病好发于网球运动员,故称其为网球肘。其他的运动项目如乒乓球、羽毛球运动员也是高发人群,此外家庭主妇、砖瓦工、木工等长期反复用力做肘部活动者,也易患此病。网球肘多见于平常生活中研究表明,所有年龄及种族的人均可患此病。在前臂过度旋前或旋后时,行握拳屈腕和伸腕等动     

基于希尔伯特-黄变换和提升小波包相结合的心音信号识别研究

目的：研究基于希尔伯特-黄变换和提升小波包相结合的方法对正常和异常心音信号识别的效果。方法：首先用DB6小波对心音信号进行降噪处理,然后用希尔伯特-黄变换分析提取心音信号的时域、频域特征值,再通过自适应提升小波包提取信号的频带能量特征值,最后通过支持向量机对心音信号进行分类识别。结果：对临床采集的240例异常心音和正常心音进行实验,正确识别率达到97.2%,且运算速度很快。结论：希尔伯特-黄变换和自适应提升小波包相结合的方法可有效识别正常和各种异常的心音信号,值得推广应用。     

小波变换在事件诱发脑磁图分析中的应用

目的:验证基于Morlet小波变换的时频分析方法在事件诱发脑磁图中的应用效果。方法:根据Morlet小波变换的数学原理及特点,对脑磁图数据进行时频分解,采用时频能量分布图和锁相因子分布图表示脑磁图信号。结果:从脑磁图数据中有效提取了锁相和非锁相的能量变化信息。结论:Morlet小波变换的时频分析方法能够适应脑磁图信号是时变非平稳信号的特点,从中提取出感兴趣的信息。     

A sparse Bayesian learning based scheme for multi-movement recognition using sEMG

This paper proposed a feature extraction scheme based on sparse representation considering the non-stationary property of surface electromyography (sEMG). Sparse Bayesian learning was introduced to extract the feature with optimal class separability to improve recognition accuracy of multi-movement patterns. The extracted feature, sparse representation coefficients (SRC), represented time-varying characteristics of sEMG effectively because of the compressibility (or weak sparsity) of the signal in some transformed domains. We investigated the effect of the proposed feature by comparing with other fourteen individual features in offline recognition. The results demonstrated the proposed feature revealed important dynamic information in the sEMG signals. The multi-feature sets formed by the SRC and other single feature yielded more superior performance on recognition accuracy, compared with the single features. The best average recognition accuracy of 94.33 % was gained by using SVM classifier with the multi-feature set combining the feature SRC, Williston amplitude (WAMP), wavelength (WL) and the coefficients of the fourth order autoregressive model (ARC4) via multiple kernel learning framework. The proposed feature extraction scheme (known as SRC + WAMP + WL + ARC4) is a promising method for multi-movement recognition with high accuracy.     

微处理器控制的植入式心脏起搏器专用电路研制

介绍了一种基于微处理器的植入式心脏起搏器专用电路设计。该设计采用的技术路线和国外现有技术方案不同，它采用通用微处理器设计，降低了投资风险，缩短了开发周期．特别适合于我国这样的发展中国家。     

一种基于HSV颜色空间模糊量化的图像检索方法

目的：为了提高图像检索的准确率,选择一个合适的颜色空间和准确的量化方法。方法：选取HSV颜色空间,在颜色量化时引入了模糊量化方法,并在此基础上采用分块直方图描述方法进行了相应的实验。结果：实验结果表明,HSV颜色空间模糊量化方法能较好地描述图像的颜色特征,检索结果比较令人满意。结论：该方法能够提高图像检索的准确率,且检索出的相关图像排序更靠前。     

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.     

Singular value based characterization and analysis of thermal patches for early breast abnormality detection

The purpose of this study is to develop a novel breast abnormality detection system by utilizing the potential of infrared breast thermography (IBT) in early breast abnormality detection. Since the temperature distributions are different in normal and abnormal thermograms and hot thermal patches are visible in abnormal thermograms, the abnormal thermograms possess more complex information than the normal thermograms. Here, the proposed method exploits the presence of hot thermal patches and vascular changes by using the power law transformation for pre-processing and singular value decomposition to characterize the thermal patches. The extracted singular values are found to be statistically significant (p?<?0.001) in breast abnormality detection. The discriminability of the singular values is evaluated by using seven different classifiers incorporating tenfold cross-validations, where the thermograms of the Department of Biotechnology-Tripura University-Jadavpur University (DBT-TU-JU) and Database of Mastology Research (DMR) databases are used. In DMR database, the highest classification accuracy of 98.00% with the area under the ROC curve (AUC) of 0.9862 is achieved with the support vector machine using polynomial kernel. The same for the DBT-TU-JU database is 92.50% with AUC of 0.9680 using the same classifier. The comparison of the proposed method with the other reported methods concludes that the proposed method outperforms the other existing methods as well as other traditional feature sets used in IBT based breast abnormality detection. Moreover, by using Rank1 and Rank2 singular values, a breast abnormality grading (BAG) index has also been developed for grading the thermograms based on their degree of abnormality.     

Decomposition feature selection with applications in detecting correlated biomarkers of bipolar disorders

Feature selection is an important initial step of exploratory analysis in biomedical studies. Its main objective is to eliminate the covariates that are uncorrelated with the outcome. For highly correlated covariates, traditional feature selection methods, such as the Lasso, tend to select one of them and eliminate the others, although some of the eliminated ones are still scientifically valuable. To alleviate this drawback, we propose a feature selection method based on covariate space decomposition, referred herein as the “Decomposition Feature Selection” (DFS), and show that this method can lead to scientifically meaningful results in studies with correlated high dimensional data. The DFS consists of two steps: (i) decomposing the covariate space into disjoint subsets such that each of the subsets contains only uncorrelated covariates and (ii) identifying significant predictors by traditional feature selection within each covariate subset. We demonstrate through simulation studies that the DFS has superior practical performance over the Lasso type methods when multiple highly correlated covariates need to be retained. Application of the DFS is demonstrated through a study of bipolar disorders with correlated biomarkers.     

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.     

Acceptability of a new intravaginal barrier contraceptive device (Femcap)

A comprehensive questionnaire to assess acceptability was sent to 106 participants (and their partners) in a study on the safety and effectiveness of the Femcap. Participants were asked to rate their satisfaction with various aspects of the method, and were also asked about complaints such as vaginal infections, urinary tract infections, or other irritation. Eighty-one percent of the study sample either returned the mailed questionnaires or completed telephone interviews. Results were encouraging in that none of the study subjects or their partners reported any discomfort, trauma, or interference in sexual spontaneity with the cap. Vaginal irritation and infections were infrequently reported. The feature best liked by most participants was the absence of hormones associated with the method; the feature least liked was removal of the device. The Femcap may be an acceptable alternative to currently available barrier contraceptive devices.     

基于单片机的实时室性QRS波分类方法的研究

在基于单片机的便携式动态心电实时分析仪中，识别室性ＱＲＳ波为判断室性心律失常的关键，本文用反映ＱＲＳ波形态特征的间期和幅度信息构成形态特征向量，并对ＭＩＴ／ＢＩＨ心电数据库中速肯室性心律失常的文件中ＱＲＳ波形态特征向量的分布进行了研究分析，采用形态特征向量聚类法用于区分多形态的室性ＱＲＳ波，在此基础上提出了适用于捕年一过性室性心律失常的形态特征参数变化法。