一种基于支持向量机的心电信号处理算法

新一代心电图(Electrocardiography,ECG)系统中,可以使用可穿戴设备来监测人体生理信号.心电图信号是一种生物医学信号,基本上与人体心脏的电活动相对应,根据其波形可以初步判断人体是否存在疾病.本文首先对ECG信号进行了预处理,然后使用自适应阈值对QRS波进行定位,最后使用支持向量机对心电信号进行分类....     

A comparison of surface and intramuscular myoelectric signal classification

The surface myoelectric signal (MES) has been used as an input to controllers for powered prostheses for many years. As a result of recent technological advances it is reasonable to assume that there will soon be implantable myoelectric sensors which will enable the internal MES to be used as input to these controllers. An internal MES measurement should have less muscular crosstalk allowing for more independent control sites. However, it remains unclear if this benefit outweighs the loss of the more global information contained in the surface MES. This paper compares the classification accuracy of six pattern recognition-based myoelectric controllers which use multi-channel surface MES as inputs to the same controllers which use multi-channel intramuscular MES as inputs. An experiment was designed during which surface and intramuscular MES were collected simultaneously for 10 different classes of isometric contraction. There was no significant difference in classification accuracy as a result of using the intramuscular MES measurement technique when compared to the surface MES measurement technique. Impressive classification accuracy (97%) could be achieved by optimally selecting only three channels of surface MES.     

多通道表面肌电信号同步采样与处理研究

在康复机器人的人机交互系统中,表面肌电信号(sEMG)发挥着重要作用。针对采集单通道的肌电信号已经不能满足获取更多信息量的需求,该文设计一个8通道的肌电信号同步采集系统。该系统包括表面电极贴片、仪表放大器、带通滤波与二级放大、50 Hz陷波器、同步采集ADC和无线蓝牙模块等部分。通过测试实验,该采集系统很好地采集了人体的表面肌电信号,有效去除了共模噪声和50 Hz的工频干扰。获取的同步肌电信号可以进一步用于人机交互系统的模式识别研究。     

心电信号处理中的数字滤波器的设计

心电信号是一种基本的人体信号,其中常带有肌电干扰、基线漂移和工频干扰等各种噪声,为了得到不失真的原始心电信号,在显示信号前要进行必要的滤波预处理.介绍了处理ECG信号中滤波器的设计,包括去除噪声的低通、高通和带阻滤波器.     

Surface myoelectric signal analysis: dynamic approaches for change detection and classification

Toward the goal of elbow and wrist prostheses control by characterizing events in surface myoelectric signals, this paper presents a dynamic method to simultaneously detect and classify such events. Dynamic cumulative sum of local generalized likelihood ratios using wavelet decomposition of the myoelectric signal is used for on-line detection. Frequency as well as energy changes are detected with this hybrid approach. Classification is composed of using multiresolution wavelet analysis and autoregressive modeling to extract signal features while polynomial classifiers are used for pattern modeling and matching. The results of detecting and classifying four elbow and wrist movements show that, in average, 91% of the events are correctly detected and classified using features obtained from multiresolution wavelet analysis while 95% accuracy is achieved with AR modeling. The classification accuracy decreases, however, if short prostheses response delay is desired. This paper also shows that the performance of the polynomial classifiers is better than that of the commonly used neural networks since it gives higher classification accuracy and consistent classification outcomes. In comparison to the well known support vector machine classification, the polynomial classifier gives similar results without the need to optimize and search for classifier parameters.     

Chip-in-the-loop learning algorithm for Boltzmann machine

A new algorithm is presented for chip-in-the-loop learning of the synchronous Boltzmann machine, which has two distinct advantages: it allows optimal use of analogue circuit dynamic range, unlike the conventional learning algorithm, and it features a coherent dual weight representation, which improves learning efficiency     

心电信号采集数字滤波算法优化

由于心电信号(ECG)检测的实际环境情况并非理想状态,因此ECG信号中总会夹杂着各种随机噪声。这些噪声的存在使得心电信号模糊而且严重地影响了医疗诊断的可靠度。因此,当前需要优化的问题是如何更好地抑制这些严重干扰ECG信号检测波形的噪声。虽然针对这方面的滤波算法有了一定的成果,但在心电信号的细节特征上做得不够细致,所以文中从数字滤波角度出发,基于分析IIR法,以提高心电信号特征点清晰度。由仿真结果可以看出,多阶反馈式整形IIR滤波器能够更加明显清晰地保留原始信号特征,有利于后期诊断。     

Adaptive parametric algorithms for processing coherentDoppler-lidar signal

The authors study the autoregressive and moving average (ARMA) filter for lidar signal processing. After a short presentation of the atmospheric laser Doppler instrument project (ALADIN), they introduce the objective of this paper, which is to extract the Doppler frequency and to retrieve the spectral width of a noised lidar signal. A general presentation of ARMA filters and parametric adaptive algorithms (PAAs) is provided. Then they present results about the choice of the model, the Doppler frequency estimate, and the spectral width estimate. Finally, they study the possible estimate of SNR, which is biased by the first estimates (Doppler frequency and spectral width)     

Adaptive fuzzy systems for multichannel signal processing

Processing multichannel signals using digital signal processing techniques has received increased attention lately due to its importance in applications such as multimedia technologies and telecommunications. The objective of this paper is twofold: 1) to introduce adaptive filtering techniques to the reader who is just beginning in this area and 2) to provide a review for the reader who may be well versed in signal processing. The perspective of the topic offered here is one that comes primarily from work done in the field of multichannel (color) image processing. Hence, many of the techniques and works cited here relate to image processing with the emphasis placed primarily on filtering algorithms based on fuzzy concepts, multidimensional scaling, and order statistics-based designs. It should be noted, however, that multichannel signal processing is a very broad field and thus contains many other approaches that have been developed from different perspectives, such as transform domain filtering, classical least-square approaches, neural networks, and stochastic methods, just to name a few. We present a general formulation based on fuzzy concepts, which allows the use of adaptive weights in the filtering structure, and we discuss different filter designs. The strong potential of fuzzy adaptive filters for multichannel signal applications, such as color image processing, is illustrated with several examples     

Highlights of statistical signal and array processing

The Statistical Signal and Array Processing Technical Committee (SSAP-TC) deals with signals that are random and processes an array of signals simultaneously. The field of SSAP represents both solid theory and practical applications. Starting with research in spectrum estimation and statistical modeling, study in this field is always full of elegant mathematical tools such as statistical analysis and matrix theory. The area of statistical signal processing expands into estimation and detection algorithms, time-frequency domain analysis, system identification, and channel modeling and equalization. The area of array signal processing also extends into multichannel filtering, source localization and separation, and so on. This article represents an endeavor by the members of the SSAT-TC to review all the significant developments in the field of SSAP. To provide readers with pointers for further study of the field, this article includes a very impressive bibliography-close to 500 references are cited. This is just one of the indications that the field of statistical signals has been an extremely active one in the signal processing community. The article also introduces the recent reorganization of three technical committees of the Signal Processing Society     

改进蝙蝠算法优化极限学习机的图像分类

针对分类器中的极限学习机参数优化问题,本文提出一种改进蝙蝠算法优化极限学习机的图像分类模型。首先将极限学习机参数看作蝙蝠位置,然后采用改进蝙蝠算法进行求解。采用病毒群体感染主群体,主群体在历代个体间纵向传递信息,病毒群体通过感染操作在同代个体间横向传递信息,增强了算法跳出局部极小值的能力。最后根据最优参数建立图像分类模型,并对模型的性能进行仿真测试。仿真结果表明,相对于对比模型,本文模型不仅提高了图像分类正确率,而且加快了分类速度,是一种有效的图像分类模型。     

基于ASP.NET的网络选课系统的设计与实现

本设计为基于Dotnet技术开发的网络选课系统,引入学分制和竞分制的概念。提供学生、教师、管理员三个角色登陆系统。实现了学生选课、退课、竞买选课、查看成绩,查看选课结果,查看个人信息、课程信息、教室信息;教师查看教学计划,录入成绩;管理员维护学生、教师、课程、教室信息,设置系统状态等功能。在课程上进行了较细的分类,主要分为专业选修课、专业必修课、公共选修课、公共必修课。该系统具有操作简便,功能强大,可扩展性好等特点。为学生,教师,管理员提供简单快捷的选课工作环境。     

Machine learning for multimodality genomic signal processing

This paper discusses how machine learning can be applied to genomic signal processing, particularly via fusion of multiple biological or algorithmic modalities, to improve prediction performance.     

Monte Carlo methods for signal processing: a review in the statistical signal processing context

In this article, MCMC (Markov chain Monte Carlo methods) and SMC (sequential Monte Carlo methods) are introduced to sample and/or maximize high-dimensional probability distributions. These methods enable to perform likelihood or Bayesian inference for complex non-Gaussian signal processing problems.     

A statistical method for the measurement of muscle activationintervals from surface myoelectric signal during gait

The aim of this work is to present an original double-threshold detector of muscle activation, specifically developed for gait analysis. This detector operates on the raw myoelectric signal and, hence, it does not require any envelope detection. Its performances are fixed by the values of 3 parameters, namely, false-alarm probability (P_fa), detection probability, and time resolution. Double-threshold detectors are preferable to single-threshold ones because, for a fixed value of the P_fa, they yield higher detection probability; furthermore, they allow the user to select the couple false alarm-detection probability with a higher degree of freedom, thus, adapting the performances of the detector to the characteristics of the myoelectric signal of interest and of the experimental situation. Here, first the authors derive the detection algorithm and describe different strategies for selecting its parameters, then they present the performances of the proposed procedure evaluated by means of computer simulations, and finally they report an example of application to myoelectric signals recorded during gait. The characterization of the proposed double-threshold detector demonstrates that, in most practical situations, the bias of the estimates of the on-off transitions is smaller than 10 ms, the standard deviation may be kept lower than 15 ms, and the percentage of erroneous patterns is below 5%. These results show that this detection approach is satisfactory in research applications as well as in the clinical practice     

A statistical self-organizing learning system for remote sensing classification

A new learning system called a statistical self-organizing learning system (SSOLS), combining functional-link neural networks, statistical hypothesis testing, and self-organization of a number of enhancement nodes, is introduced for remote sensing applications. Its structure consists of two stages, a mapping stage and a learning stage. The input training vectors are initially mapped to the enhancement vectors in the mapping stage by multiplying with a random matrix, followed by pointwise nonlinear transformations. Starting with only one enhancement node, the enhancement layer incrementally adds an extra node in each iteration. The optimum dimension of the enhancement layer is determined by using an efficient leave-one-out cross-validation method. In this way, the number of enhancement nodes is also learned automatically. A t-test algorithm can also be applied to the mapping stage to mitigate the effect of overfitting and to further reduce the number of enhancement nodes required, resulting in a more compact network. In the learning stage, both the input vectors and the enhancement vectors are fed into a least squares learning module to obtain the estimated output vectors. This is made possible by choosing the output layer linear. In addition, several SSOLSs can be trained independently in parallel to form a consensual SSOLS, whose final output is a linear combination of the outputs of each SSOLS module. The SSOLS is simple, fast to compute, and suitable for remote sensing applications, especially with hyperspectral image data of high dimensionality.     

Power-line interference detection and suppression in ECG signal processing

A novel power-line interference (PLI) detection and suppression algorithm is presented to preprocess the electrocardiogram (ECG) signals. A distinct feature of this proposed algorithm is its ability to detect the presence of PLI in the ECG signal before applying the PLI suppression algorithm. No PLI suppression operation will be performed if PLI is not detected. We propose a PLI detector that employs an optimal linear discriminant analysis (LDA) algorithm to make a decision for the PLI presence. An efficient recursive least-squares (RLS) adaptive notch filter is also developed to serve the purpose of PLI suppression. Experimental results demonstrate superior performance of this proposed algorithm.