A switchable scheme for ECG beat classification based on independent component analysis

A switchable scheme is proposed to discriminate different types of electrocardiogram (ECG) beats based on independent component analysis (ICA). The RR-interval serves as an indicator for the scheme to select between the longer (1.0 s) and the shorter (0.556 s) data samples for the following processing. Six ECG beat types, including 13900 samples extracted from 25 records in the MIT-BIH database, are employed in this study. Three conventional statistical classifiers are employed to testify the discrimination power of this method. The result shows a promising accuracy of over 99%, with equally well recognition rates throughout all types of ECG beats. Only 27 ICA features are needed to attain this high accuracy, which is substantially smaller in quantity than that in the other methods. The results prove the capability of the proposed scheme in characterizing heart diseases based on ECG signals.     

基于FastICA和神经网络的电子鼻模式识别

气体传感器阵列是电子鼻系统的重要组成部分,传感器阵列的交叉敏特性严重影响电子鼻对气体识别的准确率.将快速独立分量分析算法和BP网络相结合用于电子鼻的模式识别可以有效地改善这一问题.并由一个5个传感器组成的电子鼻系统,对10组不同体积分数的3种气体测量得到的30组数据样本进行仿真.结果表明,用快速独立分量分析对数据作预处理,可以简化计算,减少数据之间的相关性,将预处理后的数据样本作为BP网络的输入,使网络结构简化,收敛速度快.利用该方法可以提高电子鼻识别气体的准确率.     

Electrogastrogram extraction using independent component analysis with references

Electrogastrogram (EGG) is a noninvasive measurement of gastric myoelectrical activity cutaneously, which is usually covered by strong artifacts. In this paper, the independent component analysis (ICA) with references was applied to separate the gastric signal from noises. The nonlinear uncorrelatedness between the desired component and references was introduced as a constraint. The results show that the proposed method can extract the desired component corresponding to gastric slow waves directly, avoiding the ordering indeterminacy in ICA. Furthermore, the perturbations in EGG can be suppressed effectively. In summary, it can be a useful method for EGG analysis in research and clinical practice.     

Face representation using independent component analysis

This paper addresses the problem of face recognition using independent component analysis (ICA). More specifically, we are going to address two issues on face representation using ICA. First, as the independent components (ICs) are independent but not orthogonal, images outside a training set cannot be projected into these basis functions directly. In this paper, we propose a least-squares solution method using Householder Transformation to find a new representation. Second, we demonstrate that not all ICs are useful for recognition. Along this direction, we design and develop an IC selection algorithm to find a subset of ICs for recognition. Three public available databases, namely, MIT AI Laboratory, Yale University and Olivette Research Laboratory, are selected to evaluate the performance and the results are encouraging.     

Combining nonlinear independent component analysis and neural network for the prediction of Asian stock market indexes

With the economic successes of several Asian economies and their increasingly important roles in the global financial market, the prediction of Asian stock markets has becoming a hot research area. As Asian stock markets are highly dynamic and exhibit wide variation, it may more realistic and practical that assumed the stock indexes of Asian stock markets are nonlinear mixture data. In this research, a time series prediction model by combining nonlinear independent component analysis (NLICA) and neural network is proposed to forecast Asian stock markets. NLICA is a novel feature extraction technique to find independent sources from observed nonlinear mixture data where no relevant data mixing mechanisms are available. In the proposed method, we first use NLICA to transform the input space composed of original time series data into the feature space consisting of independent components representing underlying information of the original data. Then, the ICs are served as the input variables of the neural network to build prediction model. Among the Asian stock markets, Japanese and China’s stock markets are the biggest two in Asia and they respectively represent the two types of stock markets. Therefore, in order to evaluate the performance of the proposed approach, the Nikkei 225 closing index and Shanghai B-share closing index are used as illustrative examples. Experimental results show that the proposed forecasting model not only improves the prediction accuracy of the neural network approach but also outperforms the three comparison methods. The proposed stock index prediction model can be therefore a good alternative for Asian stock market indexes.     

Ensemble learning for independent component analysis

It is well known that the applicability of independent component analysis (ICA) to high-dimensional pattern recognition tasks such as face recognition often suffers from two problems. One is the small sample size problem. The other is the choice of basis functions (or independent components). Both problems make ICA classifier unstable and biased. In this paper, we propose an enhanced ICA algorithm by ensemble learning approach, named as random independent subspace (RIS), to deal with the two problems. Firstly, we use the random resampling technique to generate some low dimensional feature subspaces, and one classifier is constructed in each feature subspace. Then these classifiers are combined into an ensemble classifier using a final decision rule. Extensive experimentations performed on the FERET database suggest that the proposed method can improve the performance of ICA classifier.     

Lattice independent component analysis for functional magnetic resonance imaging

We introduce a lattice independent component analysis (LICA) unsupervised scheme to functional magnetic resonance imaging (fMRI) data analysis. LICA is a non-linear alternative to independent component analysis (ICA), such that ICA’s statistical independent sources correspond to LICA’s lattice independent sources. In this paper, LICA uses an incremental lattice source induction algorithm (ILSIA) to induce the lattice independent sources from the input dataset. The ILSIA computes a set of Strongly Lattice Independent vectors using properties of lattice associative memories regarding Lattice Independence and Chebyshev best approximation. The lattice independent sources constitute a set of Affine Independent vectors that define a simplex covering the input data. LICA carries out data linear unmixing based on the lattice independent sources basis. Therefore, LICA is a hybrid combination of a non-linear lattice based component and a linear unmixing component. The principal advantage over ICA is that LICA does not impose any probabilistic model assumptions on the data sources. We compare LICA with ICA in two case studies. Firstly, including simulated fMRI data, LICA discovers the spatial location of meaningful sources with less ambiguity than ICA. Secondly, including real data from an auditory stimulation experiment, LICA improves over some state of the art ICA variants discovering the activation patterns detected by Statistical Parametric Mapping (SPM) on the same data.     

Eigenfilter design of linear-phase FIR digital filters using neural minor component analysis

This paper proposes a minor component analysis-based neural learning algorithm for designing linear-phase finite impulse response digital filters. The objective function to be minimized in the least-squares design can be formulated as the eigenvalue problem for solving an appropriate real, symmetric, and positive-definite matrix. To achieve the eigenfilter design, an alternative neural learning rule based on the minor component analysis algorithm is exploited. The optimal filter coefficients corresponding to the eigenvector of the smallest eigenvalue of the positive-definite matrix can be achieved in an iterative manner, avoiding the complex computation of eigenvalue decomposition. Furthermore, the learning step parameter that affects the convergence performance is investigated empirically. The simulation results indicate that the proposed neural-based approach can be applied to eigenfilter design and yields a lower computational complexity compared with traditional matrix algebraic-based approaches.     

A general theory of a class of linear neural nets for principal and minor component analysis

This paper presents a unified theory of a class of learning neural nets for principal component analysis (PCA) and minor component analysis (MCA). First, some fundamental properties are addressed which all neural nets in the class have in common. Second, a subclass called the generalized asymmetric learning algorithm is investigated, and the kind of asymmetric structure which is required in general to obtain the individual eigenvectors of the correlation matrix of a data sequence is clarified. Third, focusing on a single-neuron model, a systematic way of deriving both PCA and MCA learning algorithms is shown, through which a relation between the normalization in PCA algorithms and that in MCA algorithms is revealed. This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–21, 1998     

Negatively correlated neural networks for classification

This paper presents a new algorithm for designing neural network ensembles for classification problems with noise. The idea behind this new algorithm is to encourage different individual networks in an ensemble to learn different parts or aspects of the training data so that the whole ensemble can learn the whole training data better. Negatively correlated neural networks are trained with a novel correlation penalty term in the error function to encourage such specialization. In our algorithm, individual networks are trained simultaneously rather than independently or sequentially. This provides an opportunity for different networks to interact with each other and to specialize. Experiments on two real-world problems demonstrate that the new algorithm can produce neural network ensembles with good generalization ability. This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan January 19–21, 1998     

Constructing and training feed-forward neural networks for pattern classification

A new approach of constructing and training neural networks for pattern classification is proposed. Data clusters are generated and trained sequentially based on distinct local subsets of the training data. Obtained clusters are then used to construct a feed-forward network, which is further trained using standard algorithms operating on the global training set. The network obtained using this approach effectively inherits the knowledge from the local training procedure before improving on its generalization ability through the subsequent global training. Various experiments demonstrate the superiority of this approach over competing methods.     

主成分分析与神经网络的结合在多变量序列预测中的应用

目前预测方法的研究主要集中在单变量时间序列上,本文建立起一种针对多元变量非线性时间序列建模和预测的方法框架.首先,同时考虑序列状态间的线性相关性和非线性相关性,建立初始延迟窗以包含充分的预测信息;然后,利用主成分分析(PCA)方法寻找不同变量在数据空间中的最大方差方向,扩展PCA应用于提取多个变量的综合信息,重构多元变量输入状态相空间;最后,利用神经网络逼近不同变量之间以及当前状态和将来状态之间的函数映射关系,实现多元变量预测.对Ro¨ssler混沌方程和大连降雨、气温序列的预测仿真说明了本文方法的有效性,为多元变量时间序列分析提供了一条新的途径.     

基于独立分量分析的高光谱遥感影像决策树分类

为解决高光谱遥感影像波段众多所带来的信息丰富与“维数灾难”间的矛盾并提高分类精度,针对传统特征选择方法信息损失大的缺陷,基于EO-1 Hyperion高光谱遥感影像,采用独立分量分析(ICA)和决策树分类(DTC)方法联合运作流程,开展影像的地物分类实验研究,提出了ICA-DTC模型。首先运用ICA方法对影像进行特征提取,并以所提取的独立分量特征及其他地理辅助要素组成分类指标集;继而选择适当的指标组合和阈值设定判别规则,建立DTC模型进行影像的地物分类;最后将分类结果与传统最大似然分类法进行比对。结果显示：从分类的总体精度看,前者可达89.34%,高出后者18.8%;从单一地物的分类精度看,前者仅水体的精度略低于后者,而其他11种地物的精度都高于后者。理论分析与实验结果均表明,ICA-DTC模型可有效提高复杂地形条件下的地物分类精度。     

一种基于FastICA的波达方向估计新方法

独立分量分析是一种新颖的盲源分离技术，该方法作为目前信号处理领域的一项新技术，具有非常重要的理论意义和实用价值，已广泛应用于通讯、雷达信号处理、生物医学图像处理、模式识别等众多领域。简要介绍了独立分量分析的基本原理和算法，并提出将快速独立分量分析（FastICA）方法应用于波达方向估计（DOA），通过仿真实验和分析，可以得到DOA的一种简单估计，实验结果亦表明该算法在波达方向估计应用中的可行性和有效性。     

Application of independent component analysis in removing artefacts from the electrocardiogram

Routinely recorded electrocardiograms (ECGs) are often corrupted by different types of artefacts and many efforts have been made to enhance their quality by reducing the noise or artefacts. This paper addresses the problem of removing noise and artefacts from ECGs using independent component analysis (ICA). An ICA algorithm is tested on three-channel ECG recordings taken from human subjects, mostly in the coronary care unit. Results are presented that show that ICA can detect and remove a variety of noise and artefact sources in these ECGs. One difficulty with the application of ICA is the determination of the order of the independent components. A new technique based on simple statistical parameters is proposed to solve this problem in this application. The developed technique is successfully applied to the ECG data and offers potential for online processing of ECG using ICA.     

基于独立分量分析和BP网络的电子鼻模式识别

为了提高电子鼻对混合气体的识别率,针对气体传感器阵列的交叉敏感特性,探讨了在电子鼻系统中基于独立分量分析(ICA)算法与BP神经网络相结合进行模式识别的可行性。并对4个气体传感器组成的电子鼻对4种气体混合物所测得的原始数据进行处理,结果表明:ICA算法对数据进行有效预分类,减少了样本之间的相关性,将生成的新样本作为BP网络的输入,使网络结构简化,在保证一定正确率的前提下,大大提高网络的学习速度。利用该方法可以提高电子鼻识别混合气体的准确率。     

A class of neural networks for independent component analysis

Independent component analysis (ICA) is a recently developed, useful extension of standard principal component analysis (PCA). The ICA model is utilized mainly in blind separation of unknown source signals from their linear mixtures. In this application only the source signals which correspond to the coefficients of the ICA expansion are of interest. In this paper, we propose neural structures related to multilayer feedforward networks for performing complete ICA. The basic ICA network consists of whitening, separation, and basis vector estimation layers. It can be used for both blind source separation and estimation of the basis vectors of ICA. We consider learning algorithms for each layer, and modify our previous nonlinear PCA type algorithms so that their separation capabilities are greatly improved. The proposed class of networks yields good results in test examples with both artificial and real-world data.     

Training neural networks with harmony search algorithms for classification problems

Training neural networks (NNs) is a complex task of great importance in the supervised learning area. However, performance of the NNs is mostly dependent on the success of training process, and therefore the training algorithm. This paper addresses the application of harmony search algorithms for the supervised training of feed-forward (FF) type NNs, which are frequently used for classification problems. In this paper, five different variants of harmony search algorithm are studied by giving special attention to Self-adaptive Global Best Harmony Search (SGHS) algorithm. A structure suitable to data representation of NNs is adapted to SGHS algorithm. The technique is empirically tested and verified by training NNs on six benchmark classification problems and a real-world problem. Among these benchmark problems two of them have binary classes and remaining four are n-ary classification problems. Real-world problem is related to the classification of most frequently encountered quality defect in a major textile company in Turkey. Overall training time, sum of squared errors, training and testing accuracies of SGHS algorithm, is compared with the other harmony search algorithms and the most widely used standard back-propagation (BP) algorithm. The experiments presented that the SGHS algorithm lends itself very well to the training of NNs and also highly competitive with the compared methods in terms of classification accuracy.     

Some global and local convergence analysis on the information-theoretic independent component analysis approach

In this paper, we present a detailed theoretical analysis on the information-theoretic Independent Component Analysis (IT-ICA) approach. We first provide a number of lemmas and theorems on properties of the corresponding cost function in the general n-channel case with differentiable, odd, monotonic decreasing nonlinearity. A theorem on behaviour of the cost function along a radially outward line is given for characterizing the global configuration of the cost function in the parameter space. Furthermore, on the 2-channel IT-ICA system with cubic nonlinearity, we not only exhaustively solve out all equilibrium points and the condition for stability, but also give a global convergence theorem.     

Evolution of neural networks for classification and regression

Although Artificial Neural Networks (ANNs) are importantdata mining techniques, the search for the optimal ANN is a challenging task: the ANN should learn the input–output mapping without overfitting the data and training algorithms may get trapped in local minima. The use of Evolutionary Computation (EC) is a promising alternative for ANN optimization. This work presents two hybrid EC/ANN algorithms: the first evolves neural topologies while the latter performs simultaneous optimization of architectures and weights. Sixteen real-world tasks were used to test these strategies. Competitive results were achieved when compared with a heuristic model selection and other Data Mining algorithms.