QRS feature extraction using linear prediction

This communication proposes a method called linear prediction (a high performant technique in digital speech processing) for analyzing digital ECG signals. There are several significant properties indicating that ECG signals have an important feature in the residual error signal obtained after processing by Durbin's linear prediction algorithm. This communication also indicates that the prediction order need not be more than two for fast arrhythmia detection. The ECG signal classification puts an emphasis on the residual error signal. For each ECG's QRS complex, the feature for recognition is obtained from a nonlinear transformation which transforms every residual error signal to a set of three states pulse-code train relative to the original ECG signal. The pulse-code train has the advantage of easy implementation in digital hardware circuits to achieve automated ECG diagnosis. The algorithm performs very well in feature extraction in arrhythmia detection. Using this method, our studies indicate that the PVC (premature ventricular contraction) detection has at least a 92 percent sensitivity for MIT/BIH arrhythmia database.     

Comments on linear feature extraction (Corresp.)

The problem considered is that of finding the best linear transformation to reduce a random-data vectorzto a vector of smaller dimension. It is assumed that the original data are Gaussian under either of two hypotheses, and that one wishes to use the transformed data to distinguish the hypotheses. The Bhattacharya distance is used to measure the information carried by the transformed data. A compromise solution is obtained for the case in which the data have both different means and different covariances under the alternative hypotheses.     

一种基于SAR图像的典型线特征抽取方法

针对合成孔径雷达(Synthetic Aperture Radar)图像的相干斑噪声会使图像的分割和目标的提取困难的问题,文中利用改进的Lee滤波手段去除相干斑噪声,并使用照度均衡算法平衡SAR图像的光照度,最后使用图割(Graph Cuts)算法交互式地对SAR图像中的线性结构进行抽取。实验结果表明Lee滤波和照度均衡算法分别对于抑制相干斑噪声干扰和修正图像对比度有显著效果,图割算法可以有效地提取出用户感兴趣的线性结构。     

Wavelet-based feature extraction for improved endmember abundance estimation in linear unmixing of hyperspectral signals

This paper shows that the use of appropriate features, such as discrete wavelet transform (DWT)-based features, can improve the least squares estimation of endmember abundances using remotely sensed hyperspectral signals. On average, the abundance estimation deviation is reduced by 30% to 50% when using the DWT-based features, as compared to the use of original hyperspectral signals or conventional principal component analysis (PCA)-based features. Theoretical analyses further reveal that the increase of endmember separability is a fundamental reason leading to this improvement. In addition, the robustness of the DWT-based features is verified experimentally. Finally, the idea is generalized as a point that the remote sensing community needs to investigate feature extraction (or dimensionality reduction) methods that are based on signal classification, such as the DWT approach, for linear unmixing problems, rather than using feature extraction methods that are based on signal representation, such as the conventional PCA approach.     

Temporal feature extraction and clustering analysis ofelectromyographic linear envelopes in gait studies

A technique for automatically clustering linear envelopes of the EMG during gait has been developed which uses a temporal feature representation and a maximum peak matching scheme. This new technique provides a viable way to define compact and meaningful EMG waveform features. The envelope matching is performed by dynamic programming, providing qualitatively the largest numbers of matched peaks and quantitatively a minimum distance measurement. The resulting averaged EMG profiles have low statistical variation and can serve as templates for EMG comparison and further classification.     

Generalized feature extraction for time-varying autoregressivemodels

In this paper, a feature extraction scheme for a general type of nonstationary time series is described. A non-stationary time series is one in which the statistics of the process are a function of time; this time dependency makes it impossible to utilize standard globally derived statistical attributes such as autocorrelations, partial correlations, and higher order moments as features. In order to overcome this difficulty, the time series vectors are considered within a finite-time interval and are modeled as time-varying autoregressive (AR) processes. The AR coefficients that characterize the process are functions of time that may be represented by a family of basis vectors. A novel Bayesian formulation is developed that allows the model order of a time-varying AR process as well as the form of the family of basis vectors used in the representation of each of the AR coefficients to be determined. The corresponding basis coefficients are then invariant over the time window and, since they directly relate to the time-varying AR coefficients, are suitable features for discrimination. Results illustrate the effectiveness of the method     

Optimizing feature extraction for multiclass problems

Feature extraction has been an important research topic in pattern classification and has been studied extensively by many researchers. Most of the conventional feature extraction methods are performed using a criterion function defined between two classes or a global function. Although these methods work relatively well in most cases, it is generally not optimal in any sense for multiclass problems. In order to address this problem, the authors propose a method to optimize feature extraction for multiclass problems. The authors first investigate the distribution of classification accuracies of multiclass problems in the feature space and find that there exist much better feature sets that the conventional feature extraction algorithms fail to find. Then the authors propose an algorithm that finds such features. Experiments with remotely sensed data show that the proposed algorithm consistently provides better performances compared with the conventional feature extraction algorithms     

Nonparametric weighted feature extraction for classification

In this paper, a new nonparametric feature extraction method is proposed for high-dimensional multiclass pattern recognition problems. It is based on a nonparametric extension of scatter matrices. There are at least two advantages to using the proposed nonparametric scatter matrices. First, they are generally of full rank. This provides the ability to specify the number of extracted features desired and to reduce the effect of the singularity problem. This is in contrast to parametric discriminant analysis, which usually only can extract L-1 (number of classes minus one) features. In a real situation, this may not be enough. Second, the nonparametric nature of scatter matrices reduces the effects of outliers and works well even for nonnormal datasets. The new method provides greater weight to samples near the expected decision boundary. This tends to provide for increased classification accuracy.     

Discriminative temporal feature extraction for robust speechrecognition

A discriminative temporal feature processing method for robust speech recognition is presented by combining the knowledge and the statistical methods. The cepstral features are first filtered by a RASTA method based on human hearing perception and then processed using the minimum classification error algorithm. Improved recognition performance can be achieved in both quiet and noisy environments     

Wavelet packet feature extraction for vibration monitoring

Condition monitoring of dynamic systems based on vibration signatures has generally relied upon Fourier-based analysis as a means of translating vibration signals in the time domain into the frequency domain. However, Fourier analysis provided a poor representation of signals well localized in time. In this case, it is difficult to detect and identify the signal pattern from the expansion coefficients because the information is diluted across the whole basis. The wavelet packet transform (WPT) is introduced as an alternative means of extracting time-frequency information from vibration signatures. The resulting WPT coefficients provide one with arbitrary time-frequency resolution of a signal. With the aid of statistical-based feature selection criteria, many of the feature components containing little discriminant information could be discarded, resulting in a feature subset having a reduced number of parameters without compromising the classification performance. The extracted reduced dimensional feature vector is then used as input to a neural network classifier. This significantly reduces the long training time that is often associated with the neural network classifier and improves its generalization capability     

语音信号特征提取算法研究

语音作为一种搭载着特定的信息模拟信号,已成为人们社会生活中获取信息和传播信息的重要的手段。语音信号处理的目的就是在复杂的语音环境中提取有效的语音信息。环境干扰在语音传播过程中对信号的影响不容小觑,因此语音信号处理的抗噪声能力已经成为一个重要的研究方向。Matlab的应用有着广泛的领域,在信息处理领域其强大的数据处理能力可以将非平稳时变的语音数据转换为离散的数据,然后可对离散数据进行分析或者做进一步运算处理。它的信号处理工具箱可以迅速、有效地实现语音信号的处理和分析,Matlab是适用于信号处理领域的强大的处理工具。在此运用Matlab对一段包含有环境噪声的语音进行傅里叶变换、时域和频域分析、提取部分语音信号及分析信号的处理。     

Global feature extraction operations for near-sensor imageprocessing

Near-sensor image processing (NSIP) is a new approach to low-level image processing in which the ability to perform global operations is crucial. We define a 2-D global logic unit (GLU), which consists of simple logical circuits arranged in a regular net, and describe how to perform powerful segmentation and feature-extracting operations using this net. Typical segmentation operations performed by the net are hole filling, smallest circumscribing rectangle, thresholding with hysteresis, and arbitrary propagation patterns. We describe how to obtain features like the position of an object in the image. Finally, we show that the NSIP concept, including the global functions, can be implemented in a single chip using today's VLSI technology.     

Optimal windowing for wideband linear arrays

The problem of constructing a wideband beam pattern that is concentrated in both angle and frequency is discussed. This paper is a direct extension of the work of Slepian and co-workers on time- and frequency-limited functions. It is shown that the singular vectors and singular functions of the mapping relating the set of weights of a linear wideband array to its far-field directivity pattern have both concentration properties and double orthogonality properties and so they can be thought of as the wideband equivalents of the discrete prolate spheroidal sequences and wave functions. These singular functions are used to obtain approximations to a frequency-invariant beam pattern.     

Optimal supports for linear predictive models

The problem of finding the optimal set of causal pixels (support) for use in linear predictive models is addressed. After presenting counterexamples to popular intuitions about supports, a general result relating the distortion incurred with a small support to optimal coefficients of a larger support is derived. A geometrical interpretation is provided. Two algorithms that optimally increase/decrease support sizes by one at each step are presented. Experimental results illustrate the significant gains realized by the algorithms compared with commonly used supports     

New learning subspace method for image feature extraction

Image pattern recognition is the most i mportant re-search directions currently,and feature extraction is itscoretechnology.Researchinthisfield dates backat leastto the 1940s ,but most feature extraction techniques re-quire users to give a priori probabil…     

Optimization of symbolic feature extraction for pattern classification

The concept of symbolic dynamics has been used in recent literature for feature extraction from time series data for pattern classification. The two primary steps of this technique are partitioning of time series to optimally generate symbol sequences and subsequently modeling of state machines from such symbol sequences. The latter step has been widely investigated and reported in the literature. However, for optimal feature extraction, the first step needs to be further explored. The paper addresses this issue and proposes a data partitioning procedure to extract low-dimensional features from time series while optimizing the class separability. The proposed procedure has been validated on two examples: (i) parameter identification in a Duffing system and (ii) classification of fatigue damage in mechanical structures, made of polycrystalline alloys. In each case, the classification performance of the proposed data partitioning method is compared with those of two other classical data partitioning methods, namely uniform partitioning (UP) and maximum entropy partitioning (MEP).     

Fast feature extraction method for robust face verification

A feature extraction technique for face verification is proposed. It utilises polynomial coefficients derived from 2D discrete cosine transform (DCT) coefficients of neighbouring blocks. Experimental results suggest that the technique is more robust against illumination direction changes than 2D Gabor wavelets, 2D DCT and eigenface methods. Moreover, compared to Gabor wavelets, the proposed technique is over 80 times quicker to compute.     

Magnetic energy-based feature extraction for low-quality fingerprint images

In fingerprint recognition systems, feature extraction is an important part because of its impact on the final performance of the overall system, particularly, in the case of low-quality images, which poses significant challenges to traditional fingerprint feature extraction methods. In this work, we make two major contributions: First, a novel feature extraction method for low-quality fingerprints images is proposed, which mimics the magnetic energy when attracting iron fillings, and this method is based on image energies attracting uniformly distributed points to form the final features that can describe a fingerprint. Second, we created a new low-quality fingerprints image database to evaluate the proposed method. We used a mobile phone camera to capture the fingerprints of 136 different persons, with five samples for each to obtain 680 fingerprint images in total. To match the computed features, we used the dynamic time warping and evaluated the performance of our system based on k-nearest neighbor classifier. Further, we represent the features using their probability density functions to evaluate the method using some other classifiers. The highest identification accuracy recorded by several experiments reached 95.11% using our in-house database. The experimental results show that the proposed method can be used as a general feature extraction method for other applications.     

Sinh-Domain linear transformation filters

A new systematic method for designing Sinh-Domain linear transformation (LT) filters is introduced in this article. For this purpose, a substitution scheme containing the Sinh-Domain LT equivalent of each passive prototype has been introduced. The proposed equivalents have been realised by employing appropriate Sinh-Domain building blocks with low-voltage operation capability. As an example, a third-order Sinh-Domain elliptic LT filter has been designed and its performance has been evaluated through simulation results. In addition, a detailed comparison with the corresponding Sinh-Domain and log-domain counterparts has been performed and the obtained results have been further discussed.