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.     

A transform domain SVD filter for suppression of muscle noise artefacts in exercise ECG's

The proposed filter assumes the noisy electrocardiography (ECG) to be modeled as a signal of deterministic nature, corrupted by additive muscle noise artefact. The muscle noise component is treated to be stationary with known second-order characteristics. Since noise-free ECG is shown to possess a narrow-band structure in discrete cosine transform (DCT) domain and the second-order statistical properties of the additive noise component is preserved due to the orthogonality property of DCT, noise abatement is easily accomplished via subspace decomposition in the transform domain. The subspace decomposition is performed using singular value decomposition (SVD). The order of the transform domain SVD filter required to achieve the desired degree of noise abatement is compared to that of a suboptimal Wiener filter using DCT. Since the Wiener filter assumes both the signal and noise structures to be statistical, with a priori known second-order characteristics, it yields a biased estimate of the ECG beat as compared to the SVD filter for a given value of mean-square error (mse). The filter order required for performing the subspace smoothing is shown to exceed a certain minimal value for which the mse profile of the SVD filter follows the minimum-mean-quare error (mmse) performance warranted by the suboptimal Wiener filter. The effective filter order required for reproducing clinically significant features in the noisy ECG is then set by an upper bound derived by means of a finite precision linear perturbation model. A significant advantage resulting from the application of the proposed SVD filter lies in its ability to perform noise suppression independently on a single lead ECG record with only a limited number of data samples.     

心电信号检测中滤除肌电干扰的方法

心电信号检测是医生诊断治疗心血管疾病的重要辅助手段,但由于心电信号检测实际条件不理想,心电信号中常混有各种干扰信号,常见有肌电干扰、基线漂移和工频干扰。文中针对肌电干扰,采用数字滤波方法进行了去除噪声的滤波器设计。并通过对心电图信号滤波器的设计,不仅提高了ECG信号滤波器的去噪效果,且提高了工作效率。     

基于独立成份分析的肌电信号消噪方法研究

本文提出了一种基于独立成份分析(ICA)的改进方法,去除肌电信号中的心电噪声。该方法基于独立成份分析算法,在肌电成份中分解出心电独立成份:对分解出的心电独立成份进行低通滤波处理,进一步降低了其中肌电成份;而将其反向投影回原始信号空间,并从原始信号中除去,以便在最大程度上消除肌电信号中的心电干扰成份。真实16通道单极性肌电信号的处理和分析表明,该方法取得了较好的效果。     

一种因果维纳滤波器的推导方法

维纳滤波是一种以最小均方误差为最优准则的线性滤波器。它利用了平稳随机过程的相关特性和频谱特性对混有噪声的信号进行滤波。它是目前基本的滤波方法之一,也是当今＂现代数字信号处理＂课程的经典内容之一。为了加深对这一内容的理解,利用了单边Z变换的性质,提出一种基于单边Z变换的因果维纳滤波器的推导方法,旨在对维纳滤波器的进一步研究提供了一种新的途径。     

用小波变换对ECG信号进行去噪研究

小波消失矩阶数的不同,对应的小波滤波器的幅频曲线也不相同,因此选用不同的小波滤波器对信号进行滤波,去噪效果会有明显差异。本文通过数学建模研究分析小波滤波器的幅频特性,明确小波幅频特征及与小波滤波器消失矩的阶数之间的关系,为选择最优小波滤波器提供理论依据。本文提出针对ECG噪声的频率特点实现精确陷波去噪,有效的保留了信号的奇异点与特征值,减少了信号失真。实验结果表明,选择具有相对最优消失矩阶数的提升小波滤波器对ECG进行去噪处理,可以使信号能量分布更加集中,去噪效果更好。      

Selectivity of spatial filters for surface EMG detection from the tibialis anterior muscle

Many spatial filters have been proposed for surface electromyographic (EMG) signal detection. Although theoretical and modeling predictions on spatial selectivity are available, there are no extensive experimental validations of these techniques based on single motor unit (MU) activity detection. The aim of this study was to compare spatial selectivity of one- and two-dimensional (1-D and 2-D) spatial filters for EMG signal detection. Intramuscular and surface EMG signals were recorded from the tibialis anterior muscle of ten subjects. The simultaneous use of intramuscular wire and surface recordings (with the spike triggered averaging technique) allowed investigation of the activity of single MUs at the skin surface. The surface EMG signals were recorded with a grid of point electrodes (3 x 3 electrodes) and a ring electrode system at 15 locations over the muscle, with the wires detecting signals from the same intramuscular location. For most subjects, it was possible to classify, from the intramuscular recordings, the activity of the same MUs for all the contractions. The surface EMG signals were averaged with the intramuscularly detected MU action potentials as triggers. In this way, eight spatial filters--longitudinal and transversal, single and double differential (LSD, TSD, LDD, TDD), Laplacian (NDD), inverse binomial filter of the second order (IB2), inverse rectangle filter (IR), and differential ring system (C1)--could be compared on the basis of their spatial selectivity. The distance from the source (transversal with respect to the muscle fiber orientation) after which the surface detected potential did not exceed +/- 5% of the maximal peak-to-peak amplitude (detection distance) was statistically smaller for the 2-D systems and TDD than for the other filters. The MU action potential duration was significantly shorter with LDD and with the 2-D systems than with the other filters. The 2-D filters investigated (including C1) showed very similar performance and were, thus, considered equivalent from the point of view of spatial selectivity.     

Performance analysis of LMS adaptive prediction filters

The conditions required to implement real-time adaptive prediction filters that provide nearly optimal performance in realistic input conditions are delineated. The effects of signal bandwidth, input signal-to-noise ratio (SNR), noise correlation, and noise nonstationarity are explicitly considered. Analytical modeling, Monte Carlo simulations and experimental results obtained using a hardware implementation are utilized to provide performance bounds for specified input conditions. It is shown that there is a nonlinear degradation in the signal processing gain as a function of the input SNR that results from the statistical properties of the adaptive filter weights. The stochastic properties of the filter weights ensure that the performance of the adaptive filter is bounded by that of the optimal matched filter for known stationary input conditions     

Adaptive noise removal from biomedical signals using warpedpolynomials

Presents the time-warped polynomial filter (TWPF), a new interval-adaptive filter for removing stationary noise from nonstationary biomedical signals. The filter fits warped polynomials to large segments of such signals. This can be interpreted as low-pass filtering with a time-varying cutoff frequency. In optimal operation, the filter's cut-off frequency equals the local signal bandwidth. However, the author also presents an iterative filter adaptation algorithm, which does not rely on the (complicated) computation of the local bandwidth. The TWPF has some important advantages over existing adaptive noise removal techniques: it reacts immediately to changes in the signal's properties, independently of the desired noise reduction; it does not require a reference signal and can be applied to nonperiodical signals. In case of quasiperiodical signals, applying the TWPF to the individual signal periods leads to an optimal noise reduction. However, the TWPF can also be applied to intervals of fixed size, at the expense of a slightly lower noise reduction. This is the way nonquasiperiodical signals are filtered. The author presents experimental results which demonstrate the usefulness of the interval-adaptive filter in several biomedical applications: noise removal from ECG, respiratory and blood pressure signals, and base-line restoration of electroencephalograms (EEGs)     

基于小波变换与形态学运算的ECG自适应滤波算法

针对ECG信号常用滤波算法存在的缺陷,提出了基于小波变换与形态学运算的自适应滤波新算法。该算法利用形态学滤波器去除基线漂移信号,用小波滤波器去除高频干扰信号,并将这两部分所得到的心电噪声分量作为自适应滤波器的参考输入信号,利用自适应滤波器调整对含噪ECG信号进行滤波处理。最后,经实验验证了本文算法的有效性。     

无参考信号条件下基于MSWF的DOA估计算法

现有基于多级维纳滤波(MSWF)的子空间法波达方向(DOA)估计算法复杂度较低,但需要先验的参考信号.论文从MSWF求解线性预测问题入手,将基于MSWF的线性预测和子空间两种不同的DOA估计方法结合起来,提出了一种实用的低复杂度DOA估计算法.该算法无需构造专门的参考信号,在低信噪比或信源数估计不准的情况下,算法依然具有较好的稳健性和估计性能.仿真实验验证了本文的结论.     

Application of partition-based median type filters for suppressingnoise in images

An adaptive median based filter is proposed for removing noise from images. Specifically, the observed sample vector at each pixel location is classified into one of M mutually exclusive partitions, each of which has a particular filtering operation. The observation signal space is partitioned based an the differences defined between the current pixel value and the outputs of CWM (center weighted median) filters with variable center weights. The estimate at each location is formed as a linear combination of the outputs of those CWM filters and the current pixel value. To control the dynamic range of filter outputs, a location-invariance constraint is imposed upon each weighting vector. The weights are optimized using the constrained LMS (least mean square) algorithm. Recursive implementation of the new filter is then addressed. The new technique consistently outperforms other median based filters in suppressing both random-valued and fixed-valued impulses, and it also works satisfactorily in reducing Gaussian noise as well as mixed Gaussian and impulse noise     

Nonideal sampling and interpolation from noisy observations in shift-invariant spaces

Digital analysis and processing of signals inherently relies on the existence of methods for reconstructing a continuous-time signal from a sequence of corrupted discrete-time samples. In this paper, a general formulation of this problem is developed that treats the interpolation problem from ideal, noisy samples, and the deconvolution problem in which the signal is filtered prior to sampling, in a unified way. The signal reconstruction is performed in a shift-invariant subspace spanned by the integer shifts of a generating function, where the expansion coefficients are obtained by processing the noisy samples with a digital correction filter. Several alternative approaches to designing the correction filter are suggested, which differ in their assumptions on the signal and noise. The classical deconvolution solutions (least-squares, Tikhonov, and Wiener) are adapted to our particular situation, and new methods that are optimal in a minimax sense are also proposed. The solutions often have a similar structure and can be computed simply and efficiently by digital filtering. Some concrete examples of reconstruction filters are presented, as well as simple guidelines for selecting the free parameters (e.g., regularization) of the various algorithms.     

Adaptive alpha-trimmed mean filters under deviations from assumed noise model

Alpha-trimmed mean filters are widely used for the restoration of signals and images corrupted by additive non-Gaussian noise. They are especially preferred if the underlying noise deviates from Gaussian with the impulsive noise components. The key design issue of these filters is to select its only parameter, alpha, optimally for a given noise type. In image restoration, adaptive filters utilize the flexibility of selecting alpha according to some local noise statistics. In the present paper, we first review the existing adaptive alpha-trimmed mean filter schemes. We then analyze the performance of these filters when the underlying noise distribution deviates from the Gaussian and does not satisfy the assumptions such as symmetry. Specifically, the clipping effect and the mixed noise cases are analyzed. We also present a new adaptive alpha-trimmed filter implementation that detects the nonsymmetry points locally and applies alpha-trimmed mean filter that trims out the outlier pixels such as edges or impulsive noise according to this local decision. Comparisons of the speed and filtering performances under deviations from symmetry and Gaussian assumptions show that the proposed filter is a very good alternative to the existing schemes.     

Entropy-based optimization of wavelet spatial filters

A new class of spatial filters for surface electromyographic (EMG) signal detection is proposed. These filters are based on the 2-D spatial wavelet decomposition of the surface EMG recorded with a grid of electrodes and inverse transformation after zeroing a subset of the transformation coefficients. The filter transfer function depends on the selected mother wavelet in the two spatial directions. Wavelet parameterization is proposed with the aim of signal-based optimization of the transfer function of the spatial filter. The optimization criterion was the minimization of the entropy of the time samples of the output signal. The optimized spatial filter is linear and space invariant. In simulated and experimental recordings, the optimized wavelet filter showed increased selectivity with respect to previously proposed filters. For example, in simulation, the ratio between the peak-to-peak amplitude of action potentials generated by motor units 20 degrees apart in the transversal direction was 8.58% (with monopolar recording), 2.47% (double differential), 2.59% (normal double differential), and 0.47% (optimized wavelet filter). In experimental recordings, the duration of the detected action potentials decreased from (mean +/- SD) 6.9 +/- 0.3 ms (monopolar recording), to 4.5 +/- 0.2 ms (normal double differential), 3.7 +/- 0.2 (double differential), and 3.0 +/- 0.1 ms (optimized wavelet filter). In conclusion, the new class of spatial filters with the proposed signal-based optimization of the transfer function allows better discrimination of individual motor unit activities in surface EMG recordings than it was previously possible.     

Dual H∞ Algorithms for Signal Processing— Application to Speech Enhancement

This paper deals with the joint signal and parameter estimation for linear state-space models. An efficient solution to this problem can be obtained by using a recursive instrumental variable technique based on two dual Kalman filters. In that case, the driving process and the observation noise in the state-space representation for each filter must be white with known variances. These conditions, however, are too strong to be always satisfied in real cases. To relax them, we propose a new approach based on two dual H_infin filters. Once a new observation of the disturbed signal is available, the first H_infin algorithm uses the latest estimated parameters to estimate the signal, while the second H_infin algorithm uses the estimated signal to update the parameters. In addition, as the H_infin filter behavior depends on the choice of various weights, we present a way to recursively tune them. This approach is then studied in the following cases: (1) consistent estimation of the AR parameters from noisy observations and (2) speech enhancement, where no a priori model of the additive noise is required for the proposed approach. In each case, a comparative study with existing methods is carried out to analyze the relevance of our solution.     

Estimation of motor unit conduction velocity from surface EMG recordings by signal-based selection of the spatial filters

Muscle fiber conduction velocity (CV) can be estimated by the application of a pair of spatial filters to surface electromagnetic (EMG) signals and compensation of the spatial filter transfer function with equivalent temporal filters. This method integrates the selection of the spatial filters for signal detection to the estimation of CV. Using this approach, in this paper, we propose a novel technique for signal-based selection of the spatial filter pair that minimizes the effect of nonpropagating signal components (end-of-fiber effects) on CV estimates (optimal filters). The technique is applicable to signals with one propagating and one nonpropagating component, such as single motor unit action potentials. It is shown that the determination of the optimal filters also allows the identification of the propagating and nonpropagating signal components. The new method was applied to simulated and experimental EMG signals. Simulated signals were generated by a cylindrical, layered volume conductor model. Experimental signals were recorded from the abductor pollicis brevis with a linear array of 16 electrodes. In the simulations, the proposed approach provided CV estimates with lower bias due to nonpropagating signal components than previously proposed methods based on the entire signal waveform. In the experimental signals, the technique separated propagating and nonpropagating signal components with an average reconstruction error of 2.9 +/- 0.9% of the signal energy. The technique may find application in single motor unit studies for decreasing the variability and bias of CV estimates due to the presence and different weights of the nonpropagating components.     

An Improved SAR Image Denoising Metho d Based on Bo otstrap Statistical Estimation with ICA Basis

A new method for Synthetic aperture radar (SAR) image denoising is proposed. The prior information of speckle statistical model can be exploited to judge its distribution. The basis of SAR image can be estimated by Independent component analysis (ICA), and these bases can be divided into two different subspaces (noise and real signal subspaces) through a linear classifier. Then para-metric Bootstrap estimates the parameters of speckle sta-tistical model on the noise signal subspace, and the non-parametric Bootstrap can estimate the distribution of real image on the real signal subspace. According to different results estimated by Bootstrap, corresponding Maximum a posterior probability (MAP) filter will be selected for im-age denoising, using the noise model’s parameter for adap-tive filtering. Experiments show that the image processed by this new method can achieve a better visual perception and ob jective evaluation results.     

从混合有色噪声背景中提取正弦随相调频信号的方法

提出了从混合有色背景噪声中提取正弦随机调频信号方法－－基于高阶累积量FIR自适应滤波方法。其主要特点是：FIR自适应滤波系数是用输入信号的四阶以上累积量进行更新的；FIR自适应滤波器是收敛于与有用信号类型匹配的滤波器。因此，它既能有效地提取有用信号（匹配信号），又能有效地抑制混合有色噪声（非匹配信号）。仿真结果表明，与短时相关方法相比，该方法是很有效的。     

防离子反馈MCP噪声因子测试与分析

噪声因子是输入信噪比和输出信噪比的比值,能够反映微通道板的噪声特性,是影响微光像增强器信噪比的主要因素。为探寻降低微光像增强器中防离子反馈微通道板噪声因子的技术途径,根据微通道板噪声因子定义和测试原理,构建了防离子反馈微通道板噪声因子测试系统。由于防离子反馈微通道板的输入面镀覆有一层薄膜,其对微通道板的噪声因子有较大影响。因此,利用噪声因子测试系统重点测试了有、无防离子反馈膜以及不同材料、不同孔径、不同输入电子能量、不同微通道板作电压条件下的微通道板噪声因子,获得了微通道板噪声因子与输入电子能量、微通道板电压之间的关系,为降低防离子反馈微通道板噪声提供了有效的技术指导。