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.     

Evaluation and application of a RBF neural network for online single-sweep extraction of SEPs during scoliosis surgery

A method for on-line single sweep detection of somatosensory evoked potentials (SEPs) during intraoperative neuromonitoring is proposed. It is based on a radial-basis function neural network with Gaussian activations. In order to improve its tracking capabilities, the radial-basis functions location is partially learnt sweep-by-sweep; the training algorithm is effective, though consistent with real-time applications. This new detection method has been tested on simulated data so as to set the network parameters. Moreover, it has been applied to real recordings obtained from a new neuromonitoring technique which is based on the simultaneous observation of the SEP and of the evoked H-reflex elicited by the same electric stimulus. The SEPs have been extracted using the neural network and the results have then been compared to those obtained by ARX filtering and correlated with the spinal cord integrity information obtained by the H-reflex. The proposed algorithm has been proved to be particularly effective and suitable for single-sweep detection. It is able to track both sudden and smooth signal changes of both amplitude and latency and the needed computational time is moderate.     

基于FPGA的H.264 DCT算法的硬件实现

二维离散余弦（DCT）在H.264视频编码中承担者信号从时域到频域变换的作用。在现场可编程逻辑门阵列（FPGA）上设计了高效的采用流水线结构的H.264DCT硬件电路。首先,把二维4×4DCT变换转换成二次一维DCT变换;其次,DCT变换之间加一个两端口的RAM,以实现数列的转置;最后,在顶层设计一个有限状态机控制整个流程。该设计采用较少的资源实现了较好的功能,获得了可靠的实验结果。     

一种基于人耳掩蔽效应的DCT域音频水印

文中提出了一种基于听觉掩蔽效应的DCT域音频水印算法.首先根据人耳听觉系统的心理声学模型估算每帧音频数据的掩蔽阈值,然后利用量化的思想将水印信号自适应地嵌入到掩蔽阈值以下的频率分量所对应的DCT域系数中.仿真实验结果表明:与传统DFT方法相比,该算法具有更好的透明性和鲁棒性.     

Subspace regularization method for the single-trial estimation ofevoked potentials

A method for the single-trial estimation of the evoked potentials is proposed. The method is based on the so-called subspace regularization approach in which the second-order statistics of the set of the measurements is used to form a prior information model for the evoked potentials. The method is closely related to the Bayesian estimation. The performance of the proposed method is evaluated using realistic simulations. As a specific application the method is applied to the estimation of the target responses in the P300 test.     

联合稀疏表示的双次诱发电位提取算法

诱发电位少次提取对于研究大脑活动规律以及临床诊断等具有重要意义.根据脑电信号的特点,本文提出一种基于联合稀疏表示的双次诱发电位信号估计算法.利用诱发电位信号的准周期性和自发脑电信号的随机性,该算法将脑电信号看作为相似成分和相异成分的叠加.神经系统通过相同刺激产生的诱发电位主要在潜伏期和波幅两方面发生变化,因此该算法利用平均诱发电位进行建模,得到稀疏字典,通过联合稀疏表示算法实现双次诱发电位信号的提取.实验结果表明,该算法和其他算法相比获得了更好的效果.     

Coherence-weighted wiener filtering of somatosensory evokedpotentials

In this paper, we present a Wiener filtering (WF) approach for extraction of somatosensory evoked potentials (SEPs) from the background electroencephalogram (EEG), with sweep-to-sweep variations in its signal power. To account for the EEG power variations, WF is modified by iteratively weighting the power spectrum using the coherence function. Coherence-weighted Wiener filtering (CWWF) is able to extract SEP waveforms, which have a greater level of detail as compared with conventional time-domain averaging (TDA). Using CWWF, the components of the SEP show significantly less variability. As such, CWWF should be useful as an important diagnostic tool able to detect minimal changes in the SEP. In an experimental study of cerebral hypoxia, CWWF is shown to be more responsive to detection of injury than WF or TDA.     

数字水印信道模型的快速估算

近年来，数字水印技术开始广泛用于数字图象的版权保护并提出了大量的数字水印方案，在本文中分析了一种用于静态图象版权保护的类似于扩谱技术的DCT变换域的数字水印信道模型，在JPEG算法中，DCT变换是作用在8％8大小的像素分块上，数字水印通过编码信息来跟踪电子产品的非法使用，我们用广义高斯分布去作为自然界图像经过DCT变换后的分布模型，作为我们工作的结果，信道模型的快速估算表面式被推导并和实验结果进行了比较。     

基于奇异值分解的DCT域音频数字水印算法

提出了一种基于DCT直流分量的音频数字水印算法。算法根据音频信号在DCT域能量较大处可以隐藏较多信息的水印嵌入原则,结合音频信号在DCT域能量分布特征,首先把原音频信号先进行分段处理,然后对每段做DCT变换,找出水印嵌入点,实现了在一段音频信号中隐藏一幅二值图像的奇异值向量。实验结果表明,该算法对MP3压缩、低通滤波、重采样等恶性攻击具有较强的鲁棒性和不可感知性。     

基于分形理论的麻醉监测诱发脑电信号识别方法研究

提出了一种基于分形理论的麻醉监测诱发脑电信号识别方法.首先给出了麻醉监测中潜伏期听觉诱发脑电信号的数学模型,产生与实际信号相符的模拟脑电信号,然后对脑电信号进行小波降噪,提取降噪后脑电信号的关联维数,最后通过关联维数的大小识别麻醉状态下中潜伏期听觉诱发脑电信号的类型.实验仿真结果表明:提出的识别方法具有较高的识别率.     

基于DCT与维纳滤波的单通道语音增强算法

针对复杂噪声背景下的语音增强问题,基于离散余弦变换(DCT)和维纳滤波提出了一种新的单通道语音增强算法。该算法不依赖任何语音信号模型且无需对噪声的统计特性进行先验假定,它利用DCT域中连续时刻语音信号分量间的相关特性结合最小均方误差算法实现纯净语音分量的最优估计,弥补了一般算法仅依赖单帧带噪语音对语音分量估计得不足。多种噪声背景下的仿真结果表明,该算法在主观和客观测试中都具有良好的语音增强效果。     

Image filtering in the block DCT domain using symmetric convolution

Processing of images in the transform domain saves computation by avoiding inverse and re-transform operations. In this paper, we present a technique for filtering of images in the transform domain using symmetric convolution in the block DCT space. Due to the application of convolution-multiplication property in the DCT domain, the filtering operation requires significantly less computation than its equivalent in the original signal/image space. To take care of discontinuities along boundaries of blocks, filtering is performed on a larger DCT block composed from adjacent blocks. Subsequently, the filtered DCT block is obtained by decomposing it. The proposed filtering technique achieves the same results of linear convolution in the spatial domain with reduced cost. With the proposed filtering, it is possible to significantly speedup the operation by ignoring some elements in the filtering matrices whose magnitudes are smaller than a threshold value. Typical sparseness of DCT domain input blocks is also considered for further reduction of computational cost. The proposed method uses simple linear operations such as matrix multiplication, which is appropriate for efficient hardware implementations. We also demonstrate its applications in image sharpening and removal of blocking artifacts directly in the compressed domain.     

Parametric modeling of somatosensory evoked potentials

In this paper, we examine methods of characterizing somatosensory evoked potentials (SEP's) in both the time and frequency domains. We have found that the truncated impulse response (TIR) method produced an accurate time domain model of the SEP signals at model orders greatly reduced from the original state space matrix. The TIR method was valuable for smoothing signals that were slightly corrupted by noise. In this case, the simulated data sequence was close to the original data sequence in the mean squared error sense. For signals that were greatly corrupted by noise, the TIR method was not able to perform as well. Therefore, the TIR method was not a feature extraction method but was valuable for data simulation. In the frequency domain, we have used the autoregressive moving average model (ARMA) to parameterize the SEP signal. An overdetermined set of Yule-Walker equations was created to determine the autoregressive (AR) parameters of the original data with the model order established by the singular value decomposition. From these AR parameters, a residual time series was generated which was used to find the moving average parameters. The resulting ARMA model was used to produce a simulated data sequence. The frequency domain characteristics of the simulated sequence and the corresponding power spectral density of the ARMA filter were very close to the periodogram of the original data sequence. Accurate parameterization was achieved for the SEP waveforms at low filter lengths.     

Nonlinear system identification and overparameterization effects in multisensory evoked potential studies

Traditional signal processing techniques have not been suitable in establishing contributions from different sensory paths in multisensory evoked potentials. In this paper, a nonlinear modeling technique is proposed to demonstrate the possible mechanisms of interaction between sensory paths. The nonlinear autoregressive with exogenous inputs (NARX) model is explored to establish a relationship between electrical activities of the brain obtained by unimodal and by bimodal stimulation. The intersensory phenomenon concept is extended using nonlinear system theory and applied to show the possible interactions between the visual and auditory sensory paths. In addition, the paper addresses the compensation phenomenon caused by overparameterization in the NARX algorithm when it is applied to event-related potentials. It is hoped that the nonlinear modeling approach will generate hypotheses about the intersensory interaction phenomenon, improving and advancing its theoretical formulation.     

光电跟踪伺服系统的自适应差分进化算法辨识

为了获得准确的光电跟踪伺服系统的模型,采用自适应差分进化算法对光电跟踪伺服系统进行辨识研究,该算法根据辨识误差平方和自动调整变异、交叉因子。在输入为正弦离散数字信号下辨识系统的离散模型。为了验证算法的有效性,在频域内与扫频法辨识的一、二阶模型和系统实际输出比较研究。实验结果表明:在相同正弦离散信号条件下,辨识输出与系统实际输出基本一致,与扫频法的RMSE相比减小了20.33%,二阶模型在高频段偏离系统实际输出稍大些,一阶系统输出与系统实际输出基本一致。研究结果表明,自适应差分进化算法计算量小,方法简便,辨识准确,在光电跟踪伺服控制领域具有一定的工程应用价值。     

DCT域网格编码量化及其在图像量化中的应用

本文提出了在离散余弦变换(DCT)域内作网格编码量化(TCQ)的新方法.该方法不仅利用了信号间的时间相关性,而且也利用了信号变换域内的相关性,该方法首先用一维或二维DCT变换减小变换域内的相关性,然后利用卷集编码和信号空间扩展来增大量化信号间的欧氏距离,并用维持比算法寻找最优量化序列.仿真结果表明,基于二维DCT的TCQ方法在相对大的灰度率下,比传统的TCQ方法好2dB左右,与此同时,TCQ又比最优标量量化好2dB左右.该方法还具有编码计算复杂度适中,解码简单以及对误差扩散不敏感的优点.     

Deterioration of average evoked potential waveform due toasynchronous averaging and its compensation

In order to improve the signal-to-noise ratio of evoked potentials, the responses to repetitive stimuli are usually recorded, and the averaging method is applied to them. If each starting point of the averaging varies with respect to the response because of asynchronous sampling of the measurement data, the average waveform of the evoked potential signal is distorted by the averaging. We analyzed the asynchronous averaging in the frequency domain and derived a relationship between the fluctuating time and the degree of the deterioration. By using this relationship, a required sampling rate was derived to ensure the accuracy of the averaging. Furthermore, we also deduced a compensation procedure for the asynchronous averaging in case of coarse sampling.     

基于熵掩蔽的DCT域恰可察觉失真模型

为提高离散余弦变换（discrete cosine transform,DCT）域恰可察觉失真（just noticeable distortion,JND）模型阈值精度并避免跨域操作，将熵掩蔽效应引入DCT域JND模型。首先，从自由能理论和贝叶斯推理出发，设计基于DCT域纹理能量相似性的自回归模型模拟视觉感知过程中的自发预测行为；其次，探索视觉感知与预测残差的映射关系得到块级无序度，并将熵掩蔽效应建模为关于无序度的JND阈值调节因子；最后，结合空间对比敏感度函数、亮度自适应掩蔽以及对比度掩蔽，提出基于熵掩蔽的DCT域JND模型。与现有DCT域JND模型相比，所提模型所有运算均在DCT域执行，更高效简洁。主观、客观实验结果表明，所提模型在感知质量相同或更好的情况下，噪声污染图的平均峰值信噪比（peak signal-to-noise ratio,PSNR）值比其他4个JND对比模型低2.04 dB，更符合人眼视觉系统的感知特性。     

语音重构的DCT域加速Landweber迭代硬阈值算法

重构信号的最基本理论依据是该信号在某个变换域是稀疏的或近似稀疏的。基于语音信号在DCT域的近似稀疏性,可以采用压缩感知(Compressed Sensing, CS)理论对其进行重构。压缩感知理论中的迭代硬阈值(Iterative hard thresholding, IHT)算法以其较好的性能被广泛用来重构信号,但其收敛速度比较慢,如何提高收敛速度,一直是迭代硬阈值算法研究的重点之一。针对压缩感知理论中的IHT算法收敛速度相当慢的问题,提出了语音重构的DCT域加速Landweber迭代硬阈值(Accelerated Landweber iterative hard thresholding, ALIHT)算法。该算法对原始语音信号做DCT变换,然后在DCT域将每一步Landweber迭代分解为矩阵计算和求解两步,通过修改其中的矩阵计算部分实现Landweber迭代加速,最后通过迭代硬阈值对信号做阈值处理。实验结果表明,加速Landweber迭代硬阈值算法加快了收敛速度、减少了计算量。      

A novel approach for precise simulation of the EMG signal detected by surface electrodes

We propose a new electromyogram generation and detection model. The volume conductor is described as a nonhomogeneous (layered) and anisotropic medium constituted by muscle, fat and skin tissues. The surface potential detected in space domain is obtained from the application of a two-dimensional spatial filter to the input current density source. The effects of electrode configuration, electrode size and inclination of the fibers with respect to the detection system are included in the transfer function of the filter. Computation of the signal in space domain is performed by applying the Radon transform; this permits to draw considerations about spectral dips and clear misunderstandings in previous theoretical derivations. The effects of generation and extinction of the action potentials at the fiber end plate and at the tendons are included by modeling the source current, without any approximation of its shape, as a function of space and time and by using again the Radon transform. The approach, based on the separation of the temporal and spatial properties of the muscle fiber action potential and of the volume conductor, includes the capacitive tissue properties.