共查询到20条相似文献,搜索用时 453 毫秒
1.
基于形态学运算和自适应阈值的心电信号消噪 总被引:1,自引:0,他引:1
抑制信号中的噪声干扰,是心电(ECG)信号预处理中的关键步骤.针对传统形态学滤波损失有用信号的缺陷,本文提出了一种基于形态学运算和自适应阈值的ECG信号消噪算法.首先,对含噪ECG信号进行形态学滤波和形态学峰谷提取运算;然后,估算形态学峰谷信号中时变噪声的即时方差,并依据3σ准则对峰谷信号进行自适应阈值处理,保留其中的有用信号;最后,将阈值处理结果与形态学滤波结果相加,作为ECG信号消噪处理的最终结果.仿真试验与实际应用结果表明,该算法不仅可以有效去除时变噪声的干扰,而且较好地保持了ECG信号的特征形态,处理效果明显优于以往的形态学滤波算法,且比基于平稳小波变换的消噪算法更适用于非平稳ECG信号的消噪处理. 相似文献
2.
3.
一种基于自适应滤波的语音降噪方法研究 总被引:1,自引:1,他引:0
分析和研究自适应滤波和小波变换法的原理及方法,提出了一种新的综合使用自适应滤波和小波变换法的语音降噪方法。该方法首先用仿生小波变换法对带噪声的语音信号进行小波分解,将小渡变换法分离出来的噪声信号作为自适应滤波器的输入。最后选择用最小均方误差(LMS)的自适应算法对带噪声语音信号进行降噪处理,实现了信噪分离,去除语音信号中的噪声信号。实验结果表明,该方法对语音信号有较为明显的降噪效果。 相似文献
4.
应用于分段连续信号的基于提升格式的双自适应小波变换 总被引:2,自引:1,他引:1
由于小波具有良好的时频特性,对于平滑的信号,利用固定尺寸的小波滤波器滤波可以获得良好的线性近似结果。然而对于某些具有突变点的信号而苦,采用固定尺寸的小波滤波器进行线性滤波并不足一个理想的选择。在Piella G提出的基于提升格式的自适应小波变换算法的基础上,本文提出了一个新的双自适应小波变换算法,将其应用于分段连续信号中得到了较好的线性近似结果。 相似文献
5.
6.
一种基于数学形态学与小波域增强的滤波算法 总被引:2,自引:0,他引:2
为了有效滤除图像高斯噪声,将数学形态学与小波域增强相结合,提出了一种高斯噪声新型滤波算法.该算法首先将噪声图像进行二维小波分解,得到低频和高频子图像;然后保留低频子图像不变,对各高频子图像根据其噪声分布特点分别设计出多角度、多结构逐级形态学滤波器进行滤波处理,并进行小波分解系数重构;最后对经过形态学滤波后的图像进行2层小波分解,通过设计出一种新型小波增强函数对不同幅值的小波系数进行不同程度的收缩处理,在此基础上进行分解系数重构.将自适应中值滤波与数学形态学滤波与本文算法进行比较,实验证明本文滤波算法其去噪效果优于前两种算法. 相似文献
7.
文章根据coiflet小波在各个尺度上的不同的带通滤波特性,并利用小波变换多分辨的特点对心电信号进行滤波。文中通过软、硬阈值折衷函数及自适应阈值策略对MIT/BIH国际标准数据库中的ECG信号进行了处理与验证。实验结果表明,该算法能较好的抑制心电信号中的各类噪声干扰。 相似文献
8.
9.
10.
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。 相似文献
11.
Vullings R de Vries B Bergmans JW 《IEEE transactions on bio-medical engineering》2011,58(4):1094-1103
The ongoing trend of ECG monitoring techniques to become more ambulatory and less obtrusive generally comes at the expense of decreased signal quality. To enhance this quality, consecutive ECG complexes can be averaged triggered on the heartbeat, exploiting the quasi-periodicity of the ECG. However, this averaging constitutes a tradeoff between improvement of the SNR and loss of clinically relevant physiological signal dynamics. Using a bayesian framework, in this paper, a sequential averaging filter is developed that, in essence, adaptively varies the number of complexes included in the averaging based on the characteristics of the ECG signal. The filter has the form of an adaptive Kalman filter. The adaptive estimation of the process and measurement noise covariances is performed by maximizing the bayesian evidence function of the sequential ECG estimation and by exploiting the spatial correlation between several simultaneously recorded ECG signals, respectively. The noise covariance estimates thus obtained render the filter capable of ascribing more weight to newly arriving data when these data contain morphological variability, and of reducing this weight in cases of no morphological variability. The filter is evaluated by applying it to a variety of ECG signals. To gauge the relevance of the adaptive noise-covariance estimation, the performance of the filter is compared to that of a Kalman filter with fixed, (a posteriori) optimized noise covariance. This comparison demonstrates that, without using a priori knowledge on signal characteristics, the filter with adaptive noise estimation performs similar to the filter with optimized fixed noise covariance, favoring the adaptive filter in cases where no a priori information is available or where signal characteristics are expected to fluctuate. 相似文献
12.
Laguna P. Jane R. Meste O. Poon P.W. Caminal P. Rix H. Thakor N.V. 《IEEE transactions on bio-medical engineering》1992,39(10):1032-1044
Many bioelectric signals result from the electrical response of physiological systems to an impulse that can be internal (ECG signals) or external (evoked potentials). In this paper an adaptive impulse correlated filter (AICF) for event-related signals that are time-locked to a stimulus is presented. This filter estimates the deterministic component of the signal and removes the noise uncorrelated with the stimulus, even if this noise is colored, as in the case of evoked potentials. The filter needs two inputs: the signal (primary input) and an impulse correlated with the deterministic component (reference input). We use the LMS algorithm to adjust the weights in the adaptive process. First, we show that the AICF is equivalent to exponentially weighted averaging (EWA) when using the LMS algorithm. A quantitative analysis of the signal-to-noise ratio improvement, convergence, and misadjustment error is presented. A comparison of the AICF with ensemble averaging (EA) and moving window averaging (MWA) techniques is also presented. The adaptive filter is applied to real high-resolution ECG signals and time-varying somatosensory evoked potentials. 相似文献
13.
基于小波变换的图像混合噪声自适应滤波算法 总被引:2,自引:0,他引:2
提出了一种基于小波变换的图像混合噪声自适应滤波算法.该算法首先采用中值滤波进行预处理以去除脉冲噪声,然后对图像进行二维小波分解得到高频和低频子图像.根据各高频子图像噪声分布特征,分别设计出新的结构元素进行形态学滤波,随后定义一种新型阂值判别函数对高频和低频子图像分别设定不同调节参数,以进一步滤除残余噪声.最后进行小波系数重构.仿真结果表明,该算法去噪效果明显优于其他几种算法,从而表明该算法是一种较为有效的图像混合噪声滤除方法. 相似文献
14.
Luo Feng Wu Shunjun Jiao Licheng ZhangLinrang 《电子科学学刊(英文版)》2002,19(1):1-7
According to the relationship of wavelet transform and perfect reconstructive FIR filter banks, this paper presents a real-time chip with adaptive Donoho‘s non-linear soft-threshold for denoising in different levels of multi-scale space through rearranging the input data during convolving, filtering and sub-sampling.And more important, it gives a simple iterative algorithm to calculate the variance of the noise in interregna with no signal.It works well whether the signal or noise is stationary or not. 相似文献
15.
基于形态滤波的心电信号基线矫正算法 总被引:6,自引:0,他引:6
基线矫正是心电(ECG)信号预处理中的一个重要步骤.本文提出了一个基于形态滤波的ECG信号基线矫正算法.首先,对原始输入ECG信号进行基于相同结构元素的形态开闭-闭开滤波,抑制其中的QRS波群;然后,采用两个不同宽度的结构元素,对去除QRS波群后的ECG信号进行广义形态开-闭滤波,分离出基线漂移信号;最后,用原始ECG信号减去估计出的基漂信号,得到经过基线矫正的ECG信号.仿真实验与实际应用结果表明,本文方法不仅可以有效去除ECG信号中的基漂干扰,而且较好地保持了ECG信号的原有特征形态,处理效果明显优于以往算法. 相似文献
16.
Neural-network-based adaptive matched filtering for QRS detection 总被引:12,自引:0,他引:12
We have developed an adaptive matched filtering algorithm based upon an artificial neural network (ANN) for QRS detection. We use an ANN adaptive whitening filter to model the lower frequencies of the ECG which are inherently nonlinear and nonstationary. The residual signal which contains mostly higher frequency QRS complex energy is then passed through a linear matched filter to detect the location of the QRS complex. We developed an algorithm to adaptively update the matched filter template from the detected QRS complex in the ECG signal itself so that the template can be customized to an individual subject. This ANN whitening filter is very effective at removing the time-varying, nonlinear noise characteristic of ECG signals. Using this novel approach, the detection rate for a very noisy patient record in the MIT/BIH arrhythmia database is 99.5%, which compares favorably to the 97.5% obtained using a linear adaptive whitening filter and the 96.5% achieved with a bandpass filtering method. 相似文献
17.
机载红外搜索跟踪系统被动定位滤波算法研究 总被引:1,自引:1,他引:0
首先用扩展卡尔曼滤波算法构建了机载红外搜索跟踪系统被动定位滤波模型.然后针对该滤波算法要求先验的噪声统计及存在系统观测模型线性化误差影响滤波精度的特点.利用虚拟噪声技术,提出了适合于红外搜索跟踪系统被动定位的自适应扩展卡尔曼滤波算法。该算法实时地估计了虚拟噪声的统计特性,减小了线性化误差,提高了非线性滤波的精度。仿真结果表明,在完全相同的初始条件下,自适应扩展卡尔曼滤波对目标距离和速度的估计结果明显优于扩展卡尔曼滤波,此算法具有很高的工程应用价值。 相似文献
18.
提升小波变换用于混沌信号降噪具有良好的效果,阈值选取与混沌信号降噪后信号的畸变具有紧密联系。为了提高混沌信号中提升小波的自适应能力,降低降噪后信号的畸变率,提出了一种基于提升小波和粒子群相结合的混沌信号降噪方法。该方法在对提升小波变换后的细节部分进行阈值处理时,采用阈值自适应选择方法,并结合粒子群算法全局搜索最优阈值。通过对Colpitts模型进行仿真分析,与标准的软阈值降噪相比,能更好地对混沌信号降噪,并且降噪后信号失真度较小,具有很好的应用价值。 相似文献
19.
El-Sayed A. El-Dahshan 《Telecommunication Systems》2011,46(3):209-215
This paper introduces an effective hybrid scheme for the denoising of electrocardiogram (ECG) signals corrupted by non-stationary
noises using genetic algorithm (GA) and wavelet transform (WT). We first applied a wavelet denoising in noise reduction of
multi-channel high resolution ECG signals. In particular, the influence of the selection of wavelet function and the choice
of decomposition level on efficiency of denoising process was considered. Selection of a suitable wavelet denoising parameters
is critical for the success of ECG signal filtration in wavelet domain. Therefore, in our noise elimination method the genetic
algorithm has been used to select the optimal wavelet denoising parameters which lead to maximize the filtration performance.
The efficiency performance of our scheme is evaluated using percentage root mean square difference (PRD) and signal to noise
ratio (SNR). The experimental results show that the introduced hybrid scheme using GA has obtain better performance than the
other reported wavelet thresholding algorithms as well as the quality of the denoising ECG signal is more suitable for the
clinical diagnosis. 相似文献
20.
心电图(ECG)是心脏疾病诊断最有效的工具。噪声的去除和Q波、R波、S波的提取是心电信号检测中的两大主题。本文使用Savitzky-Golay滤波器对人体在弯腰、走路、坐下-站起等运动状态下采集的心电信号进行分析,去除信号中的基线漂移和运动伪影,并对滤波后信号的Q波、R波和S波进行检测。通过将本文提出的滤波方式与卡尔曼滤波、小波分解就时间复杂度和功率谱密度两个参数进行对比分析,评估Savitzky-Golay滤波器在心电信号中运动伪影去除的优势。实验结果表明,Savitzky-Golay滤波器能更加有效地适应心电信号的变化,有效地去除心电信号中的噪声,并且最大限度保持心电波形的形状和波峰。 相似文献