一种脉搏波和心电信号时域基线漂移消除方法

目的:脉搏波和心电信号都是临床诊断的重要依据,但是基线漂移的存在影响了诊断的准确性,因此信号处理中首先要对信号去除基线漂移。方法:检测脉搏波和心电信号的时域特征点,将检测得到的特征点作为插值节点做插值得到时域的基线信号,原信号减去基线信号即得去除基线漂移的信号。为比较不同算法的差异,实验分别采用高斯滤波、中值滤波、形态学滤波和本文算法(插值拟合法)对同一信号进行基线漂移消除实验。结果:实验表明高斯滤波的结果最差,中值滤波和形态学滤波都能有效地去除基线漂移,但是失真却比较严重,而插值拟合法不仅能有效的去除基线漂移,同时也最大程度地保留了原信号的成分。结论:插值拟合法与其他3种方法相比,具有更好的鲁棒性和适应性,对于消除基线漂移有着良好的效果。     

自适应算法与小波变换在心电信号滤波中的应用

心电信号是一种基本的人体生理信号,具有重要的临床诊断价值。然而,体表检测人体心电信号中常带有工频干扰、基线漂移、肌电干扰等各种噪声,给临床对心血管疾病的诊断带来了障碍。为了消除心电信号检测过程中带有的上述三种噪声,采用LM S自适应算法及小波变换理论,有针对性的设计了自适应滤波器、小波变换滤波器和自适应信号分离器等三种数字滤波器来滤除相应干扰。结果表明,对心电信号中存在的这三种噪声具有很好的滤波效果。     

改进的三次样条插值心电基线漂移滤波法

基准点选择对三次样条插值法去噪的效果有重要影响。本文针对通常的三次样条插值滤波方法,提出一种改进的心电(ECG)信号滤波算法,能适应更宽范围的基线噪声频率分布。算法通过对原始ECG信号求一阶导数,得到每一个心拍周期内的最大和最小值点,其对应的位置作为基准点的位置。然后对原始ECG信号通过截止频率为1.5Hz的高通滤波器,将滤波前后基准点所对应信号幅值的差值作为基准点的幅度。对这些基准点进行三次样条插值曲线拟合,所得拟合曲线为基线漂移曲线。改进算法与原单点法相比,在模拟两种基线漂移情况下,改进样条差值的拟合基线漂移曲线对模拟基线漂移的相关系数分别提高了0.242和0.13;真实基线漂移的情况下,多个临床数据实验显示改进样条差值法平均相关系数达到0.972。     

基于小波神经网络的心电信号滤波研究

针对心电(ECG)信号检测中存在的主要噪声,本文研究了基于小波神经网络(WNN)的ECG信号滤波理论。提出一种通过WNN非线性逼近能力构建的针对ECG信号的非线性滤波器算法和滤波策略,实现对ECG信号中基线漂移、肌电干扰、工频干扰噪声的滤除;给出了网络训练算法和滤波实验,滤波后信号与期望信号误差范围在微伏级,验证了本文提出的基于WNN的心电非线性滤波器对心电主要噪声快速滤波的良好效果,最后讨论了影响WNN用于心电滤波的几个关键问题。     

一种同时消除脉搏波信号中呼吸基线漂移和高频噪声的方法

目的：基于光电容积脉搏波可以实现血氧饱和度等人体生理参数的无创检测。基于光电容积脉搏波测量时,由于信号采集过程中存在人体呼吸和仪器本身热噪声等干扰,脉搏波信号中存在着呼吸基线漂移和高频噪声,影响最终的人体生理参数测量精度。方法：因此提出一种在经验模式分解的过程中结合小波变换的方法,来同时消除呼吸基线漂移和高频噪声的影响。首先通过经验模态分解将脉搏波信号分解为若干内在模式分量,并分别判断出含有呼吸基线漂移和代表高频噪声的分量,对于代表高频噪声的分量采用类似小波变换的方法进行滤波,利用小波变换将含有呼吸基线漂移的分量分解,将代表呼吸基线漂移的小波细节置零,信号重构后就达到了同时消除呼吸基线和高频噪声的目的。利用自行研制的测量装置采集的脉搏波信号进行实验验证,并采用信号交直流比R和信号的频谱进行效果评价。结果：有效地同时消除了呼吸基线漂移和高频噪声。结论：该方法将有利于血氧饱和度等人体生理参数无创检测精度的提高。     

基于三次样条插值技术的去除胸阻抗基线漂移的方法研究

目的:胸阻抗信号常伴随呼吸等干扰造成基线漂移,这给临床分析计算、测量带来不良影响,为了消除胸阻抗基线漂移,作者在分析现有去除方法优劣的基础上提出一种新的方法。方法:采用labVIEW编程语言,以阻抗波波谷为插值基准点,利用三次样条差值技术去除胸阻抗基线漂移。结果:利用该方法消除基线后的胸阻抗波形底部非常整齐,而且没有使原有信号变异或者失真。结论:三次样条插值技术去除胸阻抗波基线漂移方法在仿真和实测实验中都取得很好的效果,这种方法是行之有效的。     

基于小波变换的自适应滤波器消除脉搏波基线漂移

脉诊仪的研制中,由于呼吸运动和身体移位导致脉搏信号的基线漂移是必须克服的.本文提出了一种新的基于Meyer小波变换的自适应滤波器,其参考输入选择原始信号经小波分解后的高频分量.通过仿真和实际测量实验表明,该方法能有效消除脉搏波的基线漂移.     

基于扩散模型的心电信号基线漂移去除法

ECG信号的基线漂移直接影响波形的幅值及时程测量精度,快速、有效的基线漂移去除法是ECG信号处理的重点研究内容.提出一种基于扩散模型的ECG基线漂移信号去除法,通过对有基线漂移的ECG信号进行一系列的线性扩散,使这些信号接近于漂移的基线,从而分离出ECG信号,较好地保留了ECG主要波形的形状及其时程关系.利用合成信号的仿真结果显示,扩散模型可较好地将低频成分分离出来,且合成波的频率相差越大,其分离效果越好,显示其在ECG信号基线漂移去除中良好的应用前景.     

自适应滤波器在ECG分析中的应用:噪声消除和心律不齐检测

本文提供了几种用来消除噪声和检测心律不齐的自适应滤波器结构。这类滤波器本质上是减小主输入和参考输入间的均方误差。主输入为带有噪声的ECG;参考输入或者是以某种方式与主输入中噪声相关的噪声或者是只与主输入中ECG相关的信号。不同的滤波器结构用来消除不同形式的噪声:基线漂移、60Hz电源干扰、肌电噪声和运动伪迹。作者还提出同种用于获得正常QRS波群冲激响应的重现式自适应滤波器(recurrent adaptive filterr)。     

滤除小循环阻抗容积波中的呼吸干扰

目的消除小循环阻抗容积波中呼吸干扰.方法分析各种状态的小循环阻抗容积波信号的频谱.利用椭圆函数滤波器较陡的过渡带特性设计前向和后向滤波器,消除由于IIR滤波器造成滤波后的数据相位非线性失真.结果滤波后的小循环阻抗信号与参照信号心电和心音之间时相同步,确保了结合参照信号的综合判断的正确性.结论解决了无须依赖参照信号对小循环阻抗信号中的呼吸干扰的消除问题.     

Medical feature based evaluation of structuring elements for morphological enhancement of ultrasonic images

This paper investigates the use of morphology-based nonlinear filters, and performs deterministic and statistical analysis of the linear combinations of the filters for the image quality enhancement of B-mode ultrasound images. The fact that the structuring element shape greatly influences the output of the filter, is one of the most important features of mathematical morphology. The present reported work comparatively evaluates the structuring elements for morphological liver image enhancement and verifies the hypothesis that the speckles visible in US images are short, slightly ‘banana-shaped’ white lines. Initially, five different liver images were morphologically filtered using 10 different structuring elements and then the filtered images were assessed quantitatively. Image quality parameters such as peak signal-to-noise ratio, mean square error and correlation coefficient have been used to evaluate the performance of the morphological filters with different structuring elements. To endorse the observation of the quantitative analysis, the filtered images were then evaluated qualitatively, based on the image features looked into by the medical fraternity. The evaluation parameters have been taken on the basis of the suggestions made by a group of radiologists. The results of the processed images were then evaluated by a different group of radiologists. A multi-point rank order method has been used to identify small differences or trends in observation. The subjective analysis by radiologists indicates that morphological filter using line shaped structuring element with length 2 performs better than the other structuring elements. These observations were found to be in line with the observations of quantitative analysis.     

Noise and baseline wandering suppression of ECG signals by morphological filter

Electrocardiogram (ECG) signals describe the electrical activity of the heart, and are universally by physicists in the diagnosis of cardiac pathologies. However, during the acquisition of ECGs they are often contaminated with different sources of noise, making interpretation difficult. Different techniques have been used to filter the ECG signal, in order to optimize the signal to noise ratio (S/N). In this paper, an approach based on morphological filtering is developed in order to filter the ECG. Morphological filtering is concerned with the detection of the ECG morphology, therefore allowing the suppression of noises and particularly baseline wandering. The implemented filter is evaluated using signals taken from the MIT-BIH ECG universal database. The results show that the performance of this filter is good compared with other filtering techniques.     

Baseline drift removal and denoising of MCG data using EEMD: Role of noise amplitude and the thresholding effect

We adopt the Ensemble Empirical Mode Decomposition (EEMD) method, with an appropriate thresholding on the Intrinsic Mode Functions (IMFs), to denoise the magnetocardiography (MCG) signal. To this end, we discuss the two associated problems that relate to: (i) the amplitude of noise added to the observed signal in the EEMD method with a view to prevent mode mixing and (ii) the effect of direct thresholding that causes discontinuities in the reconstructed denoised signal. We then denoise the MCG signals, having various signal-to-noise ratios, by using this method and compare the results with those obtained by the standard wavelet based denoising method. We also address the problem of eliminating the high frequency baseline drift such as the sudden and discontinuous changes in the baseline of the experimentally measured MCG signal using the EEMD based method. We show that the EEMD method used for denoising and the elimination of baseline drift is superior in performance to other standard methods such as wavelet based techniques and Independent Component Analysis (ICA).     

Application of the spirometer in respiratory gated radiotherapy

The signal from a spirometer is directly correlated with respiratory motion and is ideal for target respiratory motion tracking. However, its susceptibility to signal drift deters its application in radiotherapy. In this work, a few approaches are investigated to control spirometer signal drift for a Bernoulli-type spirometer. A method is presented for rapid daily calibration of the spirometer to obtain a flow sensitivity function. Daily calibration assures accurate airflow measurement and also reduces signal drift. Dynamic baseline adjustment further controls the signal drift. The accuracy of these techniques was studied and it was found that the spirometer is able to provide a long-term drift-free breathing signal. The tracking error is comprised of two components: calibration error and stochastic signal baseline variation error. The calibration error is very small (1% of 3 l) and therefore negligible. The stochastic baseline variation error can be as large as 20% of the normal breathing amplitude. In view of these uncertainties, the applications of spirometers in treatment techniques that rely on breathing monitoring are discussed. Spirometer-based monitoring is noted most suitable for deep inspiration breath-hold but less important for free breathing gating techniques.     

An automated technique for analysis of current transitions in multilevel single-channel recordings

Detailed kinetic studies of ion channel gating are best carried out using the patch-clamp technique which permits the measurement of the ionic current through individual channels. Typical patch-clamp recordings show the current signal, in the form of a sequence of rectangular pulses (analogous to a random telegraph signal), riding on slow baseline drift, partially obscured by high-frequency noise and distorted by filtering. In order to analyze such recordings, we have developed a set of interactive Pascal programs based on a feature-detection algorithm capable of identifying current transitions in multiple-channel recordings in the presence of substantial levels of noise and drift. Software operation is largely automated but includes provisions for examination and correction of the output. The software was optimized and systematically evaluated using simulated data with variable amounts of noise and drift. Results indicate that satisfactory performance is obtained for signal-to-noise ratio as low as four even with uncommonly large baseline drift. Steady-state processing speeds varied from 1000 to 4000 samples per second depending on data complexity.     

An Adaptive Method for Correction of the ECG Signal Baseline Drift Using Multiresolution Wavelet Transforms

Biomedical Engineering - Different approaches to correction of the ECG signal baseline drift are compared. A new adaptive method for correction of the baseline drift is proposed. The method is...     

基于三级滤波器的表面肌电信号降噪处理

表面肌电信号（surface electromyography,sEMG）是一种非平稳微弱信号,而它的低信噪比是造成对其进行分解十分困难的主要原因之一.本文针对sEMG信号的噪声特点,提出基于经验模态分解（empirical mode decomposition,EMD）的三级滤波器技术来对sEMG信号进行预处理,即采用频谱插值法去除工频干扰,采用形态学运算去除基线漂移,采用经验模态分解去除白噪声.实验结果表明,本文所提出的方法不仅能够提高sEMG信号的信噪比,也能有效地保留运动单位动作电位（motor unit action potential,MUAP）的波形信息,这将有利于对MUAP的识别从而提高对sEMG信号的分解准确率.     

基于指尖视频图像的自适应脉搏信号提取算法研究

针对手指视频图像R分量饱和失真现象,本文提出了一种基于迭代的阈值分割算法,自适应生成R分量待检测区域,通过计算待检测区域灰度均值,从而提取出人体脉搏信号。原始脉搏信号存在基线漂移及高频噪声,结合脉搏信号特征,设计了零相位数字滤波器来滤除噪声干扰。在不同智能手机上采集了指尖视频图像,利用本文提出的算法提取出了待检测区域。考虑到每次测量时指尖压力会有所不同,本文对不同压力下提取的脉搏信号做了对比分析。为了验证本文提出的算法在心率检测方面的准确性,做了心率检测对比实验。结果表明,本文提出的算法能准确提取出人体心率信息,同时具备一定的可移植性,为进一步在智能手机平台上开发生理监测应用提供了一定的理论帮助。     

一种基于脉冲耦合神经网络的脉冲噪声滤波器设计

根据脉冲噪声与其邻域中图像灰度之间的明显差异性 ,本文提出了一种新的神经网络脉冲噪声滤波器设计方案。这种脉冲耦合神经网络PCNN滤波器比现有的PCNN逐次降噪方案迭代运算次数少 ,执行速度快。并与中值滤波器、全方位结构元约束层叠滤波器、全方位结构元形态闭 开最小、开 闭最大滤波器等现有的非线性滤波器进行实验比较 ,证明 ,该方案有更好的降噪性能 ,更重要的是比这些方案更有效地保持了图像的高频细节信息。