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。     

利用三次样条差值法抑制脉搏波基线漂移

目的脉搏波检测过程中,必须克服由于呼吸运动和身体移位会导致脉搏信号的基线漂移。本文提出一种基于三次样条差值的脉搏波基线漂移抑制方法。方法选择脉搏波的各个起始点为给定点,根据各个给定点的数值利用三次样条插值拟合出基线。然后用脉搏波在各个采样时间点上的采样值减去对应时间点上的基线函数数值,得到去基线的脉搏波波形。结果通过实际测量实验表明,脉搏波去基线后,基线比较平稳,波形比较稳定。结论三次样条差值法能有效抑制脉搏波的基线漂移,并且能够很好地应用在脉搏波实时检测中。     

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。     

基于平滑先验法去除脉搏波基线漂移

脉搏波信号中含有丰富的人体生理病理信息,然而基于光电容积描记法采集到的脉搏波基线漂移非常严重,直接影响到人体生理参数的准确提取。针对目前生物信号处理领域中去除基线漂移所用的方法计算复杂,处理信号时间过长,无法满足对脉搏波进行实时处理等问题,首次提出基于正则化最小二乘法的平滑先验法去除脉搏波的基线漂移。该方法通过分析不同正则化参数下平滑先验法的截止频率,结合脉搏波信号中基线漂移信号的频率范围,选取合适的正则化参数,实现脉搏波基线漂移的去除。实验结果表明,与小波变换法、经验模态分解法相比,该方法去除脉搏波基线漂移效果明显,提高了计算速度,同时也提高了信噪比,有利于下一步对脉搏波特征点的精确提取。     

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。     

基于小波变换的心电信号基线矫正方法

本文介绍一种基于小波变换的心电信号基线漂移去除方法。该方法利用小波变换多分辨分析的特性,将含噪声及基线漂移心电信号进行多尺度分解,结果表明,某尺度下的分解信号较好地反映了心电信号基线漂移,在重构过程中可直接将其去除。     

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.     

一种新的基于三次样条插值技术的心电图基线漂移消除方法

现有的基于三次样条插值技术的基线消除方法都是从PR段上提取“基准点”，这种提取“基准点”方法如果应用于PR段不平直或噪声较大的信号时，其准确性要下降。本文利用QRS波段的高频特性，先用一个FIR基线滤波器对ECG信号进行粗略滤波，求出R波波峰的位置和其幅度在滤波前后的变化值，经过实验论证，所求得的位置和变化值可分别被看作是一个“基准点”的位置和幅度。然后，对在周期上找到的所有的“基准点”进行三次样条插值计算，得到的拟合曲线就是基线。文章把该方法和文献^[3]所述方法进行了比较，结果表明该方法具有更好的性能。     

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.     

小波包变换在消除心电图基线漂移方面的研究

本文提出一种利用小波包变换逼近信号消除心电图ECG基线漂移噪声的方法。该方法的基本思想是：通过对ECG信号进行多分辨率分析，利用所得到的一段或几段逼近信号充分逼近ECG信号中的基线漂移噪声的特性。从而消除某线漂移分景。通过实际记录的验证，该方法在不损害信号的其他成分下具有良好的效果。     

基于EMD的自适应ECG信号基线漂移消除方法研究

对比目前使用EMD或改进EMD方法进行的心电（ECG）信号基线漂移去除算法的实现。本文在详细考察EMD方法过程的基础上,提出一种与EMD物理意义高度契合的完全自适应的基线漂移算法,通过计算ECG平均心率周期,与EMD分解产生的IMF分量的“周期”进行对比,分离出不属于ECG信号的低频IMF分量,然后重构其余IMF分量得到去除基线漂移的ECG信号。使用美国麻省理工学院提供的MIT-BIH心率失常数据库中的原始ECG对本文提出的基线漂移去除方法进行定性分析。使用ECGSYN（实际ECG波形发生器）产生模拟干净的ECG信号,加入已知的低频信号作为基线漂移噪声,对本文提出的基线漂移去除方法进行定量分析。     

An expert electrocardiogram quality evaluation algorithm based on signal mobility factors

Abstract

The major concentration of this study is to describe and to develop a new electrocardiogram (ECG) signal measurement binary quality assessment (accept–reject) technique. The proposed algorithm is composed of three major stages: pre-processing, signal mobility-based quality measurement and advanced post-evaluation. The pre-processing step includes baseline wander and high-frequency disturbances removal. The signal mobility-based quality measurement routine includes two separate stages based on energy and concavity of the ECG signal. The post-evaluation quality measurement step is mainly based on the six features inferenced from heuristic experiences and human thinking models. The proposed technique was applied to the test dataset provided by the PhysioNet Computing in Cardiology (CinC) challenge 2011 and accuracy 93.40% was achieved which shows the marginal improvement in this field.     

Empirical mode decomposition based ECG enhancement and QRS detection

In this paper an Empirical Mode Decomposition (EMD) based ECG signal enhancement and QRS detection algorithm is proposed. Being a non-invasive measurement, ECG is prone to various high and low frequency noises causing baseline wander and power line interference, which act as a source of error in QRS and other feature extraction. EMD is a fully adaptive signal decomposition technique that generates Intrinsic Mode Functions (IMF) as decomposition output. Here, first baseline wander is corrected by selective reconstruction based slope minimization technique from IMFs and then high frequency noise is removed by eliminating a noisy set of lower order IMFs with a statistical peak correction as high frequency noise elimination is accompanied by peak deformation of sharp characteristic waves. Then a set of IMFs are selected that represents QRS region and a nonlinear transformation is done for QRS enhancement. This improves detection accuracy, which is represented in the result section. Thus in this method a single fold processing of each signal is required unlike other conventional techniques.     

Baseline wander correction in pulse waveforms using wavelet-based cascaded adaptive filter

Pulse diagnosis is a convenient, inexpensive, painless, and non-invasive diagnosis method. Quantifying pulse diagnosis is to acquire and record pulse waveforms by a set of sensor firstly, and then analyze these pulse waveforms. However, respiration and artifact motion during pulse waveform acquisition can introduce baseline wander. It is necessary, therefore, to remove the pulse waveform's baseline wander in order to perform accurate pulse waveform analysis. This paper presents a wavelet-based cascaded adaptive filter (CAF) to remove the baseline wander of pulse waveform. To evaluate the level of baseline wander, we introduce a criterion: energy ratio (ER) of pulse waveform to its baseline wander. If the ER is more than a given threshold, the baseline wander can be removed only by cubic spline estimation; otherwise it must be filtered by, in sequence, discrete Meyer wavelet filter and the cubic spline estimation. Compared with traditional methods such as cubic spline estimation, morphology filter and Linear-phase finite impulse response (FIR) least-squares-error digital filter, the experimental results on 50 simulated and 500 real pulse signals demonstrate the power of CAF filter both in removing baseline wander and in preserving the diagnostic information of pulse waveforms. This CAF filter also can be used to remove the baseline wander of other physiological signals, such as ECG and so on.     

Fractal and EMD based removal of baseline wander and powerline interference from ECG signals

This paper presents novel methods for baseline wander removal and powerline interference removal from electrocardiogram (ECG) signals. Baseline wander and clean ECG have been modeled as 1st and 2nd-order fractional Brownian motion (fBm) processes, respectively. This fractal modeling is utilized to propose projection operator based approach for baseline wander removal. Powerline interference is removed by using a hybrid approach of empirical mode decomposition method (EMD) and wavelet analysis. Simulation results are presented to show the efficacy of both the methods. The proposed methods have been shown to preserve ECG shapes characteristic of heart abnormalities.     

Computerized quality assessment of phonocardiogram signal measurement-acquisition parameters

The major focus of this study is to describe and develop a phonocardiogram (PCG) signal measurement binary quality assessment (accept-reject) technique. The proposed algorithm is composed of three major stages: preprocessing, numerical-based quality measurement and advanced measurement subroutines. The preprocessing step includes normalization, wavelet-based threshold denoising and baseline wander removal. The numerical-based quality measurement routine includes two separate stages based on energy and level of noise of the PCG signal. The advanced quality measurement step is mainly based on the interval of S1 and S2 sounds. The proposed technique was applied to 400 2-min PCG signals gathered by volunteers with range of skills in PCG data acquisition from patients with different types of valve diseases from their 2R (aortic), 2L (pulmonic), 4R (apex) and 4L (tricuspid) positions by implementing an electronic stethoscope (3M Littmann(?) 3200, 4 kHz sampling frequency). The dataset was firstly annotated manually and then, by applying the proposed algorithm, an accuracy of 95.25% was achieved.     

Optimizing bioimpedance measurement configuration for dual-gated nuclear medicine imaging: a sensitivity study

Motion artefacts due to respiration and cardiac contractions may deteriorate the quality of nuclear medicine imaging leading to incorrect diagnosis and inadequate treatment. Motion artefacts can be minimized by simultaneous respiratory and cardiac gating, dual-gating. Currently, only cardiac gating is often performed. In this study, an optimized bioimpedance measurement configuration was determined for simultaneous respiratory and cardiac gating signal acquisition. The optimized configuration was located on anterolateral upper thorax based on sensitivity simulations utilizing a simplified thorax model. The validity of the optimized configuration was studied with six healthy volunteers. In the peak-to-peak and frequency content analyses the optimized configuration showed consistently higher peak-to-peak values and frequency content than other studied measurement configurations. This study indicates that the bioimpedance method has potential for the dual-gating in nuclear medicine imaging. The method would minimize the need of additional equipment, is easy for the technologists to use and comfortable for the patients.     

Computerized quality assessment of phonocardiogram signal measurement-acquisition parameters

The major focus of this study is to describe and develop a phonocardiogram (PCG) signal measurement binary quality assessment (accept–reject) technique. The proposed algorithm is composed of three major stages: preprocessing, numerical-based quality measurement and advanced measurement subroutines. The preprocessing step includes normalization, wavelet-based threshold denoising and baseline wander removal. The numerical-based quality measurement routine includes two separate stages based on energy and level of noise of the PCG signal. The advanced quality measurement step is mainly based on the interval of S1 and S2 sounds. The proposed technique was applied to 400 2-min PCG signals gathered by volunteers with range of skills in PCG data acquisition from patients with different types of valve diseases from their 2R (aortic), 2L (pulmonic), 4R (apex) and 4L (tricuspid) positions by implementing an electronic stethoscope (3M Littmann^® 3200, 4 kHz sampling frequency). The dataset was firstly annotated manually and then, by applying the proposed algorithm, an accuracy of 95.25% was achieved.