基于平稳和连续小波变换融合算法的心电信号P,T波检测

P, T波的检测在临床上是心血管疾病诊断的重要依据。由于其波形能量低、形态复杂,极易受到噪声干扰,导致现有检测算法精度仍有待提高。该文提出平稳和连续小波变换融合算法检测P, T波,利用连续小波变换的多尺度信息,获取心电图(ECG)信号中P, T波主要成分,融合其平稳小波对P, T波候选段进行平滑处理,消除波形中锯齿状毛刺对峰值点检测的影响,最后对P, T波过零点进行时移修正,保证过零点还原到原始信号过程中能够准确对应其峰值点,从而提高P, T波检测精度。该文算法在MIT-BIH arrhythmic数据库上进行验证,最终P波的误差率、敏感度、正确预测度达到：0.23%, 99.85%, 99.90%;T波的误差率、敏感度、正确预测度达到0.27%, 99.85%, 99.87%。     

基于小波和扩展卡尔曼的心电检测

文中将小波变换和扩展卡尔曼滤波器相结合,利用小波变换多尺度多分辨的特点,将心电信号进行分解。然后对心电信号在各尺度上进行扩展卡尔曼滤波。最后在扩展卡尔曼滤波的输出结果上进行QRS波形检测。文中方法经MIT-BIH心电数据库检验,QRS波Se（探测灵敏度）在99.40%以上,同时,QRS＋P（正探测率）在99.39%以上,提高了心电信号检测的正确率。     

基于小波变换的QRS波检测算法

针对基于小渡变换的心电信号QRS波的检测算法的计算量较大,硬件不易实现的问题,提出一种FPGA的实现方案.首先分析了利用小波变换检测QRS波群的算法,给出硬件实现方案,该算法由小波变换模块和检测模块两个模块实现.然后选取高端FPGA作为硬件处理平台,给出小波变换模块及波形检测模块具体实现结构.最后在Quartus Ⅱ下进行编译和仿真,完成心电信号检测算法的硬件实现.从综合后的资源占有率上可以看出系统充分利用了FPGA内部丰富的资源,从仿真的结果看出在FPGA系统上准确的检测出了QRS波.     

大光斑LiDAR全波形数据小波变换的高斯递进分解

高斯分解是波形激光雷达数据预处理的常用方法,但在应用于大光斑全波形激光雷达数据中的叠加波时却难以发挥作用,为此提出一种基于小波变换的高斯递进波形分解方法.首先,利用小波变换多尺度分析特性检测出目标地物并准确估算组分特征参数,进而建立高斯模型优化特征参数;然后通过拟合精度指标,判断是否需要添加新组分进行逐级递进分解,确定最终模型及其组分构成,最终实现全波形激光雷达数据的波形分解.为了验证算法的有效性,分别对实验数据使用本文算法和常用的基于拐点匹配的高斯分解法进行分析,结果表明,本文算法提取的目标数几乎是拐点匹配算法的2倍,可以有效地从叠加波中检测出目标组分,且拟合精度高于98%.     

应用小波变换的频谱边缘检测技术研究

频谱感知是认知无线电实现的首要步骤。作为分析奇异点和边缘检测的强大数学工具,小波变换被用于探测和估计局部频谱的不规则变化。针对小波变换实现频谱边缘检测的局限条件,提出一种带内非平稳变化,边界连续变化的频谱边缘检测方法。通过对原始频谱信号进行数据预处理和结果峰值变化特性分析,利用小波变换实现频段的边缘划分,最后进行仿真验证。仿真结果表明:本算法能够将带内不光滑且边界连续变化的频谱有效的划分成若干独立频段,从而实现信号频谱边缘检测。     

基于小波变换的自适应QRS-T对消P波检测算法

该文提出一种基于小波变换的自适应QRS-T对消P波检测算法。首先采用二进Marr小波的Mallat算法对心电信号作多尺度分解,在每个尺度下只保留超过一定阈值的小波模极大值点,其它点置零处理。在小波分解的3,4尺度下检测QRS波群,并根据心拍节律信息和QT间期,将QRS-T波群所对应的小波模极大值点进行自适应对消,最后对包含P波的剩余信号进行非线性放大,利用小波模极大值的自适应阈值检测方法定位P波。该方法经MIT-BIH心电数据库数据验证,取得了满意的结果。     

基于TDLAS技术与小波变换去噪算法的甲烷浓度检测

为进一步提高甲烷浓度检测精度,搭建了基于TDLAS（tunable diode laser absorption spectroscopy）技术的甲烷浓度检测实验系统,利用甲烷在波长1653.72 nm处吸收强度很高且可以最大限度消除其他气体干扰的特性,通过提取二次谐波信号实现甲烷浓度检测。然后分别采用heursure硬阈值算法、heursure软阈值算法和sqtwolog固定阈值算法作为小波变换阈值算法,通过分析未去噪及小波变换去噪处理后得到的甲烷吸收信号谱图、甲烷二次谐波信号谱图、甲烷吸收信号的信噪比和均方根误差,优选sqtwolog固定阈值算法作为小波变换阈值算法。不同浓度的甲烷标气线性拟合实验及特定浓度的甲烷标气重复性实验结果表明:通过小波变换（采用sqtwolog固定阈值算法）能有效降低噪声干扰,去噪处理后提取的二次谐波信号与甲烷真实浓度拟合优度R2为0.984,拟合效果更佳。采用TDLAS技术结合小波变换去噪算法,实现甲烷浓度检测的同时也能提高甲烷浓度检测精度。     

基于小波-Radon变换的线性调频信号检测与参数估计

线性调频信号（LFM）是一类应用广泛的非平稳信号.本文选取高斯线调频小波作为基函数,研究了基于小波-Radon变换的线性调频信号检测与参数估计的基本方法,然后提出了基于小波-Radon变换的多分量LFM信号检测与参数估计的算法.计算机仿真实验结果验证了该算法的有效性。     

基于小波包变换实时检测回转电机振动信号

小波包变换不仅能检测非平稳信号的整次谐波,还能检测信号的非整次谐波,而且小波变换本身对信号的奇异点十分敏感,因此可以用来跟踪牙轮钻机振动信号。简述了小波包变换的原理,并基于Matlab实现了对牙轮钻机振动信号的处理。该处理具有信噪分离、测量牙轮钻机振动功率谱、牙轮钻机振动信号的时域-频域变化规律、牙轮钻机振动速度信号三维图、伴有噪声的原始振动波形和噪声波等测量功能。实测某矿山YZ-35C牙轮钻机的振动速度信号,实测效果优于P3562A动态信号分析仪的测量结果,结果表明该方法是可行的和有效的。     

基于小波变换和FFT的电能质量检测的研究

傅立叶变换可以精确确定出平稳波形中各次谐波的幅值;它只能检测基波和整数倍于基波的谐波,傅立叶变换算法存在着频谱泄漏和栅栏现象,从而使检测出谐波的幅值、相角和频率均存在较大的误差。小波变换可以准确确定发生突变的时刻,滤除干扰信号,但出现各次谐波频段混叠现象。故此文中采用基于小波变换和FFT相结合分析电能质量信号的方法。用小波变换检测电能质量信号的突发信号,对各次谐波混叠的信号采用FFT进行频谱分析,并进行了计算机仿真,取得了较满意的结果。     

Denoising and R-Peak Detection of Electrocardiogram Signal Based on EMD and Improved Approximate Envelope

The electrocardiogram (ECG ) signal is prone to various high and low frequency noises, including baseline wandering and power-line interference, which become the source of errors in QRS and in other extracted features. This paper presents a new ECG signal-processing approach based on empirical mode decomposition (EMD) and an improved approximate envelope method. To reduce the number of the initial intrinsic mode functions (IMFs), a Butterworth lowpass filter is used to eliminate high frequency noises before the EMD. To correct baseline wandering and to eliminate low frequency noises, the two last-order IMFs are abandoned. An improved approximate envelope is proposed and applied after the Hilbert transform to enhance the energy of QRS complexes and to suppress unwanted P/T waves and noises. Then, an algorithm based on the slope threshold is used for R-peak detection. The proposed denoising and R-peak detection algorithm are validated using the MIT-BIH Arrhythmia Database. The simulation results show that the proposed method can effectively eliminate the Gaussian noise, baseline wander, and power-line interference added to the ECG signal. The method can also function reliably even under poor signal quality and with long P and T peaks. The QRS detector has an average sensitivity of Se=99.94 % and a positive predictivity of +P=99.87 % over the first lead of the MIT-BIH Arrhythmia Database.     

Detection of ECG characteristic points using wavelet transforms

An algorithm based on wavelet transforms (WT's) has been developed for detecting ECG characteristic points. With the multiscale feature of WT's, the QRS complex can be distinguished from high P or T waves, noise, baseline drift, and artifacts. The relation between the characteristic points of ECG signal and those of modulus maximum pairs of its WT's is illustrated. By using this method, the detection rate of QRS complexes is above 99.8% for the MIT/BIH database and the P and T waves can also be detected, even with serious base line drift and noise     

Model-based fiducial points extraction for baseline wandered electrocardiograms

A fast algorithm based on the nonlinear dynamical model for the electrocardiogram (ECG) is presented for the precise extraction of the characteristic points of these signals with baseline drift. Using the adaptive bionic wavelet transform, the baseline wander is removed efficiently. In fact by the means of the bionic wavelet transform, the resolution in the time-frequency domain can be adaptively adjusted not only by the signal frequency but also by the signal instantaneous amplitude and its first-order differential, which results in a better baseline wander cancellation. At the next step the parameters of the model are chosen to have the least square error with the original ECG. Determining the precise position of the waveforms of an ECG signal with baseline wander is complicated due to the varying amplitudes of its waveforms, the ambiguous and changing form of the complex and the unknown drift. A model-based approach handles these complications, therefore a method based on this concept has been developed and the fiducial points are accurately detected using the center and spread parameters of Gaussian-functions of the model. Simulation results show that the proposed method has an average sensitivity of 99.58%, average detection accuracy of 99.64%, and specificity of 100%.     

基于小波变换的QRS波群检测

提出了一种基于小波多分辨分析的算法,对心电信号进行特征提取和识别。通过小波变换对常规心电图信号进行分解去噪和特征提取,并利用动态自适应阈值和删除多检点,补偿漏检点对QRS波检测进行优化。实验结果表明该方法在QRS波形不失真的情况下,提高了一部分MIT-BIH数据库信号中QRS波识别的准确率,并且对于较低准确率的心电信号的原因进行了分析。     

一种基于Hilbert变换的R波检测算法

基于希尔波特(Hilbert)变换性质和自适应阈值检测原理,本文提出一种新的心电信号R检测算法。该方法经MIT-BIH心电数据库数据验证,可有效降低基线漂移和高频噪声的干扰,克服高大P波和T波的影响,准确检测率在99．84%以上,算法简单,实时性好。     

自适应CA-CFAR的心电信号R波检测技术

针对心电信号R波的突变特性,利用雷达信号的检测方法,本文提出一种自适应单元平均恒虚警率(cell averaging-constant false alarm rate, CA-CFAR)的R波检测方法。首先利用滤波器组对心电信号进行预处理;然后将预处理后的信号利用自适应CA-CFAR检测判决;最后由心电信号R波的间隔特性做一个不应期剔除规则的处理,得到R波的定位。对美国麻省理工学院提供的MIT-BIH数据库中心电图(Electrocardiograph, ECG)信号仿真,实验证明,自适应参考单元的CA-CFAR对MIT-BIH的ECG信号R波检测的精准率为99.842%,检测误差为0.354%。实测数据表明了算法的有效性和适用性。      

A wavelet-based ECG delineator: evaluation on standard databases

In this paper, we developed and evaluated a robust single-lead electrocardiogram (ECG) delineation system based on the wavelet transform (WT). In a first step, QRS complexes are detected. Then, each QRS is delineated by detecting and identifying the peaks of the individual waves, as well as the complex onset and end. Finally, the determination of P and T wave peaks, onsets and ends is performed. We evaluated the algorithm on several manually annotated databases, such as MIT-BIH Arrhythmia, QT, European ST-T and CSE databases, developed for validation purposes. The QRS detector obtained a sensitivity of Se = 99.66% and a positive predictivity of P+ = 99.56% over the first lead of the validation databases (more than 980,000 beats), while for the well-known MIT-BIH Arrhythmia Database, Se and P+ over 99.8% were attained. As for the delineation of the ECG waves, the mean and standard deviation of the differences between the automatic and manual annotations were computed. The mean error obtained with the WT approach was found not to exceed one sampling interval, while the standard deviations were around the accepted tolerances between expert physicians, outperforming the results of other well known algorithms, especially in determining the end of T wave.