小波变换去除心电信号中呼吸信号干扰

目的 研究用小波变换去除心电图信号中呼吸信号的方法。方法 采用db4小波对采样频率为200Hz的心电图信号作离散小波变换的多层分解，并与呼吸信号的频率成分比较，发现呼吸信号分布在心电图信号分解后第8、9、10层细节中，去除这些成分和高频干扰，对剩下的分量重构。结果 比较成功地纠正了心电信号的基线，去除了低频呼吸信号的干扰。结论 小波变换的方法能够去除心电信号中的呼吸信号干扰。     

小波变换用于从血压信号中提取呼吸及心率信息的研究

本文提供了一种新的数字信号处理工具一小波变换和小波分析程序流程图,可用于重量及其它学科科研实验研究。文中给出了利用Gauss一介导数小波变换从家兔动脉血压信号中提取呼吸频率及心率的方法。     

利用ECG信号检测睡眠呼吸暂停的小波包分析方法

文章根据睡眠呼吸暂停与心率变化的关系。阐述了从心电图中检测睡眠呼吸暂停的方法。该方法首先通过对心率信号进行小波包变换，提取特征向量，然后根据特征向量的变化检测出睡眠呼吸暂停的位置和分布。结果表明，该方法物理意义明确，诊断结果精度高，为睡眠呼吸暂停综合症的早期诊断、监护及预后评估提供了新的分析工具。     

利用小波包提取脉象信号特征的方法

将基于小波包分析构造特征向量的方法应用到脉象信号分析中,阐述了构造方法,给出了实验数据.构造的特征向量具有较好的重复性与稳定性,可以较有效地区分正常人与心脏病人.     

基于树状小波的超声医学图像阈值函数降噪

针对超声医学图像中存在特有的斑点噪声,利用树状小波分解比传统小波分解精度高的特点,将超声医学图像进行树状小波分解,然后分别采用硬阈值、软阈值和半软阈值函数三种方法进行降噪处理.结果表明半软阈值函数方法是较优阈值函数方法,可以有效地降低原图像的斑点噪声并保留图像细节.     

ECG信号的小波变换检测方法

本文反小波变换应用于ＥＣＧ信号的ＱＲＳ波检测。利用二进样条小波对信号按Ｍａｌｌａｔ算法进行变换：从二进小波变换的等效滤波器的角度，分析了信号奇异点（Ｒ峰点）与其小波变换模极大值对的零交叉点的关系。在检测中运用了一系列策略以增强算法的抗干扰能力、提高ＱＲＳ波的正确检测率。经ＭＩＴ／ＢＩＨ标准心电数据库检测验证，ＱＲＳ波正确检测率高达９９．８％。     

一种呼吸信号的检测方法

探讨呼吸波形的检测方法,针对现有方法的不足,提出了从肌电提取呼吸信号的检测方法—肌电频谱法。该方法首先利用FastICA算法消除胸部肌电信号中的心电信号成分,再对处理后的信号进行短时傅立叶变换,提取平均平移曲线得到呼吸波形。通过仿真及对实际信号的处理,表明该方法简单有效,可以准确地从肌电信号中提取呼吸信号。     

基于混合小波变换的瞬态信号检测方法

探讨了信号的小波变换与匹配滤波的关系,指出小波变换（WT）实际上就是可变检测模板的匹配滤液过程。根据这一思想,提出了基于混合小波的信号检测方法。本文中,“混合小波变换”是指在小波分解和重构中分别使用不同的基本小波。其中分解小波用于实现可变模板的信号检测,重构小波则用以增强被检测信号的特征。我们用该方法对实测脑电信号（EEG）中瞬态脉冲干扰进行检测。实验结果表明该方法能有效地检测出EEG中的瞬态脉冲。     

由单通道心电提取呼吸信息的算法

基于除呼吸作用以外，能够引起正常的QRS波群中R波振幅的干扰因素也较少的事实，提出使用室上性的QRS波群由心电提取呼吸信息（EDR）（ECG-derived respiratory signal）的算法。该算法先将单通道心电进行小波分解，再将小波近似在适当尺度上的室上性波R波振幅在时间上进行延拖，通过一低通滤波器后再将抽样频率降低至5Hz，再通过一带通滤波器（0．1～0．4Hz）后，即可得到EDR。     

基于小波变换的中医推拿扌衮法作用力信号能量分析

目的 分析中医推拿法作用力各频率段的能量分布,揭示扌衮法的动力学特点。方法 测量6位推拿专家及6位初学者的扌衮法推拿作用力信号,利用小波变换进行频带划分,计算各频率段的能量并进行统计分析。对垂直作用力和水平作用力构造18个特征量,提出了总体评价系数R。结果 专家的扌衮法推拿作用力信号在0~0.406 25 Hz的低频段集中了近70%的能量,另有约20%的能量集中在1.625~3.25 Hz频率段。6位推拿专家总体评价系数R均大于0.70,6位初学者的总体评价系数R均小于0.70,两者具有较显著的差异。结论 扌衮法作用力各频率段的能量分布体现了法“柔和”性及周期性特点。当测试者扌衮法推拿技术动作规范且总体评价系数R大于0.70时,可认为已较好地掌握了扌衮法推拿技术。     

小波熵在脑电信号分析中的应用

小波熵是一个衡量非线性信号多尺度动力学行为有序、无序程度的量化指标,其可提供信号非线性动力学过程复杂程度的信息.近年来,小波熵在脑电信号中的研究日益受到关注,国内外学者用小波熵研究脑电信号、诱发电位、事件相关电位等的复杂程度,进一步揭示了大脑电活动的动力学机制.其主要应用于大脑感知、认知活动的研究,癫痫脑电信号的动态观测,睡眠、网络成瘾、头外伤后脑神经的康复等几个方面.小波熵不仅可以显示受到刺激后脑电信号频率上同步化的动态演变过程,而且可以有效区分癫痫发作前状态和癫痫发作状态,从而加深了对脑动力学机制的理解,成为认知功能研究的一种新的方法,显示了在脑电信号分析中良好的应用前景.     

基于子波变换的心阻抗血流图的特征提取

本语文介绍了一种利用子波变换对心阻抗血流图的特征点进行检测的方法，即主要是通过把信号在多个尺度上分解，在感兴趣的尺度上找到模极值点和特征点的对应关系从而完成对特征点的自动检测，同时本文对于波时／频窗的自适应性进行了直观的说明，并对子波变换的波波器组解释和算法实现给予讨论。经过临床实验表明，该方法准确性高，有一定实用价值。     

Nonlinear analysis of wheezes using wavelet bicoherence

Wheezes, as being abnormal breath sounds, are observed in patients with obstructive pulmonary diseases, such as asthma. The aim of this study was to capture and analyze the nonlinear characteristics of asthmatic wheezes, reflected in the quadrature phase coupling of their harmonics, as they evolve over time within the breathing cycle. To achieve this, the continuous wavelet transform (CWT) was combined with third-order statistics/spectra. Wheezes from patients with diagnosed asthma were drawn from a lung sound database and analyzed in the time-bi-frequency domain. The analysis results justified the efficient performance of this combinatory approach to reveal and quantify the evolution of the nonlinearities of wheezes with time.     

Blood pressure waveform analysis by means of wavelet transform

The assessment of cardiovascular function by means of arterial pulse wave analysis (PWA) is well established in clinical practice. PWA is applied to study risk stratification in hypertension, with emphasis on the measurement of the augmentation index as a measure of aortic pressure wave reflections. Despite the fact that the prognostic power of PWA, in its current form, still remains to be demonstrated in the general population, there is general agreement that analysis and interpretation of the waveform might provide a deeper insight in cardiovascular pathophysiology. We propose here the use of wavelet analysis (WA) as a tool to quantify arterial pressure waveform features, with a twofold aim. First, we discuss a specific use of wavelet transform in the study of pressure waveform morphology, and its potential role in ascertaining the dynamics of temporal properties of arterial pressure waveforms. Second, we apply WA to evaluate a database of carotid artery pressure waveforms of healthy middle-aged women and men. Wavelet analysis has the potential to extract specific features (wavelet details), related to wave reflection and aortic valve closure, from a measured waveform. Analysis showed that the fifth detail, one of the waveform features extracted applying the wavelet decomposition, appeared to be the most appropriate for the analysis of carotid artery pressure waveforms. What remains to be assessed is how the information embedded in this detail can be further processed and transformed into quantitative data, and how it can be rendered useful for automated waveform classification and arterial function parameters with potential clinical applications.     

ECG signal processing using multiresolution analysis

In this paper, multiresolution analysis using wavelets is discussed and evaluated in ECG signal processing. The approach we developed for processing the ECG signals uses two steps. In the first step, we implement an algorithm based on multiresolution analysis using discrete wavelet transform for denoising the ECG signals. The results we obtained on MIT-BIH ECG signals show good performance in denoising ECG signals. In the second step, multiresolution analysis is applied for QRS complex detection. It is shown that with such analysis, the QRS complex can be distinguished from high P or T waves, baseline drift and artefacts. The results we obtained on ECG signals from the MIT-BIH database show a detection rate of QRS complexes above 99.8% (sensitivity = 99.88% and predictivity = 99.89%), and a total detection failure of 0.24%.     

小波分析在医学中的应用

小波分析方法是一种新的信号分析方法 ,由于其具有多分辨分析的特点 ,能较好地突出信号局部特征 ,在微弱、背景噪声较强的随机信号分析中具有重要的意义。本文综述了这一新方法在诸如 EEG、EP、ECG、医学图像等生物医学信号中的广泛应用 ,并对各自效果进行了分析。最后 ,对小波变换在生物医学信号处理中的发展作了展望。     

Pneumothorax detection using computerised analysis of breath sounds

The primary objective of the study was to investigate the effects of pneumothorax (PTX) on breath sounds and to evaluate their use for PTX diagnosis. The underlying hypothesis is that there are diagnostic breath sound changes with PTX. An animal model was created in which breath sounds of eight mongrel dogs were acquired and analysed for both normal and PTX states. The results suggested that pneumothorax was associated with a reduction in sound amplitude, a preferential decrease in high-frequency acoustic components and a reduction in sound amplitude variation during the respiration cycle (p<0.01 for each, using the Wilcoxson signed-rank test). Although the use of diminished sound amplitude for PTX diagnosis assumes availability of baseline measurements, this appears unnecessary for high-frequency reduction or sound amplitude changes over the respiratory cycle. Further studies are warranted to test the clinical feasibility of the method in humans.     

Selected ion flow tube: a technique for quantitative trace gas analysis of air and breath

The selected ion flow tube (SIFT) technique for trace gas analysis of air and breath is based on soft chemical ionisation of the trace gases to the exclusion of the major air and breath gases, in fast-flowing inert carrier gas, exploiting the ion-molecule reactions that occur between the trace gases and the pre selected precursor ions (H₃O⁺, NO⁺ and O ₂ ⁺ ). The physics and ion chemistry involved in the SIFT technique are described, as are the kinetics of the ion-molecule reactions that are exploited to quantitatively analyse the trace gases. Fast on-line data-acquisition hardware and software have been developed to analyse the mass spectra obtained, from which partial pressures of the trace gases down to about 10 parts per billion can be measured. The time response of the instrument is 20 ms, allowing the profiles of the trace gas concentrations on breath to be obtained during a normal breathing cycle. Pilot results obtained with this SIFT technique include detection and quantification of the most abundant breath trace gases, analysis of cigarette smoke, detection of gases present on smokers' breath and accurate measurement of the partial pressures of NH₃, NO and NO₂ in air. The simultaneous analysis of several breath trace gases during a single exhalation is clearly demonstrated, and thus different elution times for isoprene and methanol along the respiratory tract are observed. This technique has great potential in many clinical and biological disciplines, and in health and safety monitoring.     

Principal component analysis of chest wall movement in selected pathologies

A method is presented for assessing a compact set of parameters characteristic of respiratory system functional status. 3D movements of points in the chest wall and the volumes of chest wall compartments (pulmonary rib cage, abdominal rib cage and abdomen) are considered. The co-ordinates of these points are measured using an optoelectronic system for 3D motion analysis. Principal component analysis is applied to these data. The behaviour of the eigenvectors of the covariance matrix of the 3D co-ordinates of the points on the chest wall shows close agreement with the pathology characteristics. The same is found for the percentage of total variance explained by the principal components of the volume variations. In this case, the higher values of variance percentage explained indicate independent motions (active or passive) in the degrees of freedom of the system identified by partitioning the total volume into compartments.