基于小波变换和矢量量化的二维ECG数据压缩算法

本研究针对心电数据的压缩问题,提出了一种新的基于小波变换的二维心电（ECG）数据压缩算法。该算法首先将一维原始ECG信号转化为二维序列信号,从而使ECG数据的两种相关性可得到充分地利用;然后对二维ECG序列进行小波变换,并对变换后的系数应用了一种改进的矢量量化（VQ）方法。在改进的VQ方法中,根据小波变换后系数的特点,构造了一种新的树矢量（TV）。利用本算法与已有基于小波变换的压缩算法和其他二维ECG信号的压缩算法,对MIT/BIH数据库中的心律不齐数据进行了对比压缩实验。结果表明：本算法适用于各种波形特征的ECG信号,并且在保证压缩质量的前提下,可以获得较大的压缩比,具有一定的应用价值。     

基于小波变换的心率变异性信号的分解

根据心率变异性（Heartr ate Variability，HRV）的牲，运用小波变换的分析方法将HRV信号分解成1／f分形部分和非1／f部分，有利于提取HRV信号的特征量和进行1／f部分的定量分析。     

麻醉期心率变异性的分形特性分析

采用关联维数、分形维数、尺度指数分析38例受试者麻醉期心率变异性信号的分形特性。结果表明:麻醉期心搏周期有显著性的分形特性变化,麻醉状态下心率变异性信号的关联维数(P<0.000001)明显低于清醒状态,而短时程尺度指数α1(P<0.0001)显著性地高于清醒状态。说明了麻醉期心率变异性信号的分形特性有明显地变化,提示运用非趋势波动分析方法分析麻醉期心率变异性信号的分形特性更适合于临床麻醉深度监测。     

心电信号的小波变换滤波算法的改进

对心电信号的滤波算法进行了改进。在利用小波变换实现心电图信号滤波算法的基础上，增加了对2^3尺度下小波分解所得细节信号的模极大值对的检测功能，以修复因滤波受损的心电信号的QRS波。经MIT／BIH标准心电数据库验证，试验表明，该方法行之有效。     

心电信号的小波变换滤波算法的改进

对心电信号的滤波算法进行了改进。在利用小波变换实现心电图信号滤波算法的基础上，增加了对2^3尺度下小波分解所得细节信号的模极大值对的检测功能，以修复因滤波受损的心电信号的QRS波。经MIT/BIH标准心电数据库验证，试验表明，该方法行之有效。     

基于小波变换和改进的瞬态独立成分分析融合算法的心电信号降噪方法

本文提出一种基于小波变换与独立成分分析融合的信号处理方法,该方法用于抑制多通道同步采集的心电信号包含的噪声。首先利用小波变换对各路同步采集的原始心电信号进行八尺度分解,获得低频逼近信号与高频细节信号,通过设定阈值的方法去除属于低频噪声部分的逼近信号。然后对保留的细节信号进行反变换实现信号重构,再利用包含预同步功能的瞬态独立成分分析改进算法从重构的信号中分离出高频噪声与心电信号独立成分。最后采用信噪比与均方根误差作为信号质量评价指标,将融合算法与单独使用瞬态独立成分分析算法的处理结果进行对比,结果表明融合算法进行降噪处理这一方法具有更高的信噪比和更低的均方根误差,本文提出的融合算法具有良好的心电信号降噪性能。     

多尺度样条小波变换

本文基于数学样条理论提出了一种新的多尺度小波变换，通过其模极值或零交叉可以有效地提取信号的特征。本文给出了该小波变换相应的分解和重建快速算法及其时域、频率响应，并验证了三次Ｂ样条小波变换在特征提取等实际应用中是渐近最优的。     

基于M带小波变换多重分形的胰腺内镜超声图像分类

提出胰腺内镜超声图像分形特征的提取与分类方法,用于胰腺内镜超声图像的计算机辅助诊断,以提高胰腺癌内镜超声早期诊断的准确性。通过改进基于分形维数的M带小波变换分形特征,引入多重分形维数并进行特征筛选,获得M带小波变换多重分形的特征矢量,采用贝叶斯分类器、支持向量机和AdaBoost等三种不同的分类器进行胰腺内镜超声图像的分类研究。实验表明：基于本研究分形特征矢量的分类,在运行时间和分类准确率上均优于基于传统分形特征的分类。此分类方法对胰腺内镜超声图像具有较高的分类准确性,有望为胰腺癌的临床诊断提供有价值的参考。     

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％。     

主成分分析和线性判别分析应用于心电信号特征提取和诊断算法研究

针对心脏疾病发病率高且不易自主检测的问题,提出了一种心电信号特征提取和分类诊断算法。首先对心电信号进行提升小波变换和改进半软阈值相结合的预处理变换,在去除心电信号的噪声后,利用主成分分析(principal component analysis,PCA)对心电信号进行降维,并利用核独立成分提取心电信号的非线性特征;同时离散小波变换提取去噪后心电信号的频域特征,基于线性判别分析(linear discriminant analysis, LDA)对频域统计特征进行降维处理。将两种不同的特征向量组成多域特征空间,最后利用支持向量机对多域特征空间分类,遗传算法对其参数进行寻优,从而实现心电信号特征的分类。实验结果表明,所提出的算法能够对5类心电节拍进行准确分类,分类效率达99.11%。     

Time-Frequency Analysis of Heart Rate Variability During Transient Segments

The spectral characteristics of heart rate variability (HRV) are related to the modulation of the autonomic nervous system. As the physiological condition is changed by such external stimuli such as drugs, postural changes, and anesthesia, or by internal deregulation such as in syncope, adjective autonomic responses could alter HRV characteristics. Time-frequency analysis is commonly used to investigate the time-related HRV characteristics. An alteration of the autonomic regulation resulting in a change in mean heart rate induces a transient component in heart rate, which, with any analysis method based on signals from multiple beats, results in the apparent spread of the spectrum of frequencies. This obscures the spectral components related to the autonomic function. In this paper we investigated the influence of the transient component in several time-frequency methods including the short-time Fourier transform, the Choi-Williams distribution, the smoothed pseudo Wigner–Ville distribution (SPWVD), the filtering SPWVD compensation, and the discrete wavelet transform. One simulated signal and two heart rate signals during general anesthesia and postural change were used for this assessment. The result demonstrates that the filtering SPWVD compensation and the discrete wavelet transform have small spectrum interference from the transient component. © 2001 Biomedical Engineering Society. PAC01: 8719Hh, 8780-y     

Wavelet transform analysis of heart rate variability during myocardial ischaemia

Analysis of heart rate variability (HRV) is a valuable, non-invasive method for quantifying autonomic cardiac control in humans. Frequency-domain analysis of HRV involving myocardial ischaemic episodes should take into account its non-stationary behaviour. The wavelet transform is an alternative tool for the analysis of non-stationary signals. Fourteen patients have been analysed, ranging from 40 to 64 years old and selected from the European Electrocardiographic ST-T Database (ESDB). These records contain 33 ST episodes, according to the notation of the ESDB, with durations of between 40s and 12min. A method for analysing HRV signals using the wavelet transform was applied to obtain a time-scale representation for very low-frequency (VLF), low-frequency (LF) and high-frequency (HF) bands using the orthogonal multiresolution pyramidal algorithm. The design and implementation using fast algorithms included a specially adapted decomposition quadrature mirror filter bank for the frequency bands of interest. Comparing a normality zone against the ischaemic episode in the same record, increases in LF (0.0112±0.0101 against 0.0175±0.0208s²Hz⁻¹; p<0.1) and HF (0.0011±0.0008 against 0.0017±0.0020s²Hz⁻¹; p<0.05) were obtained. The possibility of using these indexes to develop an ischaemic-episode classifier was also tested. Results suggest that wavelet analysis provides useful information for the assessment of dynamic changes and patterns of HRV during myocardial ischaemia.     

基于子波多尺度分辨的心电QRS波分类方法的研究

本文分析了心电QRS波的子波多尺度分辨特征,探讨了曲线非线性分开维数的计算方法,提出了一种新的QRS波的分类方法：对心电QRS复合波进行子波多尺度分解,在尺度为4的情况下,根据局部正负极大值对检测出它们前后两个零点Zp1,Zp2,计算出局部正负极大值对位于┃Zp1,Zp┃之间的曲线段的分形维数。根据局部正负极大值对的幅度和分开维数能很好地检出正常心电信号的QRS波及早搏信号;该方法具有很强的抗噪能力,提高了QRS波的正确检出率。     

基于小波变换的R波检测算法

介绍了一种将小波变换应用于ECG信号检测R波的算法。该算法主要是在离散小波变换和多分辨率分析原理的基础上，利用db1小波特有的时频域特性，运用Mallat快速算法对心电信号进行了3层小波分解，分别在2、3层小波分解的高频系数中选择一定的阈值作为R波的判定条件，实现R波检测。通过对MIT／BIH（Massachusetts Institute of Technology／Boston’s Beth Israel Hospital）心电数据库的R波检测，结果表明，该检测算法即使在噪声干扰和病态的情况下，也很容易实现对R波的准确检测和精确定位，具有相当高的定位精度，R波正确检测率高达到99．8％。     

基于独立成分和小波分析的胎儿心电提取

背景：在胎儿心电信号的采集过程中,会受到母体和其他噪声的强干扰,如何快捷与有效地提取出胎儿心电将成为重要的研究课题。 目的：采用结合独立成分分析和小波分析的方法对来自于同一母体的观测信号进行独立分量分离,得到有效的胎儿心电。 方法：结合独立成分分析和小波分析的算法进行胎儿心电的特征提取,首先对含噪信号进行小波变换,去除奇异信号和非平稳随机信号,然后对小波重构后的信号运用快速独立成分分析算法进行成分分析。 结果与结论：在胎儿心电信号的采集过程中,会受到母体和其他噪声的强干扰,但这些信号都是随机的,不相关的,可以认为它们间是相互独立的。采用结合独立成分和小波分析的方法对来自于同一母体的观测信号进行独立分量分离,得到有效的胎儿心电。实验证明该方法是一种有效的方法。     

全身麻醉状态下心率变异性的连续小波分析

为更准确地通过分析心率变异性(HRV)判断麻醉深度随时间的变化,需要密切关注麻醉状态下HRV低频(LF)和高频(HF)成分随时间的变化情况,采用连续小波变换(CWT),将CWT中的尺度转换为频率,对患者麻醉前后的HRV信号(RR间期序列)进行了时频分析。其时频能量图以及LF、HF能量值都表明麻醉后HRV信号的LF和HF成分受到了抑制,LF/HF值也由麻醉前的9.021 9降为麻醉后的3.557 3。CWT和传统的时频分析方法在分析同一麻醉后HRV的时频分布表明,CWT可以更准确地定位HRV时域信号中出现突变的时间以及引起频率变化的频段范围。因此,CWT作为分析麻醉状态下HRV的一种新方法,能提供HRV更为准确的时频定位,进而提供更为准确的麻醉深度监控结果。     

Analysis of heart rate variability signal during meditation using deterministic-chaotic quantifiers

Abstract

This study investigated the level of chaos and the existence of fractal patterns in the heart rate variability (HRV) signal prior to meditation and during meditation using two quantifiers adapted from non-linear dynamics and deterministic chaos theory: (1) component central tendency measures (CCTMs) and (2) Higuchi fractal dimension (HFD). CCTM quantifies degree of variability/chaos in the specified quadrant of the second-order difference plot for HRV time series, while HFD quantifies dimensional complexity of the HRV series. Both the quantifiers yielded excellent results in discriminating the different psychophysiological states. The study found (1) significantly higher first quadrant CCTM values and (2) significantly lower HFD values during meditation state compared to pre-meditation state. Both of these can be attributed to the respiratory-modulated oscillations shifting to the lower frequency region by parasympathetic tone during meditation. It is thought that these quantifiers are most promising in providing new insight into the evolution of complexity of underlying dynamics in different physiological states.     

EEG-based motor imagery classification using enhanced active segment selection and adaptive classifier

In this study, an adaptive electroencephalogram (EEG) analysis system is proposed for a two-session, single-trial classification of motor imagery (MI) data. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the adaptive linear discriminant analysis (LDA) is used for classification of left- and right-hand MI data and for simultaneous and continuous update of its parameters. In addition to the original use of continuous wavelet transform (CWT) and Student's two-sample t-statistics, the 2D anisotropic Gaussian filter is proposed to further refine the selection of active segments. The multiresolution fractal features are then extracted from wavelet data by means of modified fractal dimension. The classification in session 2 is performed by adaptive LDA, which is trial-by-trial updated using the Kalman filter after the trial is classified. Compared with original active segment selection and non-adaptive LDA on six subjects from two data sets, the results indicate that the proposed method is helpful to realize adaptive BCI systems.     

HRV信号关联维计算中参数选取的研究

健康和有疾病的心率变异性（HRV）参数有明显差异,计算关联维是识别这种差异的一种重要手段。用传统的G—P算法计算关联维时,嵌入维数m、延迟时间τ、及序列长度N等参数的选取会对最终计算结果有很大影响。本文从理论和实验方面论述了如何选取这些参数以获得正确的结果,并且将其应用于正常组和心率不齐疾病组进行对照,结果显示关联维可以有效地表征由于疾病对于心脏节律造成的影响。