基于恒定空间分辨率离散序列小波变换的眼底血管造影图像拼接方法

将恒定空间分辨率离散序列小波变换（discrete sequence wavelet transform,DSWT)应用于眼底吲哚青绿血管造影（indocyanine green angiography,ICGA)图像的拼接,解决了传统基为2的DSWT会导致分解结果的空间分辨率下降的问题。提出对图像小波分解细节逼近和平滑逼近分别使用加权平均拼接和直接平均拼接进行处理的策略,以得到兼顾视觉效果和保真性的拼接结果。并且针对眼底图像背景光照不一致,提出在小波域进行处理的策略。实验结果表明拼接算法效果良好。     

Data reduction using a discrete wavelet transform in discriminant analysis of very high dimensionality data

We present a method of data reduction using a wavelet transform in discriminant analysis when the number of variables is much greater than the number of observations. The method is illustrated with a prostate cancer study, where the sample size is 248, and the number of variables is 48,538 (generated using the ProteinChip technology). Using a discrete wavelet transform, the 48,538 data points are represented by 1271 wavelet coefficients. Information criteria identified 11 of the 1271 wavelet coefficients with the highest discriminatory power. The linear classifier with the 11 wavelet coefficients detected prostate cancer in a separate test set with a sensitivity of 97% and specificity of 100%.     

Nonstationary Heart Rate Variability in Respiratory Tests

A new method was proposed for processing a nonstationary heart rate by using frequency-modulated signals rather than amplitude-modulated signals equally spaced over several points of time as in the conventional method. A frequency-modulated signal is a set of identical Gaussian peaks that coincide with the true time points of heart beats. A continuous wavelet transform was used to quantitatively describe the heart rhythm signal. A test with controlled breathing was performed as an example and included three consecutive stages: rest, rhythmic breathing at a specified frequency, and exhalation. Tachograms recorded during the breath test was found to be a nonstationary signal with the alternation of peaks of different spectral ranges. A system of quantitative parameters was developed to describe the dynamics of changes in the spectral properties of the tachogram in transitional areas. A static clustering by the effect of the respiratory test and a dynamic clustering in order to identify the time points when the autonomic nervous system is stressed were performed for all subjects. The article discusses the prospects of using the method as a means to analyze the transient effects in various functional tests and as biofeedback that would help to change the heart rhythm.     

Dynamics of local maxima chains in spectra of human electroencephalogram

The continuous wavelet transform was applied to the human EEG signals recorded in different states of brain activity. The dynamics of local maxima chains in the matrices of the continuous wavelet transform coefficients was studied. The typologization method was developed for local maxima chains to separate by their drift in the frequency space as well as by dynamics of their signal “energy.” The method proved to be highly informative. It was shown that it was highly sensitive to a selection of one of two responses to the test question. It is determined that local maxima chains in most cases are gradually increasing and decreasing in the frequency space and by changes in the values of their continuous wavelet transform coefficients. The functional asymmetry in local maxima chains types’ distribution is determined. The results obtained allow us to consider the types of the local maxima chains dynamics as a new phenomenon of EEG activity.     

Wavelet-based analysis of MMN responses in children.

Event-related potentials (ERPs) as part of the EEG are applied to assess auditory processing in children. Mismatch negativity (MMN) is a change-specific component of ERPs that indicates a pre-cognitive discrimination process. MMN responses were recorded in 10 healthy preschool children to four different types of signal changes. The signals investigated were processed using a discrete wavelet transform (DWT) to analyze the characteristics of the ERP components. All children showed distinct MMN that was significant in all tasks. The MMN amplitudes varied between subjects and depended on the different tasks. The wavelet transform allowed simplified analysis and quantification of the MMN component, as well as the double-peak structure of the P1 component. The variation in MMN amplitudes suggests the possibility of determining individual auditory profiles. Owing to the shorter time required, the MMN paradigm suggested combined with the DWT proposed offers a new objective investigation method for children.     

Multi-scale order recurrence quantification analysis of EEG signals evoked by manual acupuncture in healthy subjects

To explore the effects of manual acupuncture (MA) on brain activities, we design an experiment that acupuncture at acupoint ST36 of right leg with four different frequencies to obtain electroencephalograph (EEG) signals. Many studies have demonstrated that the complexity of EEG can reflect the states of brain function, so we propose to adopt order recurrence quantification analysis combined with discrete wavelet transform, to analyze the dynamical characteristics of different EEG rhythms under acupuncture, further to explore the effects of MA on the complexity of brain activities from multi-scale point of view. By analyzing the complexity of five EEG rhythms, it is found that the complexity of delta rhythm during acupuncture is lower than before acupuncture, and for alpha rhythm that is higher, but for beta, theta and gamma rhythms there are no obvious changes. All of those effects are especially obvious during acupuncture with frequency of 200 times/min. Furthermore, the determinism extracted from delta, alpha and gamma rhythms can be regarded as a characteristic parameter to distinguish the state acupuncture at 200 times/min and the state before acupuncture. These results can provide a theoretical support for selecting appropriate acupuncture frequency for patients in clinical, and the proposed methods have the potential of exploring the effects of acupuncture on brain activities.     

Continuous wavelet analysis: A new method for studying nonstationary oscillations in the cardiac rhythm

Continuous wavelet analysis can be used to quantitatively describe local changes in cardiac rhythm oscillations. The following new indices of the wave structure of the cardiac rhythm were suggested: the rate of changes in the frequency of the oscillatory component within a given time interval, the lengths of the fading periods for specific frequency oscillations detected on the cardiointervalogram, and the changes in the instantaneous frequency-amplitude ratios during the observation period. These indices allow a deeper insight into sympathetic and parasympathetic oscillators that affect the cardiac rhythm.     

基于DWT-GA-PLS的土壤碱解氮含量高光谱估测方法

以山东齐河县为研究区,实地采集土壤样本,在土样高光谱测试并进行一阶导数变换的基础上,先运用离散小波变换(DWT)对土壤光谱去噪降维,然后采用遗传算法(GA)筛选土壤碱解氮定量估测模型的参与变量,最后应用偏最小二乘(PLS)回归构建土壤碱解氮含量的估测模型.结果表明: 离散小波变换结合遗传算法和偏最小二乘法(DWT-GA-PLS)用于土壤碱解氮含量定量估测,不仅可压缩光谱变量、减少模型参与变量,而且可改善模型估测准确度;较之于采用土壤全谱,小波离散分解1～2层低频系数构建的模型在参与变量大幅减少的情况下,取得更准确或与之相当的预测结果,其中,基于第2层小波低频系数采用GA筛选变量构建的PLS模型的预测效果表现最好,预测R²达到0.85,RMSE为8.11 mg·kg^-1,RPD为2.53.说明DWT-GA-PLS用于土壤碱解氮含量高光谱定量估测的有效性.     

Using support vector machines to distinguish enzymes: Approached by incorporating wavelet transform

The enzymatic attributes of newly found protein sequences are usually determined either by biochemical analysis of eukaryotic and prokaryotic genomes or by microarray chips. These experimental methods are both time-consuming and costly. With the explosion of protein sequences registered in the databanks, it is highly desirable to develop an automated method to identify whether a given new sequence belongs to enzyme or non-enzyme. The discrete wavelet transform (DWT) and support vector machine (SVM) have been used in this study for distinguishing enzyme structures from non-enzymes. The networks have been trained and tested on two datasets of proteins with different wavelet basis functions, decomposition scales and hydrophobicity data types. Maximum accuracy has been obtained using SVM with a wavelet function of Bior2.4, a decomposition scale j=5, and Kyte-Doolittle hydrophobicity scales. The results obtained by the self-consistency test, jackknife test and independent dataset test are encouraging, which indicates that the proposed method can be employed as a useful assistant technique for distinguishing enzymes from non-enzymes.     

基于小波变换的单次诱发电位信号时频分析

比较小波变换和平均叠加两种方法提取“模拟自然阅读”刺激模式下的诱发脑电信号,分析其时频特性,并进行脑功能源分布定位分析。结果显示,采用平均叠加法来提取和分析诱发电位信号,损失了某些重要的诱发电位成分,且其功能源分布定位反映的只是等效功能源的静态过程;而使用小波变换和脑功能源定位来提取和分析单次诱发电位信号,既能观察到丰富的诱发电位成分,又能反映脑功能源的实时动态活动过程。这表明,小波变换下的时频分析是脑电信号处理的一种可行的新方法。     

Discrete wavelet transform analysis of surface electromyography for the fatigue assessment of neck and shoulder muscles

Assessment of neuromuscular fatigue is essential for early detection and prevention of risks associated with work-related musculoskeletal disorders. In recent years, discrete wavelet transform (DWT) of surface electromyography (SEMG) has been used to evaluate muscle fatigue, especially during dynamic contractions when the SEMG signal is non-stationary. However, its application to the assessment of work-related neck and shoulder muscle fatigue is not well established. Therefore, the purpose of this study was to establish DWT analysis as a suitable method to conduct quantitative assessment of neck and shoulder muscle fatigue under dynamic repetitive conditions. Ten human participants performed 40 min of fatiguing repetitive arm and neck exertions while SEMG data from the upper trapezius and sternocleidomastoid muscles were recorded. The ten of the most commonly used wavelet functions were used to conduct the DWT analysis. Spectral changes estimated using power of wavelet coefficients in the 12–23 Hz frequency band showed the highest sensitivity to fatigue induced by the dynamic repetitive exertions. Although most of the wavelet functions tested in this study reasonably demonstrated the expected power trend with fatigue development and recovery, the overall performance of the “Rbio3.1” wavelet in terms of power estimation and statistical significance was better than the remaining nine wavelets.     

Time/frequency events of surface mechanomyographic signals resolved by nonlinearly scaled wavelets

The purpose of this investigation is to introduce a wavelet analysis designed for analyzing short events reflecting bursts of muscle activity in non-stationary mechanomyographic (MMG) signals. A filter bank of eleven nonlinearly scaled wavelets that maintain the optimal combination of time and frequency resolution across the frequency range of MMG signals (5–100 Hz) was used for the analysis. A comparison with the short-time Fourier transform, Wigner-Ville transform and continuous wavelet transform using a test signal with known time–frequency characteristics showed that the MMG wavelet analysis resolved the intensity, timing, and frequencies of events in a more distinct way without overemphasizing high or low frequencies or generating interference terms. The analysis was used to process MMG signals from the vastus lateralis, rectus femoris, and vastus medialis muscles obtained during maximal concentric and eccentric isokinetic movements. Muscular events were observed that were precisely located in time and frequency in a muscle-specific way, thereby showing periods of synergistic contractions of the quadriceps muscles. The MMG wavelet spectra showed different spectral bands for concentric and eccentric isokinetic movements. In addition, the high and low frequency bands seemed to be activated independently during the isokinetic movement. What generates these bands is not yet known, however, the MMG wavelet analysis was able to resolve them, and is therefore applicable to non-stationary MMG signals.     

Dating the age of admixture via wavelet transform analysis of genome-wide data

We describe a PCA-based genome scan approach to analyze genome-wide admixture structure, and introduce wavelet transform analysis as a method for estimating the time of admixture. We test the wavelet transform method with simulations and apply it to genome-wide SNP data from eight admixed human populations. The wavelet transform method offers better resolution than existing methods for dating admixture, and can be applied to either SNP or sequence data from humans or other species.     

Empirical Mode Decomposition and Wavelet Transform Based ECG Data Compression Scheme

ObjectiveIn health-care systems, compression is an essential tool to solve the storage and transmission problems. In this regard, this paper reports a new electrocardiogram (ECG) data compression scheme which employs sifting function based empirical mode decomposition (EMD) and discrete wavelet transform.MethodEMD based on sifting function is utilized to get the first intrinsic mode function (IMF). After EMD, the first IMF and four significant sifting functions are combined together. This combination is free from many irrelevant components of the signal. Discrete wavelet transform (DWT) with mother wavelet ‘bior4.4’ is applied to this combination. The transform coefficients obtained after DWT are passed through dead-zone quantization. It discards small transform coefficients lying around zero. Further, integer conversion of coefficients and run-length encoding are utilized to achieve a compressed form of ECG data.ResultsCompression performance of the proposed scheme is evaluated using 48 ECG records of the MIT-BIH arrhythmia database. In the comparison of compression results, it is observed that the proposed method exhibits better performance than many recent ECG compressors. A mean opinion score test is also conducted to evaluate the true quality of the reconstructed ECG signals.ConclusionThe proposed scheme offers better compression performance with preserving the key features of the signal very well.     

sEMG wavelet-based indices predicts muscle power loss during dynamic contractions

The purpose of this study was to investigate the sensitivity of new surface electromyography (sEMG) indices based on the discrete wavelet transform to estimate acute exercise-induced changes on muscle power output during a dynamic fatiguing protocol. Fifteen trained subjects performed five sets consisting of 10 leg press, with 2 min rest between sets. sEMG was recorded from vastus medialis (VM) muscle. Several surface electromyographic parameters were computed. These were: mean rectified voltage (MRV), median spectral frequency (F_med), Dimitrov spectral index of muscle fatigue (FI_nsm5), as well as five other parameters obtained from the stationary wavelet transform (SWT) as ratios between different scales. The new wavelet indices showed better accuracy to map changes in muscle power output during the fatiguing protocol. Moreover, the new wavelet indices as a single parameter predictor accounted for 46.6% of the performance variance of changes in muscle power and the log-FI_nsm5 and MRV as a two-factor combination predictor accounted for 49.8%. On the other hand, the new wavelet indices proposed, showed the highest robustness in presence of additive white Gaussian noise for different signal to noise ratios (SNRs). The sEMG wavelet indices proposed may be a useful tool to map changes in muscle power output during dynamic high-loading fatiguing task.     

Age-related differences in the dynamics of the skin blood flow oscillations during postocclusive reactive hyperemia

Age-related changes in peripheral microcirculation were studied using laser Doppler flowmetry in 60 apparently healthy subjects. The response of microcirculation to short-term ischemia was studied using the occlusion test. Changes in the amplitude of the peripheral blood flow oscillations were determined using time-amplitude analysis based on continuous adaptive wavelet filtration. The oscillation amplitude in the frequency range of the heart rate was found to reach the maximum with a delay after the removal of the occlusion, whereas in the range of the respiratory rhythm, no delay was observed. The hyperemic response to short-term ischemia is assumed to develop under the predominant influence of the arterial-arteriolar component, whereas the dynamics of amplitude oscillations in the range of the respiratory rhythm is a result of the devastation of the venular component after removal of occlusion. In response to short-term ischemia, the maximum oscillation amplitudes of myogenic, neurogenic, and endothelial rhythms decreased with age, which demonstrates the restriction of the regulatory control of the peripheral blood flow by the corresponding systems.     

小波变换及其在医学图像处理中的应用

医学图像的好坏直接影响着医生对病情的诊断和治疗,因此利用数字图像处理等技术对医学图像进行有效的处理,已成为医学图像处理研究和开发的一大热点。小波变换是对傅里叶变换的继承和发展,在医学影像领域有着广泛的应用前景。本文介绍了二维离散小渡变换的一般形式,在图像分解与重构的基础上．系统地阐述了利用小小组变换的时频域特性与多分辨分析对医学图像进行去噪、增强以及边缘提取等深层次的处理,有效的改善图像质量。     

基于独立元分析和联合小波熵检测多导联ECG信号的QRS

QRS波群的准确定位是ECG信号自动分析的基础。为提高QRS检测率,提出一种基于独立元分析（ICA）和联合小波熵（CWS）检测多导联ECG信号QRS的算法。ICA算法从滤波后的多导联ECG信号中分离出对应心室活动的独立元;然后对各独立元进行连续小波变换（CWT）,重构小波系数的相空间,结合相空间中的QRS信息对独立元排序;最后检测排序后独立元的CWS得到QRS信息。实验对St.Petersburg12导联心率失常数据库及64导联犬心外膜数据库测试,比较本文算法与单导联QRS检测算法和双导联QRS检测算法的性能。结果表明,该文算法的性能最好,检测准确率分别为99.98%和100%。     

A Slantlet transform based intelligent system for magnetic resonance brain image classification

The present paper proposes the development of a new approach for automated diagnosis, based on classification of magnetic resonance (MR) human brain images. Wavelet transform based methods are a well-known tool for extracting frequency space information from non-stationary signals. In this paper, the proposed method employs an improved version of orthogonal discrete wavelet transform (DWT) for feature extraction, called Slantlet transform, which can especially be useful to provide improved time localization with simultaneous achievement of shorter supports for the filters. For each two-dimensional MR image, we have computed its intensity histogram and Slantlet transform has been applied on this histogram signal. Then a feature vector, for each image, is created by considering the magnitudes of Slantlet transform outputs corresponding to six spatial positions, chosen according to a specific logic. The features hence derived are used to train a neural network based binary classifier, which can automatically infer whether the image is that of a normal brain or a pathological brain, suffering from Alzheimer's disease. An excellent classification ratio of 100% could be achieved for a set of benchmark MR brain images, which was significantly better than the results reported in a very recent research work employing wavelet transform, neural networks and support vector machines.     

Comparison of forms of "unfit histograms" by a wavelet transform method

Two methods checking changes in time of histogram patterns were investigated. These methods are relied on discrete wavelet transform. The result is that in the some cases these methods can more effectively to examine latency in the behavior of histograms form constructed by on overlapping short segments of experimental data.