基于变分模态分解与多尺度排列熵的生物组织变性识别

根据高强度聚焦超声(HIFU)治疗中超声散射回波信号的特点,本文利用变分模态分解(VMD)与多尺度排列熵(MPE)对生物组织变性识别进行了研究.首先对生物组织中的超声散射回波信号进行变分模态分解,根据各阶模态的功率谱信息熵值分离出噪声分量和有用分量;对分离出的有用信号进行重构并提取其多尺度排列熵;然后通过Gustafson-Kessel (GK)模糊聚类确定聚类中心,采用欧氏贴近度与择近原则对生物组织进行变性识别.将所提方法应用于HIFU治疗中超声散射回波信号实验数据,用遗传算法对多尺度排列熵的参数优化后,对293例未变性组织和变性组织的超声散射回波信号数据进行了多尺度排列熵分析,发现变性组织的超声散射回波信号的多尺度排列熵值要高于未变性组织;多尺度排列熵可以较好地识别生物组织是否变性.相对于EMD-MPE-GK模糊聚类以及VMD-小波熵(WE)-GK模糊聚类变性识别方法,本文所提方法中变性与未变性组织特征交叠区域数据点更少,聚类效果和分类性能更好;本实验环境下生物组织变性识别结果表明,该方法的识别率更高,高达93.81%.     

Targeted damage effects of high intensity focused ultrasound (HIFU) on liver tissues of Guizhou Province miniswine

HIFU can pass through tissues and accurately damage target tissues inside organisms. This article reports on the oriented damage effects of HIFU upon miniswine internal and external liver tissues, and suggests a new conception of the 'biological focal field'. The results revealed that: (1) HIFU can be used to damage accurately liver tissues under the guide of a B-modal ultrasound device; (2) the scope of the injury is connected with sound intensity and irradiation time; and (3) the different layers of tissue through which the ultrasound has passed remain undamaged.     

Construction and Application of Functional Brain Network Based on Entropy

Functional brain network (FBN) is an intuitive expression of the dynamic neural activity interaction between different neurons, neuron clusters, or cerebral cortex regions. It can characterize the brain network topology and dynamic properties. The method of building an FBN to characterize the features of the brain network accurately and effectively is a challenging subject. Entropy can effectively describe the complexity, non-linearity, and uncertainty of electroencephalogram (EEG) signals. As a relatively new research direction, the research of the FBN construction method based on EEG data of fatigue driving has broad prospects. Therefore, it is of great significance to study the entropy-based FBN construction. We focus on selecting appropriate entropy features to characterize EEG signals and construct an FBN. On the real data set of fatigue driving, FBN models based on different entropies are constructed to identify the state of fatigue driving. Through analyzing network measurement indicators, the experiment shows that the FBN model based on fuzzy entropy can achieve excellent classification recognition rate and good classification stability. In addition, when compared with the other model based on the same data set, our model could obtain a higher accuracy and more stable classification results even if the length of the intercepted EEG signal is different.     

Composite Multivariate Multi-Scale Permutation Entropy and Laplacian Score Based Fault Diagnosis of Rolling Bearing

As a powerful tool for measuring complexity and randomness, multivariate multi-scale permutation entropy (MMPE) has been widely applied to the feature representation and extraction of multi-channel signals. However, MMPE still has some intrinsic shortcomings that exist in the coarse-grained procedure, and it lacks the precise estimation of entropy value. To address these issues, in this paper a novel non-linear dynamic method named composite multivariate multi-scale permutation entropy (CMMPE) is proposed, for optimizing insufficient coarse-grained process in MMPE, and thus to avoid the loss of information. The simulated signals are used to verify the validity of CMMPE by comparing it with the often-used MMPE method. An intelligent fault diagnosis method is then put forward on the basis of CMMPE, Laplacian score (LS), and bat optimization algorithm-based support vector machine (BA-SVM). Finally, the proposed fault diagnosis method is utilized to analyze the test data of rolling bearings and is then compared with the MMPE, multivariate multi-scale multiscale entropy (MMFE), and multi-scale permutation entropy (MPE) based fault diagnosis methods. The results indicate that the proposed fault diagnosis method of rolling bearing can achieve effective identification of fault categories and is superior to comparative methods.     

New Techniques and Designs of Focusing Piezoelectric Transducers for Ultrasonic Diagnostics and Therapy

New techniques of forming high intensity focused ultrasound (HIFU) fields using dynamic focusing and harmonic multifrequency excitation are developed for ultrasonic diagnostics and therapy. New designs of HIFU transducers based on high-performance composite materials are developed and studied. Finite-element and finite-difference simulations of HIFU transducers and processes of ultrasonic wave propagation in biological tissues are performed. The parameters of piezoceramic materials, piezoelements, and the acoustic fields of focusing ultrasonic transducers are measured. Experiments are performed on biological tissues ex vivo that confirm the efficiency, selectivity, and safety of the developed HIFU transducers and techniques of forming acoustic fields.     

Intelligent Fault Diagnosis of Rolling-Element Bearings Using a Self-Adaptive Hierarchical Multiscale Fuzzy Entropy

The fuzzy-entropy-based complexity metric approach has achieved fruitful results in bearing fault diagnosis. However, traditional hierarchical fuzzy entropy (HFE) and multiscale fuzzy entropy (MFE) only excavate bearing fault information on different levels or scales, but do not consider bearing fault information on both multiple layers and multiple scales at the same time, thus easily resulting in incomplete fault information extraction and low-rise identification accuracy. Besides, the key parameters of most existing entropy-based complexity metric methods are selected based on specialist experience, which indicates that they lack self-adaptation. To address these problems, this paper proposes a new intelligent bearing fault diagnosis method based on self-adaptive hierarchical multiscale fuzzy entropy. On the one hand, by integrating the merits of HFE and MFE, a novel complexity metric method, named hierarchical multiscale fuzzy entropy (HMFE), is presented to extract a multidimensional feature matrix of the original bearing vibration signal, where the important parameters of HMFE are automatically determined by using the bird swarm algorithm (BSA). On the other hand, a nonlinear feature matrix classifier with strong robustness, known as support matrix machine (SMM), is introduced for learning the discriminant fault information directly from the extracted multidimensional feature matrix and automatically identifying different bearing health conditions. Two experimental results on bearing fault diagnosis show that the proposed method can obtain average identification accuracies of 99.92% and 99.83%, respectively, which are higher those of several representative entropies reported by this paper. Moreover, in the two experiments, the standard deviations of identification accuracy of the proposed method were, respectively, 0.1687 and 0.2705, which are also greater than those of the comparison methods mentioned in this paper. The effectiveness and superiority of the proposed method are verified by the experimental results.     

Refined Composite Multi-Scale Reverse Weighted Permutation Entropy and Its Applications in Ship-Radiated Noise

Ship-radiated noise is one of the important signal types under the complex ocean background, which can well reflect physical properties of ships. As one of the valid measures to characterize the complexity of ship-radiated noise, permutation entropy (PE) has the advantages of high efficiency and simple calculation. However, PE has the problems of missing amplitude information and single scale. To address the two drawbacks, refined composite multi-scale reverse weighted PE (RCMRWPE), as a novel measurement technology of describing the signal complexity, is put forward based on refined composite multi-scale processing (RCMP) and reverse weighted PE (RWPE). RCMP is an improved method of coarse-graining, which not only solves the problem of single scale, but also improves the stability of traditional coarse-graining; RWPE has been proposed more recently, and has better inter-class separability and robustness performance to noise than PE, weighted PE (WPE), and reverse PE (RPE). Additionally, a feature extraction scheme of ship-radiated noise is proposed based on RCMRWPE, furthermore, RCMRWPE is combined with discriminant analysis classifier (DAC) to form a new classification method. After that, a large number of comparative experiments of feature extraction schemes and classification methods with two artificial random signals and six ship-radiated noise are carried out, which show that the proposed feature extraction scheme has better performance in distinguishing ability and stability than the other three similar feature extraction schemes based on multi-scale PE (MPE), multi-scale WPE (MWPE), and multi-scale RPE (MRPE), and the proposed classification method also has the highest recognition rate.     

A Novel Feature Extraction Method for Power Transformer Vibration Signal Based on CEEMDAN and Multi-Scale Dispersion Entropy

Effective diagnosis of vibration fault is of practical significance to ensure the safe and stable operation of power transformers. Aiming at the traditional problems of transformer vibration fault diagnosis, a novel feature extraction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multi-scale dispersion entropy (MDE) was proposed. In this paper, CEEMDAN method is used to decompose the original transformer vibration signal. Additionally, then MDE is used to capture multi-scale fault features in the decomposed intrinsic mode functions (IMFs). Next, the principal component analysis (PCA) method is employed to reduce the feature dimension and extract the effective information in vibration signals. Finally, the simplified features are sent into density peak clustering (DPC) to get the fault diagnosis results. The experimental data analysis shows that CEEMDAN-MDE can effectively extract the information of the original vibration signals and DPC can accurately diagnose the types of transformer faults. By comparing different algorithms, the practicability and superiority of this proposed method are verified.     

Fault Recognition of Rolling Bearings Based on Parameter Optimized Multi-Scale Permutation Entropy and Gath-Geva

To extract fault features of rolling bearing vibration signals precisely, a fault diagnosis method based on parameter optimized multi-scale permutation entropy (MPE) and Gath-Geva (GG) clustering is proposed. The method can select the important parameters of MPE method adaptively, overcome the disadvantages of fixed MPE parameters and greatly improve the accuracy of fault identification. Firstly, aiming at the problem of parameter determination and considering the interaction among parameters comprehensively of MPE, taking skewness of MPE as fitness function, the time series length and embedding dimension were optimized respectively by particle swarm optimization (PSO) algorithm. Then the fault features of rolling bearing were extracted by parameter optimized MPE and the standard clustering centers is obtained with GG clustering. Finally, the samples are clustered with the Euclid nearness degree to obtain recognition rate. The validity of the parameter optimization is proved by calculating the partition coefficient and average fuzzy entropy. Compared with unoptimized MPE, the propose method has a higher fault recognition rate.     

Pulse compression technique for simultaneous HIFU surgery and ultrasonic imaging: a preliminary study

In an ultrasound image-guided High Intensity Focused Ultrasound (HIFU) surgery, reflected HIFU waves received by an imaging transducer should be suppressed for real-time simultaneous imaging and therapy. In this paper, we investigate the feasibility of pulse compression scheme combined with notch filtering in order to minimize these HIFU interference signals. A chirp signal modulated by the Dolph-Chebyshev window with 3-9 MHz frequency sweep range is used for B-mode imaging and 4 MHz continuous wave is used for HIFU. The second order infinite impulse response notch filters are employed to suppress reflected HIFU waves whose center frequencies are 4 MHz and 8 MHz. The prototype integrated HIFU/imaging transducer that composed of three rectangular elements with a spherically con-focused aperture was fabricated. The center element has the ability to transmit and receive 6 MHz imaging signals and two outer elements are only used for transmitting 4 MHz continuous HIFU wave. When the chirp signal and 4 MHz HIFU wave are simultaneously transmitted to the target, the reflected chirp signals mixed with 4 MHz and 8 MHz HIFU waves are detected by the imaging transducer. After the application of notch filtering with pulse compression process, HIFU interference waves in this mixed signal are significantly reduced while maintaining original imaging signal. In the single scanline test using a strong reflector, the amplitude of the reflected HIFU wave is reduced to −45 dB. In vitro test, with a sliced porcine muscle shows that the speckle pattern of the restored B-mode image is close to that of the original image. These preliminary results demonstrate the potential for the pulse compression scheme with notch filtering to achieve real-time ultrasound image-guided HIFU surgery.     

Multiscale Entropy Analysis of Short Signals: The Robustness of Fuzzy Entropy-Based Variants Compared to Full-Length Long Signals

Multiscale entropy (MSE) analysis is a fundamental approach to access the complexity of a time series by estimating its information creation over a range of temporal scales. However, MSE may not be accurate or valid for short time series. This is why previous studies applied different kinds of algorithm derivations to short-term time series. However, no study has systematically analyzed and compared their reliabilities. This study compares the MSE algorithm variations adapted to short time series on both human and rat heart rate variability (HRV) time series using long-term MSE as reference. The most used variations of MSE are studied: composite MSE (CMSE), refined composite MSE (RCMSE), modified MSE (MMSE), and their fuzzy versions. We also analyze the errors in MSE estimations for a range of incorporated fuzzy exponents. The results show that fuzzy MSE versions—as a function of time series length—present minimal errors compared to the non-fuzzy algorithms. The traditional multiscale entropy algorithm with fuzzy counting (MFE) has similar accuracy to alternative algorithms with better computing performance. For the best accuracy, the findings suggest different fuzzy exponents according to the time series length.     

Fundamental study on subharmonic imaging by irradiation of amplitude-modulated ultrasound waves

The second harmonic and subharmonic components, the frequencies of which are twice and one half the fundamental frequency, are included in echoes from contrast agents. An imaging method, which employs a second harmonic (second harmonic imaging), is widely used in medical diagnoses. On the other hand, subharmonic is expected to provide a higher contrast between biological tissues and blood flow because echo signals are generated only from blood containing the contrast agents. However, the subharmonic component echo signal power from contrast agents is relatively low. This has resulted in little progress in the field of subharmonic imaging. In this study, a new imaging method is proposed using amplitude-modulated waves as transmitted waves combined with the pulse inversion method to enhance subharmonic echo signals. Two optimal frequencies are set, including the modulated waves, F(1) and F(2), so that the subharmonic frequency of F(1) and the second harmonic frequency of F(2) may result in the same value. This allows a more powerful signal at the frequency band because the second harmonic and subharmonic components are integrated. Furthermore, a B-mode ultrasound image of an agar phantom that imitated biological tissue and showed the effectiveness of our method was reconstructed. As a result, the echo power of the subharmonic component was enhanced by approximately 11.8 dB more than the conventional method and the signal to noise ratio showed an improvement of 7.6 dB.     

应用变分模态分解及能量熵的扬声器异常声分类

为更准确地实现扬声器异常声分类以及促进其分类的自动化,提出一种基于变分模态分解(Variational Mode Decom-position,VMD)能量熵和遗传算法优化的支持向量机(Genetic Algorithm-Support Vector Machines,GA-SVM)的扬声器异常声分类方法.首先对测得的...     

基于分数导数研究高强度聚焦超声的非线性声场

在高强度聚焦超声(high intensity focused ultrasound, HIFU) 的研究中, 生物组织的衰减和色散性质会对声能量的空间分布产生影响. 本文提出应用分数导数修正非线性Khokhlov-Zabolotskaya-Kuznetsov (KZK)方程, 研究生物组织中非线性HIFU声场. 对三种生物仿体的衰减和声速色散的理论实验研究表明分数导数应用的可行性, 在此基础上通过数值仿真分析研究了衰减及声速随频率的变化对HIFU焦域分布的影响. 研究结果表明, 在计算强非线性聚焦超声时, 由于高次谐波的强色散作用, 引入分数导数来解决生物组织特殊的衰减以及色散问题可进一步提高HIFU治疗的安全性. 关键词： 分数导数 声衰减 色散 高强度聚焦超声     

Research on Driving Fatigue Alleviation Using Interesting Auditory Stimulation Based on VMD-MMSE

The accurate detection and alleviation of driving fatigue are of great significance to traffic safety. In this study, we tried to apply the modified multi-scale entropy (MMSE) approach, based on variational mode decomposition (VMD), to driving fatigue detection. Firstly, the VMD was used to decompose EEG into multiple intrinsic mode functions (IMFs), then the best IMFs and scale factors were selected using the least square method (LSM). Finally, the MMSE features were extracted. Compared with the traditional sample entropy (SampEn), the VMD-MMSE method can identify the characteristics of driving fatigue more effectively. The VMD-MMSE characteristics combined with a subjective questionnaire (SQ) were used to analyze the change trends of driving fatigue under two driving modes: normal driving mode and interesting auditory stimulation mode. The results show that the interesting auditory stimulation method adopted in this paper can effectively relieve driving fatigue. In addition, the interesting auditory stimulation method, which simply involves playing interesting auditory information on the vehicle-mounted player, can effectively relieve driving fatigue. Compared with traditional driving fatigue-relieving methods, such as sleeping and drinking coffee, this interesting auditory stimulation method can relieve fatigue in real-time when the driver is driving normally.     

A Stepwise Assessment of Parsimony and Fuzzy Entropy in Species Distribution Modelling

Entropy is intrinsic to the geographical distribution of a biological species. A species distribution with higher entropy involves more uncertainty, i.e., is more gradually constrained by the environment. Species distribution modelling tries to yield models with low uncertainty but normally has to reduce uncertainty by increasing their complexity, which is detrimental for another desirable property of the models, parsimony. By modelling the distribution of 18 vertebrate species in mainland Spain, we show that entropy may be computed along the forward-backwards stepwise selection of variables in Logistic Regression Models to check whether uncertainty is reduced at each step. In general, a reduction of entropy was produced asymptotically at each step of the model. This asymptote could be used to distinguish the entropy attributable to the species distribution from that attributable to model misspecification. We discussed the use of fuzzy entropy for this end because it produces results that are commensurable between species and study areas. Using a stepwise approach and fuzzy entropy may be helpful to counterbalance the uncertainty and the complexity of the models. The model yielded at the step with the lowest fuzzy entropy combines the reduction of uncertainty with parsimony, which results in high efficiency.     

基于电阻抗层析成像的高强度聚焦超声温度监测技术

作为一种对正常组织无损伤且不易引起癌细胞转移的非入侵肿瘤治疗手段,高强度聚焦超声(HIFU)治疗过程中焦域的温度监测是实现剂量精准控制的关键.本文基于生物组织的温度-电阻抗的关系,将电阻抗层析成像(EIT)和HIFU治疗相结合,提出了一种利用组织焦平面的表面电压实现电阻抗重构的检测技术.建立了HIFU治疗和EIT综合系统模型,在考虑组织的声吸收条件下,对三维Helmholtz方程在柱坐标下的声场计算进行了二维简化,并引入Pennes生物热传导方程来计算HIFU焦域的声压和温升分布特性;引入生物组织的温度-电阻抗关系,基于麦克斯韦电磁场理论,建立了具有温度分布HIFU焦域的电流和电压计算模型,利用恒流注入的边界条件实现电场计算,获得焦平面的表面电压分布.在数值计算中,利用实验聚焦换能器参数,模拟了在固定声功率下组织焦域的声场和温度场分布,以及中心和偏心聚焦条件下不同治疗时刻的电导率分布;然后通过对称电极的循环电流注入,计算了组织模型焦平面内的电流密度和电势分布,获得了焦平面圆周分布的表面电极电压;进一步采用修正的牛顿-拉夫逊算法,利用32×32的表面电极电压实现了焦平面内电导率分布的重建.结果表明,基于温度-电阻抗关系的EIT电导率重建技术不但能准确定位HIFU焦域中心,还能恢复HIFU治疗中焦域的温度分布,证明了EIT用于HIFU治疗中温度监测的可行性,为其疗效评估和剂量控制提供了一种无创电阻抗测量和成像新方法.     

基于主元分析与动态时间弯曲的故障诊断方法及应用研究

轴承是工程实际中常用而又极易损坏的部件,特别是对其早期微弱响应的辨识,具有重要的社会价值和意义。为提高运转轴承的安全可靠性和可维护性,提出了基于主元分析与动态时间弯曲距离的故障诊断方法,它可以准确对早期微弱动态响应辨识、诊断。该方法首先将典型故障样本信号与待测信号小波去噪并EMD分解,并对若干固有模态分量主元分析求取主元,然后对主元分量进行分析,获得相关特征值组成特征向量,计算待测信号与已知故障样本信号特征向量的弯曲距离,弯曲距离越小表明两信号越相似,从而辨识故障。此外,还可将其应用于转子、碰磨、齿轮故障诊断中,工程应用实例表明该方法可以准确故障分类,高效故障诊断。     

On the possibility of using the maximum entropy method in ultrasonic nondestructive testing for scatterer visualization from a set of echo signals

We have studied the possibility of solving the inverse scattering problem in the Born approximation, i.e., the reconstruction of scatterer images from the measured set of echo signals. We have considered generalization of the classical combined SAFT (C-SAFT) algorithm to the case of multiple reflections from uneven boundaries of the tested object taking into account the transformation of the wave type for several positions of the antenna grid, which makes it possible to obtain high-quality scatterer images. Representation of the direct problem in matrix form makes it possible to switch to solving the inverse problem, which can be solved using the Tikhonov regularization procedure, because it is an ill-posed. We have considered the possibility of using the entropy of the image estimate as the stabilizing functional that forms the essence of the maximum entropy method (MEM). The advantage of the MEM over the conventionally used linear C-SAFT method has been shown. The ray model taking into account reflections of rays from the boundaries of the tested object with uneven boundaries has been used for constructing the function estimate. We have demonstrated the ability of the MEM to obtain the scatterer images with superresolution and to suppress the “side lobes” of the function of the point scattering on the collapsed set of echo signals. The use of echo signals reflected from the boundaries of the tested object makes it possible to reconstruct the scatterer shape more exactly. Examples of images reconstructed by the MEM on echo signals obtained in the numerical and model experiments have been presented.     

基于超声射频信号的支持向量机双参量B线识别方法*

肺超声中的特殊征象B线对于临床诊断肺水肿等肺部疾病有重要意义,但诊断结果依赖于医生的主观判断,为了客观、自动地识别B线,提高诊断准确率,本文提出了一种基于超声回波射频信号的肺脏超声特殊征象B线识别方法。本文首先选取了射频信号的排列熵、信息熵、峰度、偏度、能量作为特征参数,利用独立样本t检验和单参数贝叶斯分类的方法检验超声射频数据中B线以及非B线所对应射频数据的各个参量的差异性以及各参数与B线识别的相关性。然后将不同的双参量组合输入非线性支持向量机（SVM）中进行分类,比较各个组合的分类效果。结果显示信息熵与排列熵参数组合基于射频信号的分类效果最好,分类灵敏度为90.521%,特异性为98.106%,准确率为96.328%,AUC等于0.95。在引入后处理算法后,B线识别效果有进一步提升,得到分类平均灵敏度为95.23%,平均特异性为97.22%,平均准确率为96.88%。研究结果表明基于射频数据的SVM双参量B线识别方法对辅助临床诊断具有重要价值,信息熵和排列熵的组合可以有效的对特殊征象B线进行高精度识别。