心脑疾病对同步十二导联心电图多重分形谱的影响

对心脑疾病人群的同步十二导联ECG(心电图)进行多重分形特性分析,发现不同导联的多重分形曲线互不重叠。计算其十二导联平均的多重分形奇异强度分布范围以及分布范围的十二个导联间的离散特性,发现不同人群中存在互为交叉而有明显不同的结果。用十二导联多重分形Δα的平均值Δα及其离散度δα(取Δα的标准差)两个参量来描述其多重分形谱特征。发现健康人与心脏病人Δα接近,但δα相差较大;健康人与脑损伤患者δα接近,但Δα相差较大。预示着多重分形特性受到神经自律和心脏组织结构的自谐特性的双重控制,特征参数Δα与神经控制相对应,δΔ与心脏组织结构自谐特性的各向异性相对应。     

小波多重分形在脑电信号分析中的应用

脑电图(EEG)作为客观反映大脑机能状态的一个重要方面,蕴涵着丰富的生理、心理和病理信息.分形和小波相结合的方法,发挥了小波多尺度分析和分形标度不变性的两个特点.利用小波模极大值多重分形的方法,对青老年人群的脑电图进行分析.研究发现,脑电信号具有奇异性质,脑电信号奇异谱的跨度会随着年龄的增大而减小,可以用来检测目标个体是否出现腩衰退迹象,从而辅助进一步的临床干预措施.     

基于多重分形去趋势涨落的心室纤颤和心动过速分析

本文运用多重分形去趋势涨落的分析方法,研究心动过速、心室纤颤和正常心电信号的多重分形特征,用以有效区分上述三种信号。通过分析心动过速、心室纤颤和正常心电信号的赫斯特指数、Renyi指数和多重分形谱,得出三种信号都具有不同程度的长程相关性和多重分形特性,在波动函数的阶数大于0时,三种信号的长程相关特性区别明显。通过分析多重分形谱,发现心室纤颤的多重分形谱比心动过速的多重分形谱宽,正常心电信号的多重分形谱最小。以上研究结果将对临床医学诊断识别心动过速和心室纤颤号信号有很好的借鉴意义。     

早搏信号的小波模极大多重分形分析

本文运用基于小波模极大值的多重分形分析方法,研究心脏房性早搏(APB)信号、室性早搏(PVC)信号及正常心电(ECG)信号的多重分形特征。通过分析多重分形谱得出:三种信号都具有不同程度的多重分形特性;正常ECG信号的分形程度最强,PVC信号次之,APB信号最弱。t检验结果表明,此方法得出的三种信号分形谱宽度差异具有显著性,对临床医学诊断区分APB、PVC信号有很好的借鉴意义。     

心律失常的多重分形去趋势波动分析

目的心律失常的及早诊断对及时救助病人具有重要意义.方法基于多重分形去趋势波动分析方法,本文对正常心电信号及窦、房性心律失常信号进行分析.结果发现三种信号都具有不同程度的长程相关性和多重分形特性,且在波动函数的阶数为正值时,三种信号的长程相关特性最为明显.通过比较三种信号的多重分形谱,发现正常心电信号的多重分形谱宽度最小,窦性心动过缓信号次之,心房颤动宽度最大.结论此研究结果对临床医学诊断区分心律失常与正常心电信号有很好的借鉴意义.     

血膜中白细胞分布的分形特征刻划

把分形引入血膜中中性粒细胞分布的研究,发现血膜中中性粒细胞分布为多重分形,建立了中性粒细胞多重分形的f(α）－α谱,探索了分形在临床医学和临床检验中的应用。     

结肠道压力数据的多重分形分析及熵解释

据统计结肠测压是目前应用范围最广的评价结肠道动力系统功能的检查手段,传统的方式是提取结肠压力的时域信号,根据专家和医生的判断来做出结论,但是这种方式人为的因素较多,往往不够准确.本研究对得到的结肠道压力数据进行多重分形特性的分析,通过计算多重分形谱来分析正常受试者和异常受试者结肠压力数据,其中正常受试者3例,异常受试者9例,对得到的多重分形谱利用熵理论进行分析,可以区分受试者数据的正常与异常情况.结果表明该方法在一定程度上面反映了正常的和异常的结肠压力信号的差异性,结合理论的分析可以作为判断结肠道动力性能的辅助手段.     

基于多重分形的医学图像分割方法

背景:由于人体解剖结构的复杂性、组织器官形状的不规则性及不同个体间的差异性,所以比较适合用多重分形来分析。目的:采用多重分形理论对医学图像进行图像分割。方法:采用基于容量测度的多重分形谱计算及基于概率测度的多重分形谱计算方法对图像进行分割。对于待处理图片分别进行传统的区域生长分割,max容量测度图像分割,sum容量测度图像分割,概率测度图像分割等4种分割,并加入噪声后再进行同样的分割处理作为比较。结果与结论:采用的两种基于多重分形谱的计算法中,基于容量测量的多重分形谱计算方法的关键是定义合适的测度μα;基于概率测度的多重分形谱计算方法的关键是定义合适的归一化概率Pi,不同的测度(概率)和不同的阈值对结果的影像比较大。基于概率测度的方法对噪声比较敏感,但是在滤过噪声时对图像象素大小变化比较大、比较复杂的图像有较好的分割效果。实验表明基于多重分形谱的医学图像分割方法在选择合适的测度(概率)和阈值时是可行的,特别是在较为复杂的图像处理中对于纹理和边缘的区别上有较大的优势,在准确地分割的同时能保留更多的细节,具有重要的实际意义。同时,多重分形也可以作为一种图像的特征,为特征提取多提供一种有力的数据。     

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。     

基于多重分形去趋势波动分析的脑电信号特征提取及分类方法

目的:针对脑电信号普遍存在的数据维度高、难以预测的问题,提出一种多重分形去趋势波动分析特征提取方法与长短时记忆网络（LSTM）相结合的脑电信号分类方法。方法:首先对信号样本进行多重分形去趋势波动分析计算得到脑电信号样本的多重分形谱,计算广义Hurst指数hq和广义维数Dq之间的函数关系;然后对多重分形谱进行分析,找出最具代表性的坐标值作为信号的特征向量;最后将其用于LSTM进行训练和分类测试。实验采用波恩大学采集的经过处理的癫痫脑电数据集。结果:当训练样本占总体样本比例超过10%之后,LSTM分类器的测试准确率均稳定在98%以上;当占比超过80%时LSTM分类器的测试准确率达到了100%;即使训练样本较少时也有95%之上的准确率。结论:该算法有良好的准确率和稳定性。     

Self-affine fractal variability of human heartbeat interval dynamics in health and disease

The complexity of the cardiac rhythm is demonstrated to exhibit self-affine multifractal variability. The dynamics of heartbeat interval time series was analyzed by application of the multifractal formalism based on the Cramèr theory of large deviations. The continuous multifractal large deviation spectrum uncovers the nonlinear fractal properties in the dynamics of heart rate and presents a useful diagnostic framework for discrimination and classification of patients with cardiac disease, e.g., congestive heart failure. The characteristic multifractal pattern in heart transplant recipients or chronic heart disease highlights the importance of neuroautonomic control mechanisms regulating the fractal dynamics of the cardiac rhythm.Dedicated to Paolo Cerretelli on the occasion of his 70th birthday anniversary.     

Wavelet-based multifractal analysis of human balance

Center of pressure (COP) traces have been used to investigate the dynamics of human balance. In this paper we employ a wavelet-based multifractal methodology to identify structural differences in mediolateral and anterioposterior sway between COP traces of healthy and Parkinsons patients. Two statistical techniques are used to summarize the differences in multifractal spectrum (MFS) for both groups. The first technique is a multivariate repeated measures analysis on estimated MF spectra for subjects. The second technique obtains two characteristic measures from each subjects estimated MFS: (i) location and (ii) half-width of the spectrum. These measures present an intuitive summary of the MFS for each subject, allowing for statistical comparisons between the two groups. Both analyzes lead to significant discrimination between Parkinson versus healthy subjects MFS. We find that COP time series of Parkinson patients exhibit a greater degree of roughness as compared to healthy subjects COP traces. Furthermore, MFS for Parkinson patients are narrower, suggesting a reduction in complexity as compared to the healthy group. The methodology presented here may be helpful in development of clinically relevant measures, including the assessment of severity of conditions as the measures developed here correlate with standard severity measures. © 2002 Biomedical Engineering Society. PAC2002: 8719Bb, 8719St, 0250Sk, 8719Xx, 0545Df, 0230Uu     

Classification of surface electromyographic signals by means of multifractal singularity spectrum

In order to effectively control a prosthetic system, considerable attempts have been made in recent years to improve the classification accuracy of surface electromyographic (SEMG) signals. However, the extraction of effective features is still a primary challenge for the classification of SEMG signals. This study tried to solve the problem by applying the multifractal analysis. It was found that the SEMG signals were characterized by multifractality during forearm movements and different types of forearm movements were related to different multifractal singularity spectra. To quantitatively evaluate the multifractal singularity spectra of the SEMG signals, the areas of the singularity spectrum curves were calculated by integrating the spectrum curves with respect to the singularity strengths. Our results showed that there were several separate clusters resulting from singularity spectrum areas of different forearm movements when two channels of SEMG signals were used in this experimental research, which demonstrated that the multifractal analysis approach was suitable for identifying different types of forearm movements. By comparing with other feature extraction techniques, the multifractal singularity spectrum approach provided higher classification accuracy in terms of the classification of SEMG signals.     

Local fuzzy fractal dimension and its application in medical image processing

The local fuzzy fractal dimension (LFFD) is proposed to extract local fractal feature of medical images. The definition of LFFD is an extension of the pixel-covering method by incorporating the fuzzy set. Multi-feature edge detection is implemented with the LFFD and the Sobel operator. The LFFD can also serve as a characteristic of motion in medical image sequences. The experimental results show that the LFFD is an important feature of edge areas in medical images and can provide information for segmentation of echocardiogram image sequences.     

Model-based technique for the measurement of skin thickness in mammography

A model-based method is proposed for the measurement of breast skin thickness from digitised mammograms that takes into account both the geometric and radiographic properties of the skin region. The method initially identifies a salient feature that discriminates the skin from the other anatomical structures of the breast. Its identification is based on a multi-scale grey-level gradient estimation, using a wavelet decomposition of the image. The spatial distribution of this feature is organised as a graph, with each of its nodes associated with a binary set of interpretation labels. A Markov randomfield is defined on the set of labels, and the best graph labelling is finally determined with a maximum a posteriori (MAP) probability criterion. The method was applied on 11 mammograms with improved contrast characteristics at the breast periphery, obtained by an exposure equalisation technique during image acquisition. The validation of the approach was performed by calculating the root mean square (RMS) error between the detected skin thickness and manual measurements performed on each of the films. The resulting error values ranged from 0.1 mm to 0.2 mm for normal cases and reached a maximum of 0.5 mm in pathological cases with advanced skin thickening.     

Fractal correlation in human H-reflex

The amplitude of the H-reflex has been known to have considerable variability even if the intensity of the stimulation is held constant. However, previous studies largely ignored the time-dependent profile of this variability. Recent mathematical analyses have shown that some seemingly irregular biological signals have fractal properties. A fractal time series is characterized by the property of self-similarity (self-affinity), and has long-range time correlation. The aim of this study was to investigate the question of whether the fluctuation of H-reflex was fractal with strong time-correlation. Soleus H-reflexes were evoked in five healthy subjects at two levels of stimulation intensity [1.2MT (motor threshold) and 0.9 MT] every 1 s and 1050 successive amplitudes of H-wave and M-wave were recorded twice. The sequences of the H-wave and the M-wave amplitudes were analyzed by coarse graining spectral analysis to calculate the percentage of random fractal components in the sequences (%Fractal) and the spectral exponent . The %Fractal values of both sequences were above 90% [H-wave: 93.3±2.3% (1.2 MT), 91.6±3.1% (0.9 MT); M-wave: 94.3±3.3%; mean±SD]. Nonflat power spectra of the fractal components were observed for the H-wave sequences regardless of the stimulation intensity [=0.75±0.26 (1.2 MT), 0.80±0.39 (0.9 MT)], indicating that the sequences were strongly time correlated. On the other hand, the M-wave sequences had a flatter spectrum (=0.26±0.14) which was close to uncorrelated white noise. These results indicated that: (1) the fractal correlation found in the H-wave sequences was caused neither by the conduction through nerve fibers nor by the transmission at the neuromuscular junction, because the M-wave sequence had a significantly weaker time correlation, and (2) antidromic impulses in a motor nerve induced by the stimulation made a minor contribution to the generation of fractal correlation in the H-wave sequences, because it was preserved when the stimulation intensity was below MT. It was suggested that the fractal correlation in human H-reflex was generated at the synaptic connections to -motoneurons in the spinal cord.