癫痫脑电信号的奇异性研究

基于小波变换模极大值在多尺度上的变化,研究了癫痫脑电的奇异性,并用Lipschitz指数来表征.提出了一种高阶统计的方法来研究癫痫脑电的高阶奇异谱特征,并和健康脑电进行比较.实验结果表明,癫痫脑电的Lipschitz指数和高阶奇异谱与健康脑电相比存在明显的差异,说明该方法对研究脑电是有效的.     

Pathological analysis of myocardial cell under ventricular tachycardia and fibrillation based on symbolic dynamics

Ventricular fibrillation (VF) is one of the most serious malignant arrhythmias usually resulting from immediate degeneration of ventricular tachycardia (VT). In order to analyse the nonlinear dynamics of the cardiac micro-mechanism under VT and VF rhythm, at the cellular level, myocardial cell action potentials are investigated under different rhythm, normal sinus rhythm, VT and VF. On the basis of nonlinear chaotic theory and symbolic dynamics, we put forward new definitions, complexity rate, etc, and obtained some useful properties for cellular electrophysiological analysis. The results of the experiments and computation show that the myocardial cellular signals under VT and VF rhythm are different kinds of chaotic signals in that the cardiac chaos attractor under VF is higher than that under VT. The analytical complexity theory has been promising in the clinical application.     

Pathological analysis of myocardial cell under ventricular tachycardia and fibrillation based on symbolic dynamics.

Ventricular fibrillation (VF) is one of the most serious malignant arrhythmias usually resulting from immediate degeneration of ventricular tachycardia (VT). In order to analyse the nonlinear dynamics of the cardiac micro-mechanism under VT and VT rhythm, at the cellular level, myocardial cell action potentials are investigated under different rhythm, normal sinus rhythm, VT and VT. On the basis of nonlinear chaotic theory and symbolic dynamics, we put forward new definitions, complexity rate, etc, and obtained some useful properties for cellular electrophysiological analysis. The results of the experiments and computation show that the myocardial cellular signals under VT and VF rhythm are different kinds of chaotic signals in that the cardiac chaos attractor under VF is higher than that under VT. The analytical complexity theory has been promising in the clinical application.     

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.     

脑电棘波识别和噪声消除的小波变换方法

研究了利用二进小波变的的模极大值识别脑电信号奇异点如棘波和消除噪声的方法，该方法在较好保留原脑电信号奇异信息的同时能有效地消除噪声，进一步讨论了信号与白噪声的奇异性指数的区别，以及小波变换模极大值沿各变换尺度传递的不同特性，并利用该特性区分信号中的奇异点和噪声，能准确识别奇异点的位置，这种奇异性识别技术在信号的特征提取和消除噪声方面有广阔的应用前景。     

室性心动过速与室颤的多尺度多重分形分析

基于多尺度多重分形分析(MSMF)对正常心电信号(ECG)、室性心动过速(ventriculartachycard ia,VT)与室颤(ventricular fibrillation,VF)ECG进行研究分析,可以鉴别区分这三种信号。通过直接计算奇异谱f(α)的方法,计算出正常ECG、VT和VF信号的奇异谱,并分析得出其奇异强度分布范围Δα,结果发现这三种信号奇异强度分布范围Δα存在较大差异,并且VF信号的Δα大于VT信号,同时VT信号的Δα又大于正常ECG信号。本文用奇异强度分布范围Δα作为区分正常、VT和VF、ECG的一个判据,且实验证实该判据可有效区分正常ECG、VT和VF信号。     

Dynamic analysis of inter-words time intervals: a method to analyze the structure of communicative signals

Speech analyses are usually focused on words as signifiers ignoring inter-words time intervals (IWIs), which are related to the 'form' of speech, rather than to its 'content'. Applying the method of power spectrum analysis to inter-vocalizations time intervals of bird singing, underlying periodic processes were detected. In contrast, human IWIs revealed non-periodicity, which may be random or chaotic. To differentiate between these two possibilities, the non-linear dynamic methods of unstable periodic orbits and correlation dimension were applied to show that IWIs are characterized by a low dimensional chaotic attractor. Its correlation dimension of 3.2 +/- 1.1 suggests a minimum number of four variables underlying the system. The methods developed in the present communication can be further applied: (a) for the measurement of specific alterations in the processes underlying the form of speech in human disorders, i.e., schizophrenia, (b) for the assessment of normal and pathological developmental aspects of speech processes in children; (c) for comparing communicative signals between humans and other species.     

高可靠度心率变异性信号获取的实验研究

心率变异性信号的获取在生理研究和临床诊断中都有着重要的应用价值。为了保证心率变异性分析的准确性，必须考虑心率变异性的获取方法。本文利用信号奇异点及其小波变换的关系，设计了HRV信号的R波获取软件。对MIT／BIH心电数据库中的37个记录文件进行R波的检测实验，检测实验效果令人满意。     

基于独立分量分析算法研究儿童癫痫脑电的混沌动力学特征

首先采用独立分量分析(Independent component analysis,ICA)算法,将儿童癫痫信号从复杂的背景脑电(Electroencephalogram,EEG)中分离出来;然后采用了一维时间序列相空间重构技术和混沌的定量判据,对分离出来的独立分量信号进行了分析与计算.通过对生理和癫痫状态下独立分量信号的相图、功率谱、关联维数和Lyapunov指数的对比研究,得出如下结论:(1)EEG独立分量的相图、功率谱、关联维数和Lyapunov指数反映了大脑的总体动态特征,它们可作为一种定量指标衡量大脑的健康状态;(2)在正常的生理状态下EEG是混沌的,而在癫痫状态下则趋于有序。     

基于局部非线性投影滤波算法的ECG信号处理

含有一定强度干扰的混沌系统可以通过局部非线性投影的方法逼近其吸引子。单导心电信号（ECG）在时间延迟嵌入空间中表现出一定的几何结构，包含其它成分的ECG可以在一个较高维的空间得到没有交叠的状态轨迹。利用局部非线性投影法可以逼近ECG的一个低维吸引子，从而将ECG和其它成分分离。     

The Spatial Equivalence Between Wavelet Decomposition and Phase Space Embedding of EEG

Using both the wavelet decomposition and the phase space embedding, the phase trajectories of electroencephalogram (EEG) is described. It is illustrated based on the present work,that is,the wavelet decomposition of EEG is essentially a projection of EEG chaotic attractor onto the wavelet space opened by wavelet filter vectors, which is in correspondence with the phase space embedding of the same EEG. In other words, wavelet decomposition and phase space embedding are equivalent in methodology. Our experimental results show that in both the wavelet space and the embedded space the structure of phase trajectory of EEG is similar to each other. These results demonstrate that wavelet decomposition is effective on characterizing EEG time series.     

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。     

基于独立分量分析的生理信号盲源分离

用于盲源分离的独立分量分析 (ICA)和扩展ICA算法 ,基于极大似然估计 ,给出一个衡量输出分量统计独立的目标函数 ,最优化该目标函数 ,得到一种用于独立分量分析的迭代算法。扩展ICA算法的优点在于迭代过程中不需要计算信号的高阶统计量 ,收敛速度快 ,同时适用于超高斯和亚高斯信号的分离。应用该算法实现了脑电、心电信号以及语音信号的分离 ,并给出了实验结果     

Real time continuous wavelet transform implementation on a DSP processor

The continuous wavelet transform (CWT) is an effective tool when the emphasis is on the analysis of non-stationary signals and on localization and characterization of singularities in signals. We have used the B-spline based CWT, the Lipschitz Exponent (LE) and measures derived from it to detect and quantify the singularity characteristics of biomedical signals. In this article, a real-time implementation of a B-spline based CWT on a digital signal processor is presented, with the aim of providing quantitative information about the signal to a clinician as it is being recorded. A recursive algorithm implementation was shown to be too slow for real-time implementation so a parallel algorithm was considered. The use of a parallel algorithm involves redundancy in calculations at the boundary points. An optimization of numerical computation to remove redundancy in calculation was carried out. A formula has been derived to give an exact operation count for any integer scale m and any B-spline of order n (for the case where n is odd) to calculate the CWT for both the original and the optimized parallel methods. Experimental results show that the optimized method is 20–28% faster than the original method. As an example of applying this optimized method, a real-time implementation of the CWT with LE postprocessing has been achieved for an EMG Interference Pattern signal sampled at 50 kHz.     

Can the analytic techniques of nonlinear dynamics distinguish periodic, random and chaotic signals?

Recent advances in the mathematical discipline of nonlinear dynamics have led to its use in the analysis of many biologic processes. But the ability of the tools of nonlinear dynamic analysis to identify chaotic behavior has not been determined. We analyzed a series of signals--periodic, chaotic and random--with five tools of nonlinear dynamics. Periodic signals were sine, square, triangular, sawtooth, modulated sine waves and quasiperiodic, generated at multiple amplitudes and frequencies. Chaotic signals were generated by solving sets of nonlinear equations including the logistic map, Duffing's equation, Lorenz equations and the Silnikov attractor. Random signals were both discontinuous and continuous. Gaussian noise was added to some signals at magnitudes of 1, 2, 5, 10 and 20% of the signal's amplitude. Each signal was then subjected to tools of nonlinear dynamics (phase plane plot, return map, Poincaré section, correlation dimension and spectral analysis) to determine the relative ability of each to characterize the underlying system as periodic, chaotic or random. In the absence of noise, phase plane plots and return maps were the most sensitive detectors of chaotic and periodic processes. Spectral analysis could determine if a process was periodic or quasiperiodic, but could not distinguish between chaotic and random signals. Correlation dimension was useful to determine the overall complexity of a signal, but could not be used in isolation to identify a chaotic process. Noise at any level effaced the structure of the phase plane plot. Return maps were relatively immune to noise at levels of up to 5%. Spectral analysis and correlation dimension were insensitive to noise. Accordingly, we recommend that unknown signals be subjected to all of the techniques to increase the accuracy of identification of the underlying process. Based on these data, we conclude that no single test is sufficiently sensitive or specific to categorize an unknown signal as chaotic.     

A chaos-based visual encryption mechanism for clinical EEG signals

In this study, we have developed a chaos-based visual encryption mechanism that can be applied for clinical electroencephalography (EEG) signals. In comparison with other types of random sequences, chaos sequences were mainly used to increase unpredictability. We used a 1D chaotic scrambler and a permutation scheme to achieve EEG visual encryption. One approach of realizing the visual encryption mechanism is to scramble the signal values of the input EEG signal by multiplying a 1D chaotic signal to randomize the EEG signal values. We then applied a chaotic address scanning order encryption to the randomized reference values. Simulation results show that when the correct deciphering parameters are entered, the signal is completely recovered, and the percent root-mean-square difference (PRD) values for control and alcoholic clinical EEG signals are 4.33 × 10⁻¹⁵ and 4.11 × 10⁻¹⁵%, respectively. As long as there is an input parameter error, with an initial point error of 0.00000001% as an example, thereby making these clinical EEG signals unrecoverable.     

Chaotic behavior of the coronary circulation

The regulation of the coronary circulation is a complex paradigm in which many inputs that influence vasomotor tone have to be integrated to provide the coronary vasomotor adjustments to cardiac metabolism and to perfusion pressure. We hypothesized that the integration of many disparate signals that influence membrane potential of smooth muscle cells, calcium sensitivity of contractile filaments, receptor trafficking result in complex non-linear characteristics of coronary vasomotion. To test this hypothesis, we measured an index of vasomotion, flowmotion, the periodic fluctuations of flow that reflect dynamic changes in resistances in the microcirculation. Flowmotion was continuously measured in periods ranging from 15 to 40 min under baseline conditions, during antagonism of NO synthesis, and during combined purinergic and NOS antagonism in the beating heart of anesthetized open-chest dogs. Flowmotion was measured in arterioles ranging from 80 to 135 μm in diameter. The signals from the flowmotion measurements were used to derive quantitative indices of non-linear behavior: power spectra, chaotic attractors, correlation dimensions, and the sum of the Lyapunov exponents (Kolmogorov–Sinai entropy), which reflects the total chaos and unpredictability of flowmotion. Under basal conditions, the coronary circulation demonstrated chaotic non-linear behavior with a power spectra showing three principal frequencies in flowmotion. Blockade of nitric oxide synthase or antagonism of purinergic receptors did not affect the correlation dimensions, but significantly increased the Kolmogorov–Sinai entropy, altered the power spectra of flowmotion, and changed the nature of the chaotic attractor. These changes are consistent with the view that certain endogenous controls, nitric oxide and various purines (AMP, ADP, ATP, adenosine) make the coronary circulation more predictable, and that blockade of these controls makes the control of flow less predictable and more chaotic. Supported by NIH grant HL32788.     

心电QRS积分信号的非线性分析

QRS波群积分能反映心室去极化活动,为不少心电研究者所重视。本文采用了非线性的分析方法,在非线性去噪的基础上重构此积分信号的相空间,计算了吸引子的相关维数与最大L yapunov指数值,分析了QRS波群积分的混沌特征。利用MIT- BIH心电数据库数据分析了多种情况下QRS波群积分的非线性指标变化特点,并同HRV信号非线性分析相比较,发现在某些情况下对QRS波群积分序列分析更具有优势。结论是利用QRS波群积分信号的非线性指标可对心脏功能做出评价。     

A hybrid ECG compression algorithm based on singular value decomposition and discrete wavelet transform

Increasing use of computerized ECG processing systems requires effective electrocardiogram (ECG) data compression techniques which aim to enlarge storage capacity and improve data transmission over phone and internet lines. This paper presents a compression technique for ECG signals using the singular value decomposition (SVD) combined with discrete wavelet transform (DWT). The central idea is to transform the ECG signal to a rectangular matrix, compute the SVD, and then discard small singular values of the matrix. The resulting compressed matrix is wavelet transformed, thresholded and coded to increase the compression ratio. The number of singular values and the threshold level adopted are based on the percentage root mean square difference (PRD) and the compression ratio required. The technique has been tested on ECG signals obtained from MIT-BIH arrhythmia database. The results showed that data reduction with high signal fidelity can thus be achieved with average data compression ratio of 25.2:1 and average PRD of 3.14. Comparison between the obtained results and recently published results show that the proposed technique gives better performance.