The base-scale entropy analysis of short-term heart rate variability signal

The complexity of heart rate variability (HRV) signal can reflect physiological functions and healthy status of heart system. Detecting complexity of the short-term HRV signal has an important practical meaning. We introduce the base-scale entropy method to analyze the complexity of time series. The advantages of our method are its simplicity, extremely fast calculation for very short data and anti-noise characteristic. For the well-known chaotic dynamical system- logistic map, it is shown that our complexity behaves similarly to Lyapunov exponents, and is especially effective in the presence of random Gaussian noise. This paper addresses the use of base-scale entropy method to 3 low-dimensional nonlinear deterministic systems. At last, we apply this idea to short-term HRV signal, and the result shows the method could robustly identify patterns generated from healthy and pathologic states, as well as aging. The base-scale entropy can provide convenience in practically applications.     

甲亢患者心率变异信号的频域分析

采用心电信号采集系统，将心电图机的模拟信号放大滤波后送入计算机，利用现代谱分析法分析了正常人和甲亢患者心率变异信号的VLF／HF值。观察结果表明：甲亢患者和正常人的VLF／HF值有明显的差异，说明谱分析方法是分析甲亢患者自主神经异常的有效参数手段之一。     

Sign series entropy analysis of short-term heart rate variability

Complexity and nonlinearity approaches can be used to study the temporal and structural order in heart rate variability （HRV） signal, which is helpful for understanding the underlying rule and physiological essence of cardiovascular regulation. For clinical applications, methods suitable for short-term HRV analysis are more valuable. In this paper, sign series entropy analysis （SSEA） is proposed to characterize the feature of direction variation of HRV. The results show that SSEA method can detect sensitively physiological and pathological changes from short-term HRV signals, and the method also shows its robustness to nonstationarity and noise. Thus, it is suggested as an efficient way for the analysis of clinical HRV and other complex physiological signals.     

Nonlinear short-term heart rate variability prediction of spontaneous ventricular tachyarrhythmia

As malign ventricular tachyarrhythmias triggering sudden cardiac death （SCD）, both ventricular tachycardla （VT） and ventricular fibrillation （VF） are major causes of mortality. The most efficient therapy for SCD prevention is implantable cardioverter defibrillators （ICD）. The ICD can accurately and effectively Identify the forthcoming of fatal ventricular tachyarrhythmlas and deliver a shock in order to restore patients＇ normal sinus rhythm. In this study, two nonlinear complexity measures based on entropy： approximate entropy （ApEn） and sample entropy （SampEn） as well as two time linear indices： the mean RR interval （the average of time intervals between consecutive R-waves） and the standard deviation of RR Intervals were used for short-term forecasting of VT-VF occurrence. The last small sections of interbeat Intervals preceding 135 VT-VF episodes from 78 patients stored by the ICD were analyzed and compared with individually acquired control time series （CON series） from the same patients, which are normally intrinsic sinus rhythms. The results demonstrate that in addition to an obvious increase in heart rates of the patients, the values of two entropy measures are significantly smaller for VT-VF episodes than those for CON series. Conclusions can be drawn that when a ventricular tachyarrhythmia approaches, the sympathetic tone of the patients is increased, and the complexity of their RR intervals immediately before the onset of VT-VF events is obviously lower than that of RR intervals recorded during sinus rhythms. For a better separation, the optimal range of threshold r is determined for two algorithms. ApEn and SampEn measures might be the suitable nonlinear parameters for shodterm prediction of life-threatening ventricular tachyarrhythmias in the application of the cardioversion and defibrillation.     

病态胎儿心率信号的替代数据分析

利用基于关联维数的振幅调节傅里叶变换的替代数据法对死前和羊水少两组胎儿心率（Fetal beart rate，即FHR）时间序列进行了分析，给出了对于胎儿心率时间序列进行非线性判定和混沌判定方法，计算结果表明两组病态胎儿心率时间序列的混沌判据S〈2，说明病态信号不具有混沌特性；但死前胎儿心率信号非线性判据t〉ta.f，说明死前信号具有非线性，而羊水少胎儿心率信号非线性判据t〈ta.f，说明羊水少信号不具有非线性。     

病态胎儿心率信号的替代数据分析

利用基于关联维数的振幅调节傅里叶变换的替代数据法对死前和羊水少两组胎儿心率(Fe-tal heart rate,即FHR)时间序列进行了分析,给出了对于胎儿心率时间序列进行非线性判定和混沌判定方法,计算结果表明两组病态胎儿心率时间序列的混沌判据S<2,说明病态信号不具有混沌特性;但死前胎儿心率信号非线性判据t>ta,f,说明死前信号具有非线性,而羊水少胎儿心率信号非线性判据t相似文献   

正态分布区间数的包含度和可能度及其应用

文章针对正态分布区间数的多属性决策问题,提出了一种基于包含度和可能度的决策方法。介绍了正态分布区间数的基本概念和运算法则,定义了它的包含度和可能度,并证明了其相关性质;提取正理想对象并结合包含度给出了属性权重的计算公式,同时提出了正态分布区间数的集结方法;利用可能度构造可能度矩阵,利用排序向量法得到最优决策,并通过实例分析验证了该决策方法的可行性和有效性。     

关于ARMAV（n,m）模型的建模

在一维长自回归模型比较参数法建模的基础上,根据相同观测数据所对应的适用ARV(p)模型与ARMAV(n,m)模型两者传递函数相等的原理,给出了ARMAV(n,m)模型的建模方法。     

具有高非线性度和最优代数次数的弹性函数的构造

具有良好的非线性度和最优代数次数的弹性布尔函数在流密码和分组密码设计和分析中起着至关重要的作用.本文通过修改Maiorana-McFarland(M-M)类Bent函数,利用不同的低阶弹性函数,给出构造高非线性度弹性布尔函数的一种新方法,所构造的函数具有严格几乎最优的非线性度和最优的代数次数.     

广义模糊时间序列模型模糊区间划分研究

在模糊时间序列模型的构架中,介绍了广义模糊时间序列模型建立过程和常用的模糊区间划分方法,提出了基于均匀划分、模糊C均值聚类和自动聚类3种模糊区间划分方法的广义模糊时间序列模型,并用Alabama大学入学人数和沪市股指两组数据对模型进行了详细的分析.实验结果不仅揭示了这3种方法对模型预测结果的影响,还证明了广义模型优于传统模型.     

MA模型参数求解与幅度谱重构信号问题关系

幅度谱重构信号问题已取得了许多成果,而MA参数模型的求解却比较困难．通过推导．建立了MA模型参数求解与幅度谱重构信号问题之间的关系,从而把这两个问题联系起来．这样,所有幅度谱重构信号问题的理论与算法均可应用于MA模型参数求解当中．     

时间序列分析方法及人民币汇率预测的应用研究

在简要介绍时间序列模型的基础上,使用人民币／美元的日汇率值进行实证研究,建立相应的ARIMA模型和EGARCH模型并进行预测和评价。研究结果表明,EGARCH模型的预测结果较ARIMA模型理想,适合描述人民币／美元汇率的变动趋势。     

基于PMSM扩展PI参考模型的VSI非线性补偿

针对永磁同步电动机(PMSM)中电压源逆变器(VSI)非线性增益会引起输出电压畸变所导致的电流畸变问题,提出了一种新颖的SVPWM-VSI输出电压非线性补偿策略.该方法利用d,q轴PMSM扩展PI参考模型观测器将未知扰动电压分离,再设计非线性补偿器对VSI进行补偿.通过适当的配置PI参数可基本消除因补偿器动态滞后所造成的短暂零电流钳位现象.仿真结果表明,所提方法能够有效补偿VSI非线性输出增益,具有很好的参数鲁棒性,显著降低了电机电流畸变率.     

General Characteristic Equations of Four Fundamental Electronic Elements From Linearity to Nonlinearity

The approximate equations for self- and mutual-inductance can be further investigated. In addition, the general characteristic equations for the four fundamental elements in three situations are basically completed. This new development not only reveals theoretically the characteristics of the four fundamental electronic elements, but also practically deals with all the media from linearity to nonlinearity. This will meet all the purposes of application. Hence, it is very important and significant to the development of the science of electronic elements.     

周期平稳故障信号分析

针对周期平稳故障信号的特点,给出了时域、频域、时频域分析以及统计理论分析方法.首先,利用周期平稳信号的特点,采用季节性模型,对周期平稳信号进行建模,并作预测预报;其次,采用循环平稳度、循环相关谱等周期平稳信号的特征量有效地获取其他方法难以获得的信息;通过实例给出了进化谱理论及其应用方法,结果表明进化谱在故障诊断中有着较好的分析作用;最后,采用时频域分析较好地分析出非平稳信号的各种尺度信号的特征.     

基于SARIMA-GA-Elman组合模型的短时交通流预测方法

针对单一模型无法深入挖掘交通流复杂的线性和非线性特征方面的局限性以及神经网络模型在训练时收敛速度缓慢等问题,提出了一种基于SARIMA-GA-Elman的组合预测模型.该组合模型有效地融合了季节性差分自回归滑动平均(seasonal autoregressive integrated moving average,SARIMA)模型良好的线性拟合能力和Elman递归神经网络强大的非线性映射能力;在预测过程中首先基于SARIMA滚动预测时间序列的线性分量,然后使用SARIMA模型的预测误差序列建立Elman-RNN构建非线性误差模型;此外在训练非线性误差模型的过程中使用经过二进制编码的遗传算法(genetic algorithm,GA)优化Elman-RNN,旨在提升Elman-RNN的训练效率,最后把两个模型的预测结果加权组合得到最终的预测值.实验结果表明,该组合模型在预测精度和鲁棒性方面相比单一模型都有较为明显的提升.     

心率变异性的复杂性与分形维数分析

发挥非线性动力学的潜力，提出了心率变异性（ＨＲＶ）信号的复杂度分析法和分形维数分析法。由于这两种方法能从较短的ＨＲＶ信号中提取信息，因此作者又进一步把这两种分析方法发展成动态分析法。用这两种方法分别对一个改变体位的ＨＲＶ信号和一个发生心肌缺血的ＨＲＶ信号的过程进行了动态分析。分析结果表明，它们都成功地检测出了ＨＲＶ信号的生理和病理变化。可见，动态复杂度分析法和动态分形维数分析法对ＨＲＶ信号及类似于ＨＲＶ的信号是很有前途的分析方法。