心率信号的混沌分析

非线性动力学研究方法之一的混沌分析方法在许多科学领域都得到了应用，研究表明，心率信号是混沌信号，本文介绍了判断信号混沌特征的方法，如相平面图，延时图，功率谱等菜法；李雅普诺夫指数，分维数，混沌度等数值法。介绍了心率信号混沌分析在心血管疾病的诊断预测及植物神经系统功能测试方面的应用，最后还介绍了作者的部分工作。     

Classification of foetal heart rate sequences based on fractal features

Visual inspection of foetal heart rate (FHR) sequences is an important means of foetal well-being evaluation. The application of fractal features for classifying physiologically relevant FHR sequence patterns is reported. The use of fractal features is motivated by the difficulties exhibited by traditional classification schemes to discriminate some classes of FHR sequence and by the recognition that this type of signal exhibits features on different scales of observation, just as fractal signals do. To characterise the signals by fractal features, two approaches are taken. The first models the FHR sequences as temporal fractals. The second uses techniques from the chaos-theory field and aims to model the attractor based on FHR sequences. The fractal features determined by both approaches are used to design a Bayesian classification scheme. Classification results for three classes are presented; they are quite satisfactory and illustrate the importance of this type of methodology.     

Chaos in schizophrenia associations, reality or metaphor?

There is evidence that schizophrenic associations display “chaotic”, random-like behavior and decreased predictability. The evidence suggests a hypothesis that the “chaotic” mental disorganization could be explained within the concept of nonlinear dynamics and complexity in the brain that may cause chaotic neural organization. Testing of the hypothesis in the present study was performed using nonlinear analysis of bilateral electrodermal activity (EDA) during resting state and an association test in 56 schizophrenic patients and 44 healthy participants. EDA is a suitable measure of brain and autonomic activity reflecting neurobiological changes in schizophrenia that may indicate changes in nonlinear neural dynamics related to associative process. The results show that quantitative indices of chaotic dynamics (the largest Lyapunov exponents) calculated from EDA signals recorded during rest and the association test are significantly higher in schizophrenia patients than in the control group and increase during the test in comparison to the resting state. The difference was confirmed by statistical methods and using surrogate data testing that rejected an explanation within the linear statistical framework. The results provide supportive evidence that pseudo-randomness of schizophrenic associations and less predictability could be linked to increased complexity of nonlinear neural dynamics, although certain limitations in data interpretation must be taken into account.     

局部肌疲劳过程与表面肌电信号分维值变化关系的研究

利用肱二头肌在不同收缩力水平上持续恒力收缩时采集的表面肌电信号，研究局部肌疲劳过程中肌电信号的分形维变化规律。结果表明，随着疲劳程度的加深，表面肌电信号的分维值在不同收缩力水平上均呈下降趋势，与中值频率的下降趋势相一致。     

冠心患者与健康人同步12导联ECG信号的李雅普诺夫指数谱比较研究

本研究首次计算了60名具有窦性心率的冠心患者(Coronary atrery disease:CAD)和60名健康老年人的同步12导联心电图信号的李雅普诺夫指数谱.发现对同一个人,从不同导联得出的Lyapunov指数是不同的,具有明显的空间分布特性.所有导联的ECG信号的最大Lyapunov指数L1均为正数,其余指数为负,心电信号表现出明显的混沌特征.同一导联相比较,冠心患者的最大Lyapunov指数L1低于健康正常人的最大Lyapunov指数L1,提示在心肌缺血的情况下,心电信号的混沌程度下降了,重构相空间中ECG信号的奇异吸引子的动力学复杂性降低了.结果表明,在估算Lyapunov指数时,有必要指明导联的位置.在Lyapunov指数谱中,最大Lyapunov指数可以将冠心患者与健康正常人区分开来,在心脏疾病诊断中具有潜在的应用价值.     

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。     

Correlation Dimension Maps of EEG from Epileptic Absences

The understanding of brain activity, and in particular events such as epileptic seizures, lies on the characterisation of the dynamics of the neural networks. The theory of non-linear dynamics provides signal analysis techniques which may give new information on the behaviour of such networks. Methods: We calculated correlation dimension maps for 19-channel EEG data from 3 patients with a total of 7 absence seizures. The signals were analysed before, during and after the seizures. Phase randomised surrogate data was used to test chaos. Results: In the seizures of two patients we could distinguish two dynamical regions on the cerebral cortex, one that seemed to exhibit chaos whereas the other seemed to exhibit noise. The pattern shown is essentially the same for seizures triggered by hyperventilation, but differ for seizures triggered by light flashes. The chaotic dynamics that one seems to observe is determined by a small number of variables and has low complexity. On the other hand, in the seizures of another patient no chaotic region was found. Before and during the seizures no chaos was found either, in all cases. Conclusions: The application of non-linear signal analysis revealed the existence of differences in the spatial dynamics associated to absence seizures. This may contribute to the understanding of those seizures and be of assistance in clinical diagnosis.     

经颅混沌电信号治疗难治性癫痫的初步临床研究

目的 探讨混沌电信号辅助治疗难治性阗痫的疗效和耐受性。方法 设计输出混沌电信号的刺激仪，用于辅助治疗难治性癫痫６例，经６个月的随访观察，比较治疗前后发作频率及严重程度的变化。结果 所有病例对此种措施的耐受性均良好，且发作频率和发作严重度有不同程度降低。结论 混沌信号治疗癫肖其光明前景，但用于临床仍有大量理论及技术问题有待解决。