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.     

Qualitative chaos analysis for ventricular tachycardia and fibrillation based on symbolic complexity.

Ventricular fibrillation (VF) is one of the most serious malignant arrhythmias, usually resulting from immediate degeneration of ventricular tachycardia (VT). The surrogate data test (SDT) has been employed in the qualitative detection and analysis of cardiac chaos. Unfortunately, the current SDT method, based on the GP (Grassberger and Procaccia) algorithm, may not be suitable for the analysis of VF rhythm, which has been shown to be a high-dimensional signal. This paper proposes a novel qualitative analysis method based on symbolic dynamics for chaotic systems: the complexity dispersity method. Compared with the GP algorithm, our qualitative complexity method demonstrates better analytical accuracy and robustness and requires less data points (5 seconds vs 20 seconds). When used in the analysis of experimental data, our method achieved 100% accuracy in the detection of cardiac pathology for VT and VF.     

Complexity measure and complexity rate information based detection of ventricular tachycardia and fibrillation

On the basis of non-linear dynamics, the paper uses a Lempel-Ziv complexity measure and presents a new definition of the information complexity rate: cc(n). Using such a definition, relative properties are obtained to help identify chaotic process accurately. Applying complexity analysis to abnormal ECGs recorded from patients with an implantable cardioverter defibrillator, the reasonableness of this information complexity and complexity rate approach are confirmed by means of biological experiments and computer simulations. Finally, objective analysis and explanations of the mechanisms of VT and VF are reported. The results indicate that, with the help of the complexity measure and complexity rate, recognition of ventricular tachycardia (VT) and ventricular fibrillation (VF) signals can be achieved with accuracy up to 100% (VT: 100%; VF: 98.7%).     

基于短时心率变异性信号两种熵测度的过速型室性心律失常的预测分析

目的:过速型室性心律失常［持续性室性心动过速或心室纤颤（VT/VF）］是心脏猝死的主要诱因,测试VT/VF发生前心率变异性信号是否有明显改变可作为VT和VF发生的提前预报信号。方法:以78名患者体内心脏复律除颤器记录的VT/VF事件发生前心率变异性信号（VT/VF序列）和来自同一患者的正常窦性节律（CON序列）组成的135个样本对作为实验序列。通过预处理消除实验序列的伪差、异位心搏等干扰,采用两种基于熵的非线性复杂度测度——样本熵和逐点多尺度熵（PPMSE）,分析VT和VF发生前十几分钟的VT/VF序列,以及心率增加和减小的VT/VF序列复杂性,并采用PPMSE方法讨论了接近VT/VF发生时VT/VF序列复杂性变化。结果:与正常对照组CON序列相比,在一定匹配容差内,VT/VF发生前心率变异性信号的样本熵明显减小（r<0.25×SD, P<0.000 5）,心率增加的VT/VF序列减小更显著（r<0.3×SD, P<0.000 1）;VT/VF序列的PPMSE在越接近VT/VF发生时刻减小越显著,提取的CI指数存在显著差异（如1~30尺度,N=986、500、250时,P=1.5×10-2、P=4.3×10-3、P=1.3×10-5）,心率增加的VT/VF序列区分性能更好。结论:过速型心律失常的自然发作并不是突发现象,在其发作前或许存在某种生理预兆,两种熵测度可能是短时预报恶性室性心律失常事件的有效非线性参数。     

基于联合熵的室性心动过速与室颤识别

对心电信号进行分析,是医学临床上检测和诊断心脏功能的重要手段。室性心动过速(VT)和心室纤颤(VF)是威胁人类生命的严重的心脏疾病。本文应用联合熵方法分析正常心跳信号(NSR)、VT和VF信号的动力学复杂性信息。将动力学符号统计理论以及替代数据的概念融入其中,通过计算原始时间序列和其替代时间序列之间的联合熵值,来量化序列的动力学复杂性。经过对实际心率数据的计算机分析,证明了联合熵方法的合理性。根据联合熵值的不同对NSR、VT和VF信号进行区分,取得了令人满意的效果。     

基于复杂度和复杂率的心动过速和心室纤颤检测

本文从非线性动力学的角度 ,利用L Z复杂度 ,提出了复杂率指标及其定义 ,推导了复杂率的相关性质 ,并应用于异常心电信号的复杂性分析。经过生物实验、数据采集和计算机的仿真分析 ,证明了信息复杂度和复杂率的合理性。最后 ,对心脏异常心电信号中的VT和VF信号机理和复杂性进行了客观的分析和解释。结果表明 ,利用复杂度和复杂率 ,VT和VF信号的识别和分辨率达到 10 0 %。     

Serum transforming growth factor-beta1 as a risk stratifier of sudden cardiac death

Sudden cardiac death prematurely claims the lives of some 7 million each year worldwide. It occurs primarily in patients with an underlying structural cardiac abnormality, and regardless of the type of the underlying pathology (heart failure, dilated and hypertrophic cardiomyopathies, myocardial infarction and aging), death is almost always caused by ventricular tachycardia (VT) which rapidly degenerates to ventricular fibrillation (VF). Implantable cardioverter defibrillator is an effective but expensive therapy for preventing SCD, and finding a reasonably specific, sensitive and cost-effective risk stratification tool for patients at high risk of sudden cardiac death will have great clinical utility in preventing premature sudden cardiac death. Increased myocardial fibrosis has been shown to develop in a wide range of cardiac diseases all manifesting increased risk of VT and VF. Clinical and experimental studies attribute a major role for fibrosis in the initiation of VT, VF and sudden cardiac death. Transforming growth factor-beta1 (TGF-beta1) has been shown to promote myocardial tissue fibrosis and perhaps more importantly in cardiac conditions associated with increased myocardial fibrosis are shown to be positively correlated with increased serum levels of TGF-beta1. In the present hypothesis we suggest that monitoring the serum levels of TGF-beta1 may be a cost-effective risk stratifier to identify patients at high risk of sudden cardiac death caused by VT and VF.     

Analysis of implantable cardioverter defibrillator signals for non conventional cardiac electrical activity characterization

Implantable cardioverter defibrillators (ICDs) can store intracardiac electrograms (EGMs) in sinus rhythm (SR), at the onset of spontaneous ventricular tachyarrhythmias (VT) or during their course. This allows the investigation of unknown features of the heart electrical activity associated with different cardiac rhythms. In this study we propose a non conventional cardiac electrical activity characterization (CEAC) that extracts quantitative information about the power spectrum wideness and variability of the beat-by-beat morphology. We analyze 293 EGMs from 40 patients who underwent implantation of St Jude Medical–Ventritex ICDs that allow the storage of EGMs with two different modes of recording: bipolar (BIP) and unipolar or far-field (FF). The EGMs are studied with this CEAC by (1) exploring differences between the CEAC measured from FF and BIP EGMs during similar cardiac rhythms, and (2) investigating the mode of recording that allows a better separation between SR and VT rhythms. Results show that, with similar cardiac rhythm, the CEACs from FF or BIP recordings are different (for SR rhythm: sensitivity 81.5%, specificity 93.6%; for VT rhythm: sensitivity and specificity 100%); thus FF and BIP EGMs analyze different aspects of cardiac activity.The CEAC applied to FF EGMs distinguishes better EGMs obtained during SR from VT rhythms (VT vs SR with sensitivity 92.7% and specificity 79.7%) than when it is applied to BIP signals (VT vs SR with sensitivity 60% and specificity 73.3%).     

Multiscale Analysis of Heart Rate Variability: A Comparison of Different Complexity Measures

Heart rate variability (HRV) is an important dynamical variable of the cardiovascular function. There have been numerous efforts to determine whether HRV dynamics are chaotic or random, and whether certain complexity measures are capable of distinguishing healthy subjects from patients with certain cardiac disease. In this study, we employ a new multiscale complexity measure, the scale-dependent Lyapunov exponent (SDLE), to characterize the relative importance of nonlinear, chaotic, and stochastic dynamics in HRV of healthy, congestive heart failure (CHF), and atrial fibrillation subjects. We show that while HRV data of all these three types are mostly stochastic, the stochasticity is different among the three groups. Furthermore, we show that for the purpose of distinguishing healthy subjects from patients with CHF, features derived from SDLE are more effective than other complexity measures such as the Hurst parameter, the sample entropy, and the multiscale entropy.     

Irregularity test for very short electrocardiogram (ECG) signals as a method for predicting a successful defibrillation in patients with ventricular fibrillation

A significant proportion of patients with ventricular fibrillation (VF) can only be defibrillated after a period of chest compressions and ventilation before the defibrillation attempt. In these patients, unsuccessful defibrillations increase the duration of heart arrest and reduce the possibility of a successful resuscitation, which could be avoided if a reliable prediction for the success of defibrillation could be made. A new method is presented for estimating the irregularity in very short electrocardiographic (ECG) recordings that enables the prediction of a successful defibrillation in patients with VF. This method is based on a recently developed determinism test for very short time series. A slight modification shows that the method can be used to determine relative differences in irregularity of the studied signals. In particular, ECG recordings of VF from patients who could be successfully defibrillated are characterized by a higher level of irregularity, indicating a chaotic nature of the dynamics of the heart, which is in agreement with previous studies on long ECG recordings showing that cardiac chaos was prevalent in healthy heart, whereas in severe congestive heart failure, a decrease in the chaotic behavior was observed.     

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.     

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。     

Characterization of the nonlinear content of the heart rate dynamics during myocardial ischemia

We develop a method to quantify the changes in heart rate dynamics during local myocardial ischemia induced by a percutaneous transluminal coronary angioplasty procedure (PTCA). The method introduces an index measuring the nonlinear content of the beat-to-beat (RR) time series by using nonlinear time series techniques such as surrogate data analysis and average mutual information.The index is applied to RR data from 67 subjects obtained before, during, and after the ischemic period and shows an increase in the nonlinearity of the cardiac control dynamics during ischemic and reperfusion stages. The nonlinear index is also used to characterize the effects of performing the coronary occlusion at different arteries and distances. We observe that the effect of ischemia becomes larger as the occlusion distance is reduced, and that most of the changes in the nonlinear content of the dynamics occur at long time scales typically related to sympathetic modulation of the cardiac rhythm (6–25 s).     

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.     

A classification scheme for ventricular arrhythmias using wavelets analysis

Identification and classification of ventricular arrhythmias such as rhythmic ventricular tachycardia (VT) and disorganized ventricular fibrillation (VF) are vital tasks in guiding implantable devices to deliver appropriate therapy in preventing sudden cardiac deaths. Recent studies have shown VF can exhibit strong regional organizations, which makes the overlap zone between the fast paced rhythmic VT and VF even more ambiguous. Considering that implantable cardioverter-defibrillator (ICD) are primarily rate dependent detectors of arrhythmias and that there may be patients who suffer from arrhythmias that fall in the overlap zone, it is essential to identify the degree of affinity of the arrhythmia toward VT or organized/disorganized VF. The method proposed in this work better categorizes the overlap zone using Wavelet analysis of surface ECGs. Sixty-three surface ECG signal segments from the MIT-BIH database were used to classify between VT, organized VF (OVF), and disorganized VF (DVF). A two-level binary classifier was used to first extract VT with an overall accuracy of 93.7 % and then the separation between OVF and DVF with an accuracy of 80.0 %. The proposed approach could assist clinicians to provide optimal therapeutic solutions for patients in the overlap zone of VT and VF.     

基于LMCD的病理心电信号分析

目的采用基于Lopez-Mancini-CalbetDivergence（LMCD）的统计复杂度分析方法,对充血性心力衰竭信号、心脏性猝死信号与正常窦性心律信号进行统计复杂度分析。方法采用Bandt-Pompe算法对符号序列进行模式概率统计,分析了充血性心力衰竭信号、心脏性猝死信号与正常窦性心律信号的统计复杂度。结果 3种心律信号的统计复杂度存在差异,正常窦性心率信号的统计复杂度最高,充血性心力衰竭信号次之,心脏性猝死信号最低。方差分析表明,基于LMCD的分析方法得出的3种心电信号的统计复杂度差异具有统计学意义。结论采用LMCD的统计复杂度方法可以有效地区分3种不同生理病理状态下心电信号,为辅助临床诊断提供了一种新手段。     

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.     

一个基于频谱检测是否发生心室纤颤的算法

本文提出可能利用一维滑动ＤＦＴ并行处理器实时提取ＥＣＧ信号的滑动频谱，以中间一段的频谱的模的均方值作为特征来判别病人是否发生了心室纤颤。作者对１４０个Ｖｅｎｔｒｉｃｕｌａｒ Ｆｉｂｒｉｌｌａｔｉｏｎ（ＶＦ）信号和Ｖｅｎｔｒｉｃｕｌａｒ Ｔａｃｈｙｃａｒｄｉａ（ＶＴ）信号作了统计，并用概率纸证明在特征值取值范围内是正态概率分布模型。作者使用了序贯假设检验的办法来进行判决，通过扩大判决阀值减少了误判率     

Ruthenium red-induced transition from ventricular fibrillation to tachycardia in isolated rat hearts:: possible involvement of changes in mitochondrial calcium uptake

INTRODUCTION: Ventricular tachycardia (VT) is considered to be the most common precursor of ventricular fibrillation (VF) and sudden cardiac death. However, the mechanisms underlying the transition from VT to VF remain unclear despite more than a century of study. Here, we investigated whether perfusion of the heart with blockers of mitochondrial Ca(2+) uniporter changed the macrodynamics of the heart between VT and VF. METHODS: The experiments were performed using Langendorff perfused isolated rat hearts in which left ventricular pressure (LVP) and left ventricular cardiomyogram (LVCMG) were measured. Sustained VT or VF was induced by burst pacing of the left ventricular muscles. RESULTS: During pacing-induced sustained VF, perfusion of the heart with ruthenium red (RR) or Ru 360, blockers of mitochondrial Ca(2+) uniporter, resulted in the reversible conversion of VF to VT. In contrast, during pacing-induced sustained VT, perfusion of the heart with spermine, an activator of mitochondrial Ca(2+) uptake, resulted in the reversible conversion of VT to VF, and the effect was antagonized by cotreatment with RR. In addition, RR-induced conversion of VF to VT was antagonized by cotreatment with S(-)-Bay K8644 (Bay K), an activator of L-type Ca(2+) channels, suggesting that the inactivation of L-type Ca(2+) channels was responsible for the RR-induced effect on the macrodynamics of hearts. In fact, perfusion with verapamil, an antagonist of L-type Ca(2+) channels, during pacing-induced sustained VF, resulted in the conversion of VF to VT. CONCLUSION: This study demonstrated that perfusion of isolated rat hearts with blockers of Ca(2+) uptake by mitochondria resulted in the reversible conversion of pacing-induced sustained VF to VT, suggesting that changes in mitochondrial Ca(2+) uptake were possibly involved in the transition between VT and VF.