Xinbao Ning

Multifractal analysis of electronic cardiogram taken from healthy and unhealthy adult subjects

Electronic Cardiogram (ECG) data taken from healthy adult subjects are found to characterize multifractality. In order to quantitatively analyze multifractal spectrum, the area of the spectrum is computed. We have a comparison between the spectrum of the young subjects and that of the old ones. We find that the area of young adult subjects multifractal spectrum is far larger than the older ones and the logarithm of the area of the spectrum is inversely proportion to age. It shows that when time is running on human heartbeat energy is exponentially decreasing until heart failure. And distinct difference between the area of the multifractal spectrum of healthy subjects and that of having coronary disease is not found. We analyze the ECG data taken from patients with brain injury. The area of their ECG multifractal spectrum is distinctly descending. It shows that a persons multifractal spectrum is controlled mainly by his neurosystem. With advancing age, the neuroautonomic control of peoples body on the ECG decreases and tends from multifractality to monofractality.

Visibility graph analysis on heartbeat dynamics of meditation training

We apply the visibility graph analysis to human heartbeat dynamics by constructing the complex networks of heartbeat interval time series and investigating the statistical properties of the network before and during chi and yoga meditation. The experiment results show that visibility graph analysis can reveal the dynamical changes caused by meditation training manifested as regular heartbeat, which is closely related to the adjustment of autonomous neural system, and visibility graph analysis is effective to evaluate the effect of meditation.

Lyapunov exponents for synchronous 12-lead ECG signals

The Lyapunov exponents of synchronous 12-lead ECG signals have been investigated for the first time using a multi-sensor (electrode) technique. The results show that the Lyapunov exponents computed from different locations on the body surface are not the same, but have a distribution characteristic for the ECG signals recorded from coronary artery disease (CAD) patients with sinus rhythms and for signals from healthy older people. The maximum Lyapunov exponent L1 of all signals is positive. While all the others are negative, so the ECG signal has chaotic characteristics. With the same leads, L1 of CAD patients is less than that of healthy people, so the CAD patients and healthy people can be classified by L1, L1 therefore has potential values in the diagnosis of heart disease.

Distribution of correlation dimensions of synchronous 12-lead ECG signals

Correlation dimensionsD2 of the synchronous 12-lead ECG signals have been investigated for the first time by distributed multi-sensor (multi-electrode) technique. The results show that correlation dimension of heart has the distributed characteristics.D2 calculated from different lead ECG signals is not a constant regardless of a healthy person or a coronary heart disease (CHD) patient with sinus rhythm. But with the same lead signal,D2 of CHD patients is evidently smaller than that of a healthy person except I, II, III leads. A healthy person and CHD patient can be identified byD2 statistically andD2 shows the potential application in the diagnosis of the CHD patients.

Modulation of heart disease information to the 12-lead ECG multifractal distribution

In this paper, a new viewpoint is proposed: the synchronous 12-lead ECG multifractal singularity spectrum distribution is modulated by the heart disease information. There is a well-regulated multifractal singularity spectrum distribution among different leads. We consider that the mean value of the area of human 12-lead ECG multifractal singularity spectrum is controlled mainly by his neurosystem and which shows the strength of the bodys neuroautonomic control on the heart but not the extent of the heart disease. The human hearts intrinsic physiological and pathological activity modulates the human ECG multifractal singularity spectrum distribution. After modulation, the abnormal lead multifractal singularity spectrum can denote the heart disease region.

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.

Brain Connectivity Variation Topography Associated with Working Memory

Brain connectivity analysis plays an essential role in the research of working memory that involves complex coordination of various brain regions. In this research, we present a comprehensive view of trans-states brain connectivity variation based on continuous scalp EEG, extending beyond traditional stimuli-lock averaging or restriction to short time scales of hundreds of milliseconds after stimulus onset. The scalp EEG was collected under three conditions: quiet, memory, and control. The only difference between the memory and control conditions was that in the memory condition, subjects made an effort to retain information. We started our investigation with calibrations of Pearson correlation in EEG analysis and then derived two indices, link strength and node connectivity, to make comparisons between different states. Finally, we constructed and studied trans-state brain connectivity variation topography. Comparing memory and control states with quiet state, we found that the beta topography highlights links between T5/T6 and O1/O2, which represents the visual ventral stream, and the gamma topography conveys strengthening of inter-hemisphere links and weakening of intra-hemisphere frontal-posterior links, implying parallel inter-hemisphere coordination combined with sequential intra-hemisphere coordination when subjects are confronted with visual stimuli and a motor task. For comparison between memory and control states, we also found that the node connectivity of T6 stands out in gamma topography, which provides strong proof from scalp EEG for the information binding or relational processing function of the temporal lobe in memory formation. To our knowledge, this is the first time for any method to effectively capture brain connectivity variation associated with working memory from a relatively large scale both in time (from a second to a minute) and in space (from the scalp). The method can track brain activity continuously with minimal manual interruptions; therefore, it has promising potential in applications such as brain computer interfaces and brain training.

Multifractal ECG Mapping of Ventricular Epicardium During Regional Ischemia in the Pig

Myocardial ischemia creates abnormal electrophysiological substrates that can result in life-threatening ventricular arrhythmias. Early clinical identification of ischemia in patients is important to managing their condition. We analyzed electrograms from an ischemia-reperfusion animal model in order to investigate the relationship between myocardial ischemia and variability of electrocardiogram (ECG) multifractality. Ventricular epicardial electropotential maps from the anesthetized pig during LAD ischemia-reperfusion were analyzed using multifractal methods. A new parameter called the singularity spectrum area reference dispersion (SARD) is presented to represent the temporal evolution of multifractality. By contrasting the ventricular epicardial SARD and range of singularity strength (Deltaalpha) maps against activation-recovery interval (ARI) maps, we found that the dispersions of SARD and Deltaalpha increased following the onset of ischemia and decreased with tissue recovery. In addition, steep spatial gradients of SARD and Deltaalpha corresponded to locations of ischemia, although the distribution of multifractality did not reflect the degree of myocardial ischemia. However, the multifractality of the ventricular epicardial electrograms was useful for classifying the recoverability of ischemic tissue. Myocardial ischemia significantly influenced the multifractality of ventricular electrical activity. Recoverability of ischemic myocardium can be classified using the multifractality of ventricular epicardial electrograms. The location and size of regions of severe ischemic myocardium with poor recoverability is detectable using these methods

Mode entropy and dynamical analysis of irregularity for HFECG

A new algorithm—ModEn (mode entropy) is proposed by analyzing and modifying ApEn (approximate entropy), so that the irregular analysis can be applied to the time series of short-term signals with broad amplitude and slow fluctuation (SBS signals); and the ModEn is introduced in the irregular dynamic analysis of high frequency electrocardiogram (HFECG) on a myocardium infarction (MI) animal model. It is shown that the ModEn has a considerable dynamic change in MI. Hence there are potential application values of the algorithm in the early stage diagnosis of heart disease.

A multi-scale feedback ratio analysis of heartbeat interval series in healthy vs. cardiac patients

The second-order difference plot, as a modified Poincaré plot, is one of the important approaches for assessing the dynamics of heart rate variability. However, corresponding quantitative analysis methods are relatively limited. Based on the second-order difference plot, we propose a novel method, called the multi-scale feedback ratio analysis, which can measure the feedback properties of heart rate fluctuations on different temporal scales. The index [R(TF([τ(1), τ(2)]) is then defined to quantify the average feedback ratio through a definite scale range. Analysis of Gaussian white, 1/f and Brownian noises show that the feedback ratios are indeed on different levels. The method is then applied to heartbeat interval series derived from healthy subjects, subjects with congestive heart failure and subjects with atrial fibrillation. Results show that, for all groups, the feedback ratios vary with increasing time scales, and gradually reach relatively stable states. The R(TF)([10,20]) values of the three groups are significantly different. Thus, R(TF)([10,20]) becomes an effective parameter for distinguishing healthy and pathologic states. In addition, RTF([10,20]) for healthy, congestive failure and atrial fibrillation subjects are close to those of the 1/f, Brownian and white noises respectively, indicating different intrinsic dynamics.