Complexity Of Cardiac Dynamics Research Articles

Heart rate variability and insomnia in depressed patients with breast cancer

ObjectivesDepression is associated with unhealthy autonomic regulation. However, whether patients with breast cancer (BC) with different degrees of depression can be identified from linear and non-linear dynamics in the autonomic...

Complexity and characteristic frequency studies in ECG signals of mice based on multiple scale factors

Existing methods of physiological signal analysis based on nonlinear dynamic theories only examine the complexity difference of the signals under a single sampling frequency. We developed a technique to measure the multifractal characteristic parameter intimately associated with physiological activities through a frequency scale factor. This parameter is highly sensitive to physiological and pathological status. Mice received various drugs to imitate different physiological and pathological conditions, and the distributions of mass exponent spectrum curvature with scale factors from the electrocardiogram (ECG) signals of healthy and drug injected mice were determined. Next, we determined the characteristic frequency scope in which the signal was of the highest complexity and most sensitive to impaired cardiac function, and examined the relationships between heart rate, heartbeat dynamic complexity, and sensitive frequency scope of the ECG signal. We found that all animals exhibited a scale factor range in which the absolute magnitudes of ECG mass exponent spectrum curvature achieve the maximum, and this range (or frequency scope) is not changed with calculated data points or maximal coarse-grained scale factor. Further, the heart rate of mice was not necessarily associated with the nonlinear complexity of cardiac dynamics, but closely related to the most sensitive ECG frequency scope determined by characterization of this complex dynamic features for certain heartbeat conditions. Finally, we found that the health status of the hearts of mice was directly related to the heartbeat dynamic complexity, both of which were positively correlated within the scale factor around the extremum region of the multifractal parameter. With increasing heart rate, the sensitive frequency scope increased to a relatively high location. In conclusion, these data provide important theoretical and practical data for the early diagnosis of cardiac disorders.

Non‐linear cardiac dynamics and morning dip: an unsound circadian rhythm

The frequency of sudden cardiac death increases in the morning. The relationship between decreased complexity of heart rate dynamics and sudden cardiac death has been documented. An understanding of circadian variation in the complexity of cardiac dynamics may be important to predict and prevent sudden cardiac death. Dynamic 24-h electrocardiographic recordings were obtained from 30 healthy ambulant subjects aged 41-50 years, and the digitized data were partitioned into sections of 30 min duration. For each section, four indexes obtained from separate algorithms of non-linear dynamics of the RR interval--modified correlation dimension, Lyapunov exponent, approximate entropy, and fractal dimension--were calculated. Normalized low-(0.04-0.15 hertz) and high-frequency (> 0.15 hertz) components were also calculated. All four indexes of non-linear dynamics showed a remarkably similar circadian rhythm: a prominent morning dip preceded by a steep decline during the late night, a recovery during the evening and a peak around midnight. In the morning, the low-frequency component rose rapidly with concomitant reduction in the high-frequency component. The complexity of cardiac dynamics decreases significantly in the morning, and this may contribute to the ominously increased rate of cardiac death in the morning hours.

Nonlinear Analysis of ECG in Normal Subjects and Acute Myocardial Infarction Patients

Background: In the last two decades, methods from nonlinear analysis and chaos theory have been used for the characterization of cardiac dynamics. Previous studies, mainly in the analysis of RR time series, indicated the diagnostic and prognostic value of such techniques in several pathological conditions, but also generated disputes regarding the presence of true nonlinearities in the data. The application of such methods to ECG time series analysis, in normal subjects as well as in patients with heart disease, is less investigated. The present study was undertaken to test whether nonlinear analysis of ECG can define possible changes in the complexity of cardiac dynamics in patients with acute myocardial infarction (AMI).Methods: We examined 10 normal subjects and 20 AMI patients in sinus rhythm for 3 consecutive days. Correlation dimension (D2) measurements were obtained with a combination of the SingularValue Decomposition and the Grassberger‐Procaccia methods and compared with the results from corresponding surrogate data series, in order to detect the presence of nonlinearities. The reproducibility of ECG D2 measurements was tested with the coefficient of variation.Results: No difference was found in ECG D2 between normals and patients. The surrogate data score was above two in all measurements, which indicates the presence of nonlinearities in the ECG signal. The intraindividual reproducibility of D2 measurements was moderate to good in normals and AMI patients.Conclusion: The results of this study support the hypothesis that the ECG signal is produced by a low‐dimensional dynamic system. D2 analysis of ECG is a nonlinear quantitative method with moderate to good reproducibility, but with no evident discriminating capabilities between normals and AMI patients.

Circadian aspects of apparent correlation dimension in human heart rate dynamics.

The purpose of this study was to examine changes in complexity of cardiac dynamics over 24 h. With use of Holter monitoring, 27 24-h electrocardiogram recordings were obtained from 15 healthy subjects. For each recording, the apparent dimension (DA) was calculated for consecutive sections of 500 heartbeats. These were used to determine nighttime and daytime dimension (D(An) and D(Ad), respectively) as well as the difference between D(An) and D(Ad) (delta DA). Mean 24-h DA, D(An), and D(Ad) were 5.9 +/- 0.3, 6.3 +/- 0.5, and 5.6 +/- 0.6, respectively. D(An) was significantly higher than D(Ad) (P < 0.001), with a mean delta DA of 0.6 +/- 0.7. Furthermore, 67% of delta DA values were significantly different from zero at the 0.05 level. The results show that dimension analysis may be applied to heart rate dynamics to reveal circadian differences of heart rate complexity. We suggest that the decreased complexity during daytime may result from the synchronization of physiological functions. The increase in complexity at night would then correspond to an uncoupling of these functions during the regenerative period.

Nonlinear time series analysis of electrocardiograms.

In recent years there has been an increasing number of papers in the literature, applying the methods and techniques of Nonlinear Dynamics to the time series of electrical activity in normal electrocardiograms (ECGs) of various human subjects. Most of these studies are based primarily on correlation dimension estimates, and conclude that the dynamics of the ECG signal is deterministic and occurs on a chaotic attractor, whose dimension can distinguish between healthy and severely malfunctioning cases. In this paper, we first demonstrate that correlation dimension calculations must be used with care, as they do not always yield reliable estimates of the attractor's "dimension." We then carry out a number of additional tests (time differencing, smoothing, principal component analysis, surrogate data analysis, etc.) on the ECGs of three "normal" subjects and three "heavy smokers" at rest and after mild exercising, whose cardiac rhythms look very similar. Our main conclusion is that no major dynamical differences are evident in these signals. A preliminary estimate of three to four basic variables governing the dynamics (based on correlation dimension calculations) is updated to five to six, when temporal correlations between points are removed. Finally, in almost all cases, the transition between resting and mild exercising seems to imply a small increase in the complexity of cardiac dynamics. (c) 1995 American Institute of Physics.