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Hawthorn (Crataegus) may play a role in the prevention and treatment of cardiovascular diseases such as hypertension, hyperlipidemia, and in particular, congestive heart failure. Evidence is accumulating that hawthorn may induce anti-ischemia/reperfusion-injury, anti-arrhythmic, hypolipidemic and hypotensive effects. These beneficial effects may in part be due to the presence of antioxidant flavonoid components. While a number of studies have been performed to evaluate the clinical efficacy of hawthorn, an international, multi-center, prospective clinical study including a large number of New York Heart Association (NYHA) class II/III heart failure patients is ongoing to test hawthorn's long-term therapeutic effects. Further clinical trials as well as pharmacokinetic and mechanistic studies are needed to explore and confirm its effectiveness, safety and pharmacological mechanism.

It is known that heart activity changes during aging. In this paper, we evaluated alterations of heart activity from the complexity point of view. We analyzed the variations of heart rate of patients with congestive heart failure that are categorized into four different age groups, namely 30–39, 50–59, 60–69, and 70–79 years old. For this purpose, we employed three complexity measures that include fractal dimension, sample entropy, and approximate entropy. The results showed that the trend of increment of subjects’ age is reflected in the trend of increment of the complexity of heart rate variability (HRV) since the values of fractal dimension, approximate entropy, and sample entropy increase as subjects get older. The analysis of the complexity of other physiological signals can be further considered to investigate the variations of activity of other organs due to aging.

The regularity of heart rates has a loss in cases of illness and aging. Assessing the dynamics of heart rate fluctuations can provide valuable information about cardiac system. In this paper, heart rate fluctuations and its wavelet entropy (WE) are analyzed to demonstrate its potentials for risk stratification of cardiac diseases. The regularity of heart rate fluctuations is estimated by exploiting the time-frequency localization ability of wavelet analysis and the ability of entropy. The results show that WE for patients with congestive heart failure show a very low value and can be completely separated from health subjects. In addition, the values of WE decrease with aging. The lower the values of WE, the higher the risk of heart disease is. The values of WE also reflect the distribution of the energy of heart rhythm. Significant correlations are demonstrated between WE and the power in the three frequency bands. The results have shown that WE can be used to analyze short, non-stationary data time series both in time domain and in frequency domain simultaneously and can be feasible for the discrimination of the differences of heart rate fluctuations between healthy groups and CHF groups as a diagnostic tool.

In this paper, a method is developed to determine the material constants of normal and diseased human hearts in vivo. With these material constants, one can compute regional stresses and strains through the left ventricular (LV) wall. This knowledge provides a better understanding of the heart adaptation process and may lead to earlier diagnosis of heart disease. It also may enable earlier treatment of heart disease, and evaluation of the efficacy of treatments for it.

The heart is modeled as a thick cylindrical shell and large deformation theory, incorporating residual stresses is employed to compute the regional stresses and strains through the LV wall. These stresses and strains at the end diastolic state for the normal, hypertensive and congestive heart failure cases are presented. The average circumferential stress is also computed at the end systolic state for these cases.

This study documents our efforts to provide computer support for the diagnosis of congestive heart failure (CHF). That computer support takes the form of an index value. A high index value indicates a low probability of CHF, and an index value below a threshold of 25.6 suggests a high probability of CHF. To create that index, we have designed a sophisticated algorithm chain which takes electrocardiogram signals as input. The signals are pre-processed before they are sent to a range of nonlinear feature extraction algorithms. The top 10 feature extraction methods were used to create the CHF index. By using objective feature extraction algorithms, we avoid the problem of inter- and intra-observer variability. We observed that the nonlinear feature extraction methods reflect the nature of the human heart very well. That observation is based on the fact that the nonlinear features achieved low $p$-values and high feature ranking criterion scores.

Many of the used features in sudden cardiac death (SCD) classification algorithms are based on features present in the autonomic system. However, changes in the autonomic system occur in both SCD subjects and patients with congestive heart failure (CHF). Therefore, many overlaps are observed in the features extracted from the cardiac signals of these two groups. To solve this challenge, this paper studies the changes in the multifractal dimension in patients with SCD and compares it with the subjects with CHF using the heart rate variability (HRV) signal processing. For this purpose, HRV signals are initially extracted, and their four sub-signals are determined using the empirical mode decomposition (EMD) method. Afterward, the instant amplitude of each sub-signal obtained in the previous step is calculated using the Teager energy method; thus, new signals are generated through the utilization of these instant amplitudes. Subsequently, modifications in each new signal’s fractal dimensions are obtained using the multifractal detrended fluctuation analysis (MF-DFA) method. The appropriate features are selected using the t-test method and are applied to the support vector machine algorithm as input data. The proposed algorithm can differentiate the signal of SCD subjects with an average accuracy of 84.08% in 26min prior to the event.

In this paper, we developed a new approach for the analysis of physiological time series. An iterative convolution filter is used to decompose the time series into various components. Statistics of these components are extracted as features to characterize the mechanisms underlying the time series. Studies have shown that many normal physiological systems involve irregularity, while the decrease of irregularity usually implies abnormality. This motivates the use of the statistics for “outliers” in the components as features measuring irregularity. Support vector machines are used to select the most relevant features that are able to differentiate the time series from normal and abnormal systems. This new approach is successfully used in the study of congestive heart failure by heart beat interval time series.

To study of amiodarone in treatment of congestive heart failure arrhythmia of clinical efficacy and safety. Methods 80 patients with CHF complicated with ventricular arrhythmia patients,were randomly divided into control group and treatment group,the control group was given routine anti heart failure treatment, the treatment group was treated with amiodarone therapy, comparative analysis of two groups of patients in improving cardiac function, arrhythmia, left ventricular ejection fraction (LVEF), rhythm of the heart failure readmission rates and adverse drug reaction and other negative situation. Results Antiarrhythmic amiodarone group, improvement of heart function, left ventricular ejection fraction, rhythm of the heart failure readmission rates and other aspects were significantly better than the control group, the difference was statistically significant (P<0.05). Conclusion Amiodarone is the treatment of congestive heart failure and ventricular arrhythmia of effective drugs, safe use. Adverse reaction to light