Chaotic Characteristic Analysis Of ECG Model

The heart disease is one of the most prominent diseases threatening the life of human being. Electrocardiogram (ECG) signal records the activity of physiological electricity about heart, and becomes an important basis for diagnosing the diseases and evaluating the function of heart. At the same time, ECG simulation technology has also been considerable development. We have developed a variety of ECG simulation equipment. To some extent, the simulated ECG is similar to the measured ECG, but it can’t meet needs in reality. This raises higher requirement to ECG simulation technology. It is the urgent problem how to get a more realistic simulated ECGChaos analysis has two methods:model analysis and numerical analysis. According to the realistic situation of the ECG model, the output of the RR interval time series model of the ECG model is the study object by numerical analysis to study chaos. Study of chaotic time series includes phase space reconstruction, calculating the correlation dimension, calculating the largest Lyapunov exponent and so on. This paper study the chaotic characteristics of the simulated RR interval time series. At first, the chaotic attractors is constructed by phase space reconstruction, which determines the chaotic characteristics of RR interval time series model. The key to phase space reconstruction is to calculate delay time and embedding dimension. In order to obtain accurate results, the paper uses mutual information method and C-C method to calculate optimal delay time, and uses Cao method and C-C method to calculate embedding dimension. Obtained values by different methods are mutual authentication. In order to calculate correlation dimension, the paper uses GP algorithm with the result of fraction. In order to calculate the largest Lyapunov exponent, this paper uses Wolf method and small data method with the result of positive.Using simulated RR interval time series of two-dimension chaotic attractors reconstruction, correlation dimension, largest Lyapunov exponent calculation, the various evidence demonstrates the RR interval time series model of chaotic characteristics, that is, the dynamical motion of ECG model is chaotic. This not only supports the view that the normal human heart motion is chaotic, but also provides support to obtain more realistic simulated ECG signal.