Poincare dispersed dot plot was an important method in studying heart nonlinear state and rate variability(HRV). Based on the shape of Poincare dispersed dot plot, we proposed four quantitative parameters, introduced ...Poincare dispersed dot plot was an important method in studying heart nonlinear state and rate variability(HRV). Based on the shape of Poincare dispersed dot plot, we proposed four quantitative parameters, introduced the method and algorithm how to get them, and tested them with clinical and animal experiment data. The result showed that these four parameters have certain idiosyncrasy with different heart diseases, and the animal experiment result also showed that these parameters were changed remarkably after coronary artery ligation compared with before, which indicated these parameters might be useful for clinical diagnosis. Because the algorithm we used was based only on the shape of the graph, one can apply this algorithm on any other type of graphs like Poincare dispersed dot plot.展开更多
A nonlinear autoregressive (NAR) model is built to model the heartbeat interval time series and the optimum model degree is proposed to be taken to evaluate the nonlinearity degree of heart rate variability (HRV). A g...A nonlinear autoregressive (NAR) model is built to model the heartbeat interval time series and the optimum model degree is proposed to be taken to evaluate the nonlinearity degree of heart rate variability (HRV). A group of healthy persons are studied and the results indicate that this method can effectively get nonlinear information from short (6—7 min) heartbeat series and consequently reflect the degree of heart rate variability, which supplies convenience in clinical application. Finally, a comparison with the traditional time domain method shows that the NAR model method can reflect the complexity of the whole signal and lessen the influence of noise and instability, in the signal.展开更多
Nonlinear science research is a hot point in the world. It has deepened our cognition of determinism and randomicity, simplicity and com-plexity, noise and order and it will profoundly influ-ence the progress of the s...Nonlinear science research is a hot point in the world. It has deepened our cognition of determinism and randomicity, simplicity and com-plexity, noise and order and it will profoundly influ-ence the progress of the study of natural science, including life science. Life is the most complex nonlinear system and heart is the core of lifecycle system. In the late more than 20 years, nonlinear research on heart electric activities has made much headway. The commonly used parameters are based on chaos and fractal theory, such as correlation dimension, Lyapunov ex-ponent, Kolmogorov entropy and multifractal singu-larity spectrum. This paper summarizes the commonly used methods in the nonlinear study of heart electric signal. Then, considering the shortages of the above tradi-tional nonlinear parameters, we mainly introduce the results on short-term heart rate variability (HRV) signal (500 R-R intervals) and HFECG signal (1-2s). Finally, we point out it is worthwhile to put emphasis on the study of the sensitive nonlinearity parameters of short-term heart electric signal and their dynamic character and clinical effectivity.展开更多
基金This project is supported by the National Natural Science Foundation of China(No.3 9970 2 0 5)
文摘Poincare dispersed dot plot was an important method in studying heart nonlinear state and rate variability(HRV). Based on the shape of Poincare dispersed dot plot, we proposed four quantitative parameters, introduced the method and algorithm how to get them, and tested them with clinical and animal experiment data. The result showed that these four parameters have certain idiosyncrasy with different heart diseases, and the animal experiment result also showed that these parameters were changed remarkably after coronary artery ligation compared with before, which indicated these parameters might be useful for clinical diagnosis. Because the algorithm we used was based only on the shape of the graph, one can apply this algorithm on any other type of graphs like Poincare dispersed dot plot.
文摘A nonlinear autoregressive (NAR) model is built to model the heartbeat interval time series and the optimum model degree is proposed to be taken to evaluate the nonlinearity degree of heart rate variability (HRV). A group of healthy persons are studied and the results indicate that this method can effectively get nonlinear information from short (6—7 min) heartbeat series and consequently reflect the degree of heart rate variability, which supplies convenience in clinical application. Finally, a comparison with the traditional time domain method shows that the NAR model method can reflect the complexity of the whole signal and lessen the influence of noise and instability, in the signal.
文摘Nonlinear science research is a hot point in the world. It has deepened our cognition of determinism and randomicity, simplicity and com-plexity, noise and order and it will profoundly influ-ence the progress of the study of natural science, including life science. Life is the most complex nonlinear system and heart is the core of lifecycle system. In the late more than 20 years, nonlinear research on heart electric activities has made much headway. The commonly used parameters are based on chaos and fractal theory, such as correlation dimension, Lyapunov ex-ponent, Kolmogorov entropy and multifractal singu-larity spectrum. This paper summarizes the commonly used methods in the nonlinear study of heart electric signal. Then, considering the shortages of the above tradi-tional nonlinear parameters, we mainly introduce the results on short-term heart rate variability (HRV) signal (500 R-R intervals) and HFECG signal (1-2s). Finally, we point out it is worthwhile to put emphasis on the study of the sensitive nonlinearity parameters of short-term heart electric signal and their dynamic character and clinical effectivity.
