报告题目：Dynamical Complexity: A Quantitative Index of Health and Disease
报告人：Chung-Kang Peng, Ph.D., National Central University and Harvard Medical School
时间：2013年3月11日，10:30am-11:30am
地点：数学楼210室
报告概要：
In recent years, technologies enable us to collect overwhelming amount of signals of our patients. As a result, it becomes possible to quantify health and diseases of human body from an integrative system viewpoint. However, conventional biomedical research tools that have been developed with reductionist theory may not be appropriate. Mainly because these tools typically focus on individual components of the whole system, while ignoring important nonlinear interactions among different components of the system.
Research in physics and applied mathematics on complex systems with multiple nonlinear interacting components yield remarkable insights into these systems. In particular, it is shown that the dynamics of how a system changes in time contains significant information about the system itself. In this talk, I will introduce a general framework to study physiologic fluctuations. With this framework, we can derive useful measures that best reflect the emergent properties of the integrative systems, and to identify system-level behaviors that are critical to our understanding of a healthy system and its pathological perturbations. This new approach has a wide range of biomedical applications that will be discussed briefly in this talk.
Chung-Kang Peng, Ph.D., is the Co-Director of the Rey Institute for Nonlinear Dynamics in Medicine at the
Dr. Peng has many close international collaborations, especially, with the scientific communities in the greater China area. He is the Co-Director of the International Research-Intensive Center of Excellence in Taiwan: Center for Dynamical Biomarkers and Translational Medicine(established by the
Dr. Peng has expertise in statistical physics and its application to the study of physiological measures. He has been working at the interface of statistical physics and biology since he was a graduate student. Over the years, he and his collaborators have developed several useful computational techniques, including the following:
- The detrended fluctuation analysis (DFA) (see introduction by Wikipedia), that originated from statistical physics to measure fractal properties in physiologic signals.
- The multiscale entropy (MSE) analysis approach to measure the complexity of complex signals (Phys. Rev. Lett.;
- A new algorithm, based on information theory and statistical physics, for linguistic analysis of symbolic sequences. It has been applied to bio-medical signals (Phys. Rev. Lett.), human languages, and DNA sequences. 90:108103, 2003
- A measurement of time reversal asymmetry in heart rate time series (Phys. Rev. Lett.). 95:198102, 2005
- An ECG-based cardiopulmonary coupling analysis for the study of sleep (Sleep). 28:1151-1161, 2005
- An index for dynamic cerebral autoregulation (Biomed Eng Online;
These new approaches have been highlighted in Nature News and Views (Nature 419: 263, 2002), the
欢迎有兴趣的老师和同学前来参加！