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Center for Nonlinear Studies |
One of the great challenges of contemporary biomedical science is to understand more fully the dynamics of living systems in health and disease. The importance of this challenge is highlighted by headlines announcing unexpected, life-threatening side effects of once-promising drugs, as well as the serendipitous discoveries deriving from .outside the box. approaches to major public health problems, for example, in heart disease. The basis of such unexpected findings, both negative and positive, is the extraordinary complexity of physiologic systems, which exceeds that of the most challenging systems in the physical world. These systems defy understanding based on traditional mechanistic models and conventional biostatistical analyses. Central to this enterprise are quantitative measurements that best reflect the emergent properties of the integrative system. To identify system-level behaviors that are critical to our understanding of a healthy system and its pathological perturbations, the following hypotheses are necessary: i) The complexity of a biological system reflects its ability to adapt and function in an ever-changing environment. ii) Biological systems need to operate across multiple spatial and temporal scales, and hence their complexity is also multi-scale. iii) A wide class of disease states, as well as aging, appear to degrade biological complexity and reduce the adaptive capacity of the system. Thus, loss of complexity may be a generic feature of pathologic dynamics. Recently, we developed a multiscale information approach to address this challenge. Traditional complexity measurements are often based on the concept of entropy, which quantifies the regularity (orderliness) of fluctuating signals. Entropy increases with the degree of disorder and is maximal for completely random systems. However, an increase in entropy may not always be associated with an increase in dynamical complexity. For instance, a randomized (scrambled) time series has higher entropy than the original time series, although the process of scrambling the data destroys correlations and degrades the meaningful information content of the original signal. We recently showed that this inconsistency is due to the fact that widely used entropy measures are based on single-scale analyses. Instead, biological systems operate on a wide range of temporal and spatial scales. This multiscale, hierarchical feature is critical to biological systems and needs to be taken into account in the analysis of complex systems. I will use heart rate time series and other physiologic signals as examples to illustrate the basic concept underlying the multiscale information approach. Furthermore, will demonstrate that this quantitative measurement is consistent with our hypotheses about complexity of biological systems. BIOGRAPHY Chung-Kang Peng, Ph.D., is the Co-Director of the Rey Institute for Nonlinear Dynamics in Medicine at the Beth Israel Deaconess Medical Center and Assistant Professor of Medicine at the Harvard Medical School. He is also the Associate Director of the Research Resource for Complex Physiologic Signals funded by the National Center for Research Resources of the National Institutes of Health. Dr. Peng is also a Visiting Scholar at the Boston University Physics Department and a Research Affiliate of the Harvard-MIT Division of Health Sciences and Technology. 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 developed several useful computational techniques, including the detrended fluctuation analysis (DFA), that originated from statistical physics to measure properties in physiologic signals. These new approaches have a wide range of applications in physics, biology, and economics. His work has been cited more than 3,600 times in the past 10 years. Recently, Dr. Peng and colleagues also developed the multiscale entropy (MSE) analysis approach to measure the complexity of complex signals (Phys. Rev. Lett. 89:068102, 2002), a new linguistic type of analysis for bio-medical signals (Phys. Rev. Lett. 90:108103, 2003), and a measurement of time reversal asymmetry in heart rate time series (Phys. Rev. Lett. 95:198102, 2005). These new approaches have been highlighted in Nature News and Views (Nature 419: 263, 2002), the American Institute of Physics News Update (Aug. 1, 2002), and the Boston Globe (Aug. 5, 2003), and the Harvard Focus (Dec. 2, 2005).
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