This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Complex physiologic signals may carry unique dynamical signatures that are related to their underlying mechanisms. We present a method, based on rank order statistics of symbolic sequences used in the study of nature language, to investigate the profile of different type of physiologic dynamics. We applied this method to heart rate dynamics, an output of an important physiologic control system. The method can discriminate patterns generated from physiologic, pathologic states, as well as from aging. Furthermore, we observed increased randomness in physiologic aging and pathologic states and also uncovered nonrandom patterns in ventricular response to atrial fibrillation. This new technique may have implications of heart rate control in health and disease, and bedside diagnosis.
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