Research Resource for Complex Physiologic Signals
From BTRR
Overview
The objective of this resource (Beth Israel Deaconess Medical Center/Harvard Medical School and Massachusetts Institute of Technology, Division of Health Sciences and Technology) is to accelerate current research progress and catalyze new investigations in the quantitative study of complex physiologic signals. The resource has three interdependent components: PhysioBank, PhysioToolkit, and PhysioNet. PhysioBank is a large and growing archive of well-characterized digital recordings of physiologic signals and related data for use by the biomedical research community. PhysioBank currently includes databases of multiparameter cardiopulmonary, neural, and other signals from healthy subjects and from patients with a variety of conditions with major public health implications, including life-threatening arrhythmias, sleep apnea, neurologic disorders, and aging. PhysioToolkit is a library of open-source software for physiologic signal processing, analysis, and detection of physiologically significant events with the use of both classic techniques and novel methods based on statistical physics and nonlinear dynamics. PhysioNet is an online forum for the dissemination and exchange of recorded biomedical signals and open-source software for analyzing them.
Current Research
The resource is developing new algorithms that quantify the transient and local properties of nonstationary physiologic signals and the cross-interactions among multiparameter signals. These techniques will be used to detect changes that may precede the onset of catastrophic physiologic events, including epilepsy and sudden cardiac death. Complementary studies are aimed at developing techniques to quantify the nonlinear dynamics of physiologic control, with an emphasis on modeling these mechanisms and identifying new measures that have diagnostic or prognostic utility in life-threatening pathologies, such as sleep apnea and congestive heart failure. A related core area of research is the development of methods for measuring the nonlinear complexity of physiologic signals and the loss of complexity with aging and disease.
