Automated Cardiovascular System Identification

Cardiovascular system identification (CSI) is an inverse modeling approach to studying the physiological mechanisms underlying fluctuations in cardiovascular signals. Because the results of CSI analysis are able to characterize these mechanisms, it may potentially be used a intelligent monitoring tool for observing alterations in cardiovascular state. In this thesis, an automated system, which addresses certain deficiencies in the current implementation of the CSI method, will be described in detail. One shortcoming, which the automated system will address, is the implementation of real-time signal acquisition, and thus, online analysis of cardiovascular signals. Additionally, it will incorporate an intuitive interface, which may allow the user to visualize the cardiovascular signals and the CSI results that they generate. Finally, the system will minimize the need for expert knowledge of the signal processing and conditioning stages, facilitating the use of the CSI method. After the presentation of the design, the time-critical and memory-intensive properties of this system will be discussed. This analysis will lead to a conclusion about the feasibility of automating the CSI process and future implementations. Thesis Supervisor: Richard J. Cohen Title: Whitaker Professor of Biomedical Engineering